Evaluating the Impact of Database and Data Warehouse Technologies on Organizational Performance: A Systematic Review

1. Introduction

The digital revolution has fundamentally transformed the business landscape, with data emerging as a critical asset for organizations of all sizes. SMEs need database and data warehouse technologies to enhance their organizational performance. The more a business grows, the more their data gets complex, and the more need to have an effective system to organize the information, this will help with informed and effective decision making. Databases enable SMEs to store and retrieve data, ensuring that essential business data is accessible and usable in real time. Data warehouse systems improve these steps by integrating data from various sources, allowing businesses to conduct comprehensive analysis across departments. [1,2]. SMEs in developing face several challenges when adopting and implementing database and data warehouse technologies. The primary obstacle is financial constraints. These enterprises often operate on a limited capital, making it difficult to invest in hardware and software required for effective data management. Skilled IT personnel in many developing countries are few, further complicating the adoption of these technologies. Outsourcing IT personnel is not an option as well which further strains their limited budget operation. [3,4,5,6,7]. This skills gap makes it harder for SMEs to take full advantage of the potential benefits offered by these technologies.

By automating data collection, storage, and retrieval, database and data warehouse technologies can significantly reduce operational costs and improve efficiency, addressing the financial constraints many faces. Database and data warehouse systems are increasingly designed with user-friendly interfaces, reducing the need for extensive technical knowledge and ultimately the need for expertise. [8,9,10]. This allows SMEs to manage their data more effectively even with limited IT resources. Some systems offer automated maintenance and support, further alleviating the burden of managing complex IT infrastructure. Databases allow real-time access to data, and data warehouses support cross department adaptation. [11,12]. This supports strategic planning, enabling businesses to identify trends, optimize processes, and improve customer engagement. In turn, these insights lead to better resource allocation, improved productivity, and the ability to make data-driven decisions that foster growth and competitiveness, despite the challenges of smaller workforce and limited financial resources.

1.1. Evolution of Database Technologies

The comprehensive evolution of Database technologies is illustrated in Figure 1. Database evolution began with the first wave (1960-1999), which included early database systems such as network, hierarchical, and inverted list databases, all designed to handle vast amounts of data. By the 1990s, the introduction of Object-Oriented Database Management Systems (OODBMS) enabled data modeling as objects, aligning with object-oriented programming and improving the processing of complex data types like multimedia.

This time established the foundation for current databases. Following this, the relational wave (1990-2008) saw a shift with the introduction of Relational Database Management Systems (RDBMS), which are based on E.F. Codd's relational paradigm. Despite initial problems, the usage of Structured Query Language (SQL) and developments such as Patricia Selinger's cost-based query optimization made relational databases very scalable and dependable, making them the backbone for most corporate applications in data modelling and performance. [13,14,15]

Simultaneously, the decision support wave (1990-present) began with the introduction of Online Analytical Processing (OLAP) and data warehouses designed for complicated data analysis and corporate intelligence. OLAP systems, with their multidimensional data modeling capabilities, enabled firms to do in-depth historical data analysis. The emergence of graph databases during the graph wave (1999-present) transformed how data relationships were managed, with property graph databases such as Neo4j excelling at social networks and fraud detection. Finally, the NoSQL wave (2008-present) emerged to meet the demand for scalable, flexible databases capable of managing unstructured and semi-structured data produced by big data applications. NoSQL databases, which provide schema flexibility and performance, have become vital for real-time analytics, IoT, and large-scale online platforms, representing a shift from the inflexible structure of relational databases. [16,17,18]

1.2. Evolution of Data Warehouse Technologies

In Figure 2, evolution of data warehouse technologies is illustrated. The evolution of data warehousing has gone through several stages, driven by the exponential growth of data and advancements in technology. Initially, traditional data warehouses were built on Relational Database Management Systems (RDBMS), like Oracle and SQL Server, and utilized fact and dimension tables to organize data.

These early systems could handle modest amounts of data but struggled as the volume and complexity of customer and sensor data grew in the early 2000s. To address the challenges of performance degradation and data processing limitations, appliance-based solutions like Teradata and Netezza, which utilized Massively Parallel Processing (MPP), emerged. These solutions offered faster data processing capabilities but were costly and lacked scalability. With the rise of big data, Hadoop and its ecosystem (HDFS, Hive) became prominent in 2005, introducing open-source solutions for massive data scaling. However, Hadoop’s complexity and steep learning curve limited its adoption by businesses with traditional IT infrastructures. In 2012, cloud computing brought the next significant advancement, with services like Amazon Redshift, Google Bigquery, and Microsoft Azure Data Warehouse enabling businesses to leverage cloud scalability. While these solutions offered greater flexibility, they were still rooted in older technologies and had limitations in elasticity. The most recent phase in data warehousing evolution is represented by fully cloud-native platforms like Snowflake (introduced in 2016), which were designed specifically for the cloud. Snowflake's architecture separates compute and storage, allowing companies to scale resources independently and pay only for what they use. Additionally, it offers unique features such as cloning data for testing without duplicating physical storage, and its metadata-driven architecture supports real-time elasticity. This evolution has drastically lowered the Total Cost of Ownership (TCO), making it easier for businesses of all sizes to access, analyze, and derive value from their data while minimizing maintenance and infrastructure costs. [19,20,21]

The proposed systematic review facilitates the consolidation of existing research on the impact of database and data warehouse technologies on organizational performance. By focusing on various types of databases and data warehouses, the review seeks to provide a comprehensive understanding of how these technologies can be leveraged to enhance organizational efficiency. It will assess different technologies, their cost implications, effects on decision-making, customer relationship management, and operational efficiency. By identifying gaps in the current literature and highlighting areas that require further investigation as shown in Table 1 this review is expected to drive innovation and development in the field. Ultimately, the systematic review will serve as a foundational document, offering researchers a valuable resource to build upon for advancing the understanding and application of database and data warehouse technologies in improving organizational performance.

1.3. Research Questions

In this research, we evaluate the impact of Database and Data Warehouse Technologies on organizational performance of small and medium enterprises (SME)s. This study inquires intensively on how Database and Data Warehouse Technologies can improve SMEs’ efficiency and performance, highlighting both the advantages and challenges faced in its employment. The following research questions aim to clarify these aspects to comprehend the effective use of Database and Data Warehouse Technologies in SMEs’ organizational performance.

• How does the utilization of Database and Data Warehouse technologies impact SMEs’ performance?
• What are the challenges SMEs face when adopting Database and Data Warehouse technologies?
• What specific aspects of organizational performance are looked at in relation to Database and Data Warehouse technologies?
• What are the crucial factors influence successful adoption of Database and Data Warehouse technologies?
• What are the consequences of not utilizing Database and Data Warehouse technologies?

1.4. Rationale

The fast evolution of data digitization has presented an opportunity for businesses to stay competitive, relevant, and in touch with their customers in real time. These volumes of data are unusable unless there are effective tools that will identify, store, and analyze this information. This is a huge obstacle for SMEs, which are an important part of the economy but lack the resources, awareness, and expertise needed to successfully integrate Database and Data Warehouse Technologies. Research highlights how the utilization of DB and DWT has the necessary infrastructure to boost decision-making processes, competitiveness, and operational efficiency. However, it identifies several challenges faced by SMEs, such as lack of awareness, no information management personnel, and lack of capital that hinders the investment and adoption of DB and DWT.

Present research concentrates on one side which is often the adoption of DB and DWT, disregarding their comprehensive influence on crucial organizational metrics such as financial stability, operational effectiveness, and creativity. By evaluating the impact of DB and DWT on organizational performance, we aim to provide an understanding of how SMEs can utilize DB and DWT to improve efficiency and performance. This paper will add to how decision makers can invest in DB and DWT and boost SME success.

1.5. Research Contribution

This work presents a comprehensive systematic review of the impact of database and data warehouse technologies on organizational performance. The study addresses various unresolved issues and research gaps in this field. The key contributions of this research are as follows:

• We provide a comprehensive analysis of the influence that database and data warehouse technologies have on organizational operations. This evaluation explores the effects of these technologies on decision-making processes, operational efficiency, and the overall performance of the organization. It offers valuable insights for companies seeking to leverage data management solutions to enhance productivity.
• We gather and analyze the existing knowledge concerning the impacts of data warehouse and database technologies. By highlighting deficiencies in the current literature, particularly in terms of their effectiveness and practical use across different organizational contexts, we pinpoint areas that require further investigation. This approach aims to stimulate advancements in the field and direct future research initiatives.
• We present a range of advanced analytical models to assess the impact of database and data warehouse technologies on organizational performance. These models aim to establish a foundation for future empirical research in this area and to provide a deeper insight into the diverse factors that influence organizational success.

1.6. Research Novelty

This paper presents a unique perspective to the field, according to our thorough investigation, there is currently little to no study that delves on the influence of both DB and DWT on SME performance with specific focus on factors enabling or barricading its adoption and possible long-term impacts. Our systematic review tackles gaps by identifying the key contributing factors and drawbacks to the implication of DB and DWT in SMEs. We explore the often-overlooked areas such as lack of expertise, lack of capital, and the technological readiness of SMEs, which hinder their ability to harness DB and DWT and their full potential. Through this work, we provide specific insights and practical recommendations designed for SMEs, propelling them to integrate Database and Data Warehouse Technologies to improve operational performance and efficiency.

1.7. Research Organization

The paper is structured to provide a clear and systematic examination of the impact of Database and Data Warehouse Technologies on the performance of SMEs. Following this introductory section, Section 2 outlines the materials and methods employed in the research, detailing the systematic review process, the criteria for study inclusion, and the data collection methods used. This section also explains the search strategy applied to gather relevant literature, ensuring a comprehensive review of the subject. In Section 3, the results of the review are presented, synthesizing the key findings from the studies analyzed. This section focuses on the core factors influencing the adoption of Database and Data Warehouse Technologies by SMEs, highlighting both the enablers and barriers to implementation, and providing insights into the impact of these technologies on operational performance. The Discussion follows in Section 4, where the implications of the findings are explored. This section interprets the results in the context of SME performance, drawing comparisons with existing literature and discussing the practical challenges and opportunities that SMEs face in adopting these technologies. Additionally, it offers an analysis of how the findings contribute to the broader understanding of technology adoption in small and medium enterprises.

2. Materials and Methods

This section outlines the procedure followed to carry out the review of the evaluation of the impact of Database and Data Warehouse Technologies on Organizational Performance of SMEs. The review investigates studies within the past decade (2014 – 2024). As shown in Figure 3, the methodology highlights guidelines for study selection, data collection process and origin, and the method used to evaluate the collected literature laying the foundation for a detailed investigation of each aspect in the following sections.

2.1. Eligibility Criteria

A systematic study of all peer-reviewed and published research works appropriate to the effects of database and data warehouse technologies on organizational performance was the focus of a thorough systematic evaluation that was carried out. The review covered studies that were written up in English and released between 2014 and 2024. To ensure that pertinent studies were included and to weed out those that did not directly address the evaluation of the impact of database and data warehouse technologies on organizational performance, a definitive set of inclusion and exclusion criteria was developed. As such, the review considered only peer-reviewed research that were exclusively focused on this subject. Table 2 contains specifics about the study's inclusion and exclusion criteria.

The assessment of studies for inclusion in the systematic review followed a structured, multi-stage process. In the first stage, titles and abstracts were screened by two independent reviewers to determine if they met the inclusion criteria. Any discrepancies in judgment were discussed between the reviewers to ensure consistency. In the second stage, full-text versions of the remaining studies were thoroughly reviewed to assess their adherence to the inclusion criteria, particularly focusing on the presence of a clear research framework or methodology related to the impact of database and data warehouse technologies on organizational performance. Studies that lacked sufficient methodological detail or relevance were excluded at this stage. Finally, in the third stage, any borderline cases were brought to a consensus discussion involving all reviewers. If disagreements remained unresolved, a third reviewer was consulted, and decisions were made based on a majority vote. This rigorous assessment process ensured that only studies of high relevance and methodological rigor were included in the review.

2.2. Data collection Process

The searches were conducted across multiple academic databases to ensure a broad and representative collection of studies. These databases included Scopus, Web of Science, and Google Scholar, which were chosen for their robust coverage of scientific, technical, and business literature. The choice of these databases was based on their relevance to the fields of technology, business, and organizational studies, ensuring access to high-quality and peer-reviewed research. The selected synonyms and keyword combinations were applied universally across all databases. However, slight adjustments were made in cases where the database's search engine required specific formatting or syntax. For instance, in SCOPUS, the keyword “NoSQL databases” was expanded with the phrase “OR non-relational databases” to accommodate specific database architecture discussions.

The search keywords used to conduct this systematic review were formulated to pinpoint studies that presented attributes of Database and Data Warehouse Technologies on Organizational Performance. To select index key-terms, experimental searches in a reiterative method were carried out. Keywords that did not yield research works aligned to the inclusion criteria were excluded. To ensure the inclusion of analogous terms for the fundamental keywords, synonymous terms were established for each key concept. For database technologies, synonyms included: "database technologies," "database systems," "database management systems," "DBMS," "relational databases," "NoSQL databases," and "SQL databases. Keywords related to data warehouse technologies included: "data warehousing," "data warehouse systems," "data marts," "enterprise data warehouses," "DW," and "OLAP systems." The term organizational performance was described using synonyms such as: "business performance," "company performance," "enterprise performance," "operational efficiency," "organizational efficiency," "organizational effectiveness," "business success," and "firm performance." During the search procedure, logic operators "AND" and "OR" were utilized to refine the search for relevant scholarly papers. The "AND" operator ensured that all chosen keywords were included, while the "OR" operator captured papers containing any of the selected keywords. Additionally, the wildcard asterisk (*) was employed to capture plurals and other suffix variations. This methodical search strategy ensured comprehensive coverage of the relevant literature for evaluating the impact of data-base and data warehouse technologies on organizational performance. The bibliometric analysis of study search Keywords i.e., the network visualization, overlay visualization and density visualization is illustrated in Figure 4.

Table 3 shows sample keyword combinations and their respective results in the different databases.

Table 3 provides transparency in the search process, illustrating how the various synonyms and keyword combinations were applied and the number of relevant studies retrieved from each search query.

2.3. Conceptualization of Data Warehouse

The concept of data warehousing emerged in the early 1990s as organizations began accumulating large volumes of data from various sources. A data warehouse is a centralized repository designed to store historical and current data from multiple disparate systems, enabling organizations to perform advanced analytics and derive insights for strategic decision-making. Data warehouses are structured to allow efficient querying and reporting, often integrating with business intelligence (BI) tools. Over time, data warehousing technology has evolved to accommodate the growing need for scalability, flexibility, and real-time processing capabilities, particularly as businesses have moved towards more dynamic data environments.

2.3.1. Types of Data Warehouses.

Data warehouses can be classified into several types, each catering to specific organizational needs as follows.

a) Enterprise Data Warehouse (EDW)

As shown in Figure 4, an EDW is a comprehensive, centralized data warehouse designed to store and manage data from across an entire organization. It provides a unified view of the organization’s data, facilitating enterprise-wide analytics and reporting. EDWs are typically used in large organizations with complex data needs, and they are optimized for complex queries, batch processing, and reporting.

b) Operational Data Store (ODS)

Figure 4. Enterprise Data Warehouse (EDW), [Available at: LINK].

Figure 4. Enterprise Data Warehouse (EDW), [Available at: LINK].

as shown in Figure 5, an ODS is a type of data repository that focuses on storing real-time or near-real-time operational data. Unlike EDWs, which typically deals with historical data, ODS is designed to support daily operational activities and decision-making by providing up-to-date information. An ODS is often a precursor to a full-fledged data warehouse, serving as a temporary storage area for data before it is loaded into the data warehouse.

c) Data Mart

A data mart is a subset of a data warehouse, designed to focus on specific business areas or departments such as finance, marketing, or sales. As shown in Figure 6, data marts are smaller and more specialized than EDWs, allowing faster access to relevant information for specific decision-makers. They can either be dependent (created from an EDW) or independent (created from external sources).

d) Data Lake

A modern approach to data storage, data lakes are often compared to data warehouses but differ in that they store both structured and unstructured data in raw form. While a data warehouse typically imposes a schema on data at the time of storage, a data lake applies a schema on read. Data lakes, as shown in Figure 7, offer more flexibility in terms of storage and processing but require more advanced data management tools to maintain.

2.3.3. Modern Data Warehouse Architecture

The architecture of data warehouses has evolved significantly in recent years, driven by advances in cloud computing, distributed storage, and big data technologies. Modern data warehouses are designed to handle larger volumes of data, offer greater flexibility in querying, and support advanced analytics such as machine learning. With the growth of cloud computing, many organizations are moving their data warehouses to the cloud. Cloud-based data warehouses, such as Amazon Redshift, Google BigQuery, and Snowflake, provide scalability, flexibility, and cost-efficiency. These platforms can scale up or down based on demand and provide advanced querying capabilities, enabling real-time insights and analytics. Modern data warehouses often use distributed architecture, which spreads data across multiple nodes or servers to improve performance and reliability. This architecture allows data warehouses to handle massive amounts of data and provides fault tolerance by distributing processing tasks across different systems. A newer approach to data warehousing involves data virtualization, where data from various sources can be accessed in real-time without needing to move or replicate it. This reduces data redundancy and enables faster query responses, especially when dealing with dynamic and disparate data sources.

2.4. Big Data Frameworks

Big data frameworks have become an essential part of modern data analytics, enabling organizations to process, analyze, and derive insights from vast amounts of structured and unstructured data. These frameworks, as shown in Table 4, provide the necessary tools to handle complex data operations and integrate data from diverse sources at scale. The rise of big data frameworks has been closely tied to the growth of cloud computing, distributed systems, and the demand for real-time analytics.

2.4.1. Popular Big Data Frameworks

Figure 8 shows how Big Data frameworks have evolved to address the complex demands of managing and processing large-scale datasets through various specialized systems. Big data frameworks are essential for managing and analyzing vast datasets, each offering unique capabilities depending on the specific needs of the organization. Several widely used frameworks have emerged to address different aspects of big data processing, from batch processing to real-time analytics. Apache Hadoop stands as one of the foundational frameworks, offering a distributed storage and processing system with its core components, the Hadoop Distributed File System (HDFS) and the MapReduce programming model. This framework excels in handling vast amounts of data across multiple machines, providing scalability and fault tolerance essential for batch processing.

Apache Spark builds upon this by introducing in-memory computation, significantly enhancing processing speed for iterative algorithms. It supports a broad range of workloads, including batch processing, real-time streaming, and machine learning, making it a versatile choice for big data analytics. Apache Flink extends this versatility into real-time data streaming, with a focus on delivering low-latency, real-time analytics, which is crucial for applications such as fraud detection and Internet of Things (IoT) data analysis. Complementing these frameworks is Apache Kafka, a distributed event streaming platform designed to handle high-throughput, low-latency data streams, and facilitate real-time data processing across various systems. For SQL-based querying, Hive provides a SQL-like interface on top of Hadoop, making it easier to work with large datasets using familiar syntax, while Presto offers a distributed SQL query engine that enables fast, interactive querying of big data. Together, these frameworks represent a spectrum of solutions tailored to different aspects of big data processing, from storage and batch processing to real-time analytics and querying, addressing the diverse needs of modern data management.

2.2. Taxonomy of Data

The taxonomy of big data refers to the classification of big data into various categories based on its characteristics and how it is managed. Understanding this taxonomy is crucial for developing effective big data strategies and choosing the right tools for data processing and analysis.

2.5.1. The 4 Vs of Big Data

Figure 9 offers a graphical representation of the 4 Vs of Big Data that provide a comprehensive framework for understanding the key challenges and opportunities associated with big data. Volume refers to the massive amounts of data generated daily, as organizations are inundated with data from multiple sources, including transactional data, social media interactions, IoT devices, and more. Managing the scale and storage of this data is a significant challenge for modern enterprises. The velocity of data generation and processing is critical.

With real-time data streams flowing from sources like sensors, financial markets, and social media, organizations need systems that can handle this fast-paced data influx and generate real-time insights to stay competitive. Another key element is variety, which refers to the diverse formats of big data. Data can be structured, such as traditional relational databases, semi-structured, like XML and JSON files, or unstructured, such as multimedia content, including images, videos, and text. Managing and integrating these different data formats within a single framework is a complex task but necessary for comprehensive data analysis. Along with the variety comes veracity, which deals with the accuracy and quality of the data. Big data is often incomplete, inconsistent, or incorrect, requiring organizations to adopt data cleansing and validation processes to ensure the data's reliability and utility. The 4 Vs framework encapsulates the multifaceted challenges of handling big data while emphasizing the potential value that can be unlocked with the right tools and strategies.

2.5.2. Big Data Management Models.

Traditionally, big data processing has relied on batch processing, where data is collected over a set period and then processed all at once. This method is particularly efficient for handling large datasets, as it allows organizations to process vast amounts of information in a single operation. However, the drawback of batch processing is that it does not provide real-time insights, which can be a limitation for industries or applications that need immediate data responses. In contrast, stream processing has emerged as a solution for real-time data analysis, enabling organizations to process data continuously as it is generated. This approach is especially critical for applications requiring instantaneous decision-making, such as in financial markets, where milliseconds can impact transactions, or in Internet of Things (IoT) systems that rely on immediate feedback to adjust operations. Modern big data architectures often adopt hybrid models, combining both batch and stream processing to leverage the strengths of each approach. This allows businesses to gain real-time insights from streaming data while also efficiently processing historical data in batches, ensuring both immediate responsiveness and comprehensive data analysis.

2.2. Data Items

In this paper, the focus is on gathering information that intensively investigates the effects of Database and Data Warehouse Technologies on small and medium-sized enterprises (SMEs). The outcomes are grouped in accordance with the research questions to avoid straying from topic. This systematic review evaluates the impact of database and data warehouse technologies on organizational performance by examining a variety of outcome dimensions, including operational, financial, innovation, collaboration, employee, and customer outcomes. A comprehensive search strategy is employed to identify relevant studies, including both qualitative and quantitative research. Data extraction is performed independently by two reviewers, with quality assessed using the Newcastle-Ottawa Scale and GRADE framework. A narrative synthesis and, where applicable, meta-analysis are conducted to summarize findings. Subgroup and sensitivity analyses are performed to explore variations across factors such as industry and company size. The review follows the PRISMA framework for transparent reporting.

2.6.1. Variables

This section outlines the general characteristics of the studies and contributors covered in the evaluation. Table 5 describes essential elements such as the publication year, the databases used, and the types of studies included in the analysis. The goal is to thoroughly define and evaluate key variables, including research design, techniques, and other relevant study aspects. The studies examined provide insight into how database and data warehouse technologies influence organizational outcomes and performance metrics. In studies like “The Impact of Cloud-Based Database Systems on SME Performance: An Exploratory Case Study” (2019) and “The Role of Data Warehousing in Enhancing Business Decision-Making in Developing Economies” (2023) used a combination of qualitative and quantitative methods, such as case studies and surveys, to explore the relationship between data warehousing technologies and business outcomes like operational efficiency and revenue growth. Another study, “Adoption of Hybrid Cloud Data Warehousing for Competitive Advantage in Startups” (2022), employed mixed-method approaches, integrating statistical analysis and thematic analysis, to evaluate the benefits of cloud-based and on-premises technologies in small businesses. Some research focused on specific technological implementations, such as the study “ETL Tools and their Impact on Data Quality and Performance Metrics in Large Enterprises” (2021), which employed experimental designs to test the efficacy of various data processing tools. This study used a combination of interviews and document analysis to evaluate outcomes related to data accuracy, query performance, and scalability.

The characteristics outlined in table 5 provide a comprehensive overview of the studies included in the review, detailing the factors that influence the effectiveness of database and data warehouse technologies in driving performance outcomes. This structured representation allows for a focused analysis of key variables across various studies.

2.2. Study Risk of Bias Assessment

The quality appraisal aimed to enhance the veracity of the selected research papers and establish the suitability and comprehensiveness of their findings. A total of 150 studies included in this review were evaluated for quality using a scoring technique based on the Newcastle-Ottawa Scale (NOS), which assesses studies across three dimensions: Selection, Comparability, and Outcome/Exposure. To ensure transparency in the assessment process, a flowchart process was developed to visually illustrate the steps taken during the bias assessment. Figure 10 outlines how studies were selected, evaluated, and scored, providing an easily understandable overview. The combined approach of multiple independent reviewers, a structured scoring system, and visual representation helped ensure that the risk of bias assessment was comprehensive and objective, thus supporting the reliability and validity of the conclusions drawn from the reviewed studies.

This systematic approach provided a consistent measure of the studies' reliability and robustness. The NOS allows for a maximum of nine stars across these three dimensions, with the sum of stars determining the overall quality rating of each study as High (7-9 stars), Moderate (4-6 stars), or Low Quality (0-3 stars). By applying the NOS, the assessment was designed to ensure objectivity and minimize bias, thus reinforcing the reliability of the findings. Two independent reviewers conducted assessments for all studies, facilitating inter-rater reliability. In cases of discrepancies, a third impartial reviewer mediated to resolve differences, ensuring that subjective judgments were collectively discussed and agreed upon. This collaborative approach further enhanced the credibility of the bias assessment.

The Selection dimension of the Newcastle-Ottawa Scale, contributing up to four stars, evaluates how well studies select participants, focusing on whether cases and controls in case-control studies are drawn from the same population to minimize selection bias. In cohort studies, it examines the accurate representation of exposed and unexposed groups. The Comparability dimension, which can contribute up to two stars, assesses how effectively studies control for confounding factors, with higher scores awarded to those using matching or statistical adjustments; failure to account for variables like age and pre-existing conditions may lead to misleading conclusions, exemplifying potential reporting bias. Finally, the Outcome/Exposure dimension, worth a maximum of three stars, focuses on the accuracy and reliability of outcome measurements or exposure assessments. A higher score indicates precision in tracking key variables; for instance, a study that reports positive treatment outcomes without disclosing adverse effects may skew understanding and mislead stakeholders, illustrating the impact of reporting bias.

2.8. Effect Measures

In this systematic review, various effect measures were utilized as indicated in figure 11 to accurately interpret the impact of database and data warehouse technologies on organizational performance. The choice of effect measures was driven by the nature of the outcomes being investigated, ensuring that the results could be meaningfully compared and applied in real-world contexts. For evaluating Increased Profitability, Operational Efficiency, Customer Satisfaction, and Market Share, primary effect measures included mean differences, correlation significance, and regression coefficients. When examining factors influencing data implementation, correlation coefficients and statistical significance in regression models were crucial indicators. For assessing aspects such as employee productivity, data-driven practices, and process optimization, measures like differences and percentage changes were utilized. Metrics such as risk ratios and compliance rates were used to evaluate data usability, accuracy, risks, communication effectiveness, and adherence to compliance. Challenges in Big Data adoption, such as costs and personnel needs, were assessed through event frequencies and percentage impacts. Quantitative research often involves Structural Equation Modeling (SEM) or Partial Least Squares SEM, analyzing factors like SEM results, mean differences, and regression coefficients. Qualitative research, including case studies and thematic analysis, relied on categories, observations, and case study findings. Mixed methods research combined quantitative measures with qualitative insights. Economic Performance was assessed through regression coefficients and statistical significance, while Operational and Environmental Performance were evaluated using differences, effect sizes, and coefficients from SEM. Sustainability and Competitive Advantage were measured through effect sizes, regression coefficients, and correlation measures.

For binary outcomes, such as the adoption or non-adoption of database technologies and their impact on organizational efficiency, the risk ratio (RR) and odds ratio (OR) were employed. These measures help in comparing the likelihood of outcomes between groups. For continuous outcomes, including cost reduction and customer satisfaction scores, the mean difference (MD) was used. When studies employed different instruments to measure similar continuous outcomes, the standardized mean difference (SMD) was applied to standardize the results across various scales. To interpret the size of effects, specific thresholds were utilized. For binary outcomes, a risk ratio above 1.0 indicated a positive effect, while values below 1.0 suggested no effect or a negative effect. For continuous outcomes, thresholds based on Cohen’s d scale were applied, categorizing effects as small (0.2), moderate (0.5), or large (0.8). Additionally, where a minimally important difference (MID) was defined by the studies, it was used to gauge the practical significance of the effects. In cases where the synthesized results were re-expressed, particularly for meta-analyses involving risk ratios, results were converted into absolute risk reduction (ARR) and number needed to treat (NNT), based on an assumed comparator risk. This approach provided a clearer understanding of the magnitude of the effects. The choice of effect measures, including the use of standardized mean difference and risk ratios, was guided by the nature of the data and outcomes under study. This ensured that the findings could be effectively interpreted and applied by decision-makers and practitioners in various organizational contexts.

2.9. Synthesis methods

The synthesis of studies in the review on "Evaluating the Impact of Database and Data Warehouse Technologies on Organizational Performance" involved several steps to ensure that the selected studies were appropriate for inclusion in the analysis.

• 2.9a. Deciding Study Eligibility for Each Synthesis

We weighed the characteristics of studies specifically focusing and aligning with our topic, study objectives, and concerns regarding The Effect of Database and Data Warehouse technologies on Organizational Performance. Our selection criteria prioritized papers with a clear methodology concerning the type of intervention (e.g., specific database or data warehouse technology) and the organizational performance outcomes (e.g., business performance, operational efficiency, or intelligent decision-making).

• 2.9b. Data Preparation for Presentation and Synthesis

To prepare gathered data for synthesis, various procedures were followed to ensure uniformity. The first step was to identify duplicate entries on our Excel Spreadsheet using titles, and employing the built in “Remove Duplicate” feature to eliminate rows. Missing summary data, such as standard deviations for continuous outcomes, were approximated using statistical methods. Studies that did not align with our research questions, and organizational performance outcomes were excluded from our study to ensure we stay on the same theme for consistency and accuracy.

• 2.9c. Tabulation and Visual Display of Results

To visualize our screened data for comprehension, pattern analysis, and comparison, we created tables. These tables help us to identify and distinguish categories or classifications of data.

Table 6. Pivot Table of Data Visualization and Synthesis.

Chart Type	Purpose	Data Representation Method
Forest Plot	Displays effect sizes from multiples studies and overall trends.	Numbers (odds ratio)
Pie Chart	Displays proportional distributions in a dataset.	Percentages (%)
Bar graph	Good for comparing different categories of data visualized as rectangular bars.	Numbers and/or Percentages (%)
Line Chart	Shows trends of individual or multiple categories.	Numbers and/or Percentages (%)
Scatter Plot	Displays correlation between different variables (two)	Numbers

Table 6. Pivot Table of Data Visualization and Synthesis.

Chart Type	Purpose	Data Representation Method
Forest Plot	Displays effect sizes from multiples studies and overall trends.	Numbers (odds ratio)
Pie Chart	Displays proportional distributions in a dataset.	Percentages (%)
Bar graph	Good for comparing different categories of data visualized as rectangular bars.	Numbers and/or Percentages (%)
Line Chart	Shows trends of individual or multiple categories.	Numbers and/or Percentages (%)
Scatter Plot	Displays correlation between different variables (two)	Numbers

• 2.9d. Synthesis Methodology

A procedure was followed to synthesize findings, following set standards properly arranged in an Excel Spreadsheet. This standard approach made it easier to structure, categorize and ultimately compare aspects in the same dataset, this includes titles, online databases where each study was located (SCOPUS, Google Scholar, and Web of Science). The following tables summarizes the studies found in each online database, respectively.

Table 7. Results obtained from gathered Literature Search.

No.	Online Database	Studies found
1	SCOPUS	297
2	Web of Science	643
3	Google Scholar	3581
Total		4521

Table 7. Results obtained from gathered Literature Search.

No.	Online Database	Studies found
1	SCOPUS	297
2	Web of Science	643
3	Google Scholar	3581
Total		4521

2.10. Reporting Bias Assessment

In our evaluation, we focused on the possibility of publication bias, where studies with positive results were more favorable to be published than those with less significant results. To tackle this issue, we included different studies to emersed ourselves in a wide range of studies, avoiding being one sided. Our search strategy enabled us to eliminate bias by exploring different online databases (SCOPUS, Web of Science, Google Scholar) and included grey literature (conference paper, thesis) to provide a broader focus on our initial goal.

2.11. Certainty Assessment

The reviewed literature in this systematic review on the Impact of Database and Data Warehouse Technologies on Organizational Performance was evaluated based on five quality assessment (QA) criteria to ensure accuracy, transparency, and relevance:

• QA1: The clarity and explicitness of the research aim.
• QA2: The transparency and specification of data collection methods.
• QA3: The clarity and comprehensiveness in defining database and data warehouse technologies.
• QA4: The application of a well-defined, appropriate research methodology.
• QA5: The contribution of the research findings to enhancing existing literature on organizational performance.

Each criterion was rated on a scale from zero (0) to one (1). A 'No' response was assigned '0' points, '0.5' was given if the criterion was 'Partially' met, and '1' point for a 'Yes' response. A total score between 0 and 5 points was assigned to each piece of literature under review. The certainty assessment results for the collected literature are presented in Table 5, illustrating the applications of database and data warehouse technologies in improving organizational performance.

Table 8. Study Quality Assessment Results.

Ref	QA1	QA2	QA3	QA4	QA5	Total	Grading (%)
[34]	1	0.5	0.5	0.5	1	3.5	70
[12,15,17,24,29,39,52,93,110,111,112,113,114,115,116,117,118]	1	1	0.5	1	1	4.5	90
[1,2,3,4,5,6,7,8,16,19,32,34,35,36,37,38,39,40,53,61,122,123,124,125,126,127,128,129,130,131,132,133,134,135,136,137,138,146,147,148,149,150]	1	0.5	1	1	0.5	4	80
[14, 25-28,30, 33,44, 51,54- 60, 68, 71-80,95-110,119,121,141-145]	0.5	0.5	0.5	0.5	1	3	60
[9-11, 22, 25, 35,41- 43,46- 50, 62-64, 68-70,81-93,120,122,140]	1	1	1	1	1	5	100

Table 8. Study Quality Assessment Results.

Ref	QA1	QA2	QA3	QA4	QA5	Total	Grading (%)
[34]	1	0.5	0.5	0.5	1	3.5	70
[12,15,17,24,29,39,52,93,110,111,112,113,114,115,116,117,118]	1	1	0.5	1	1	4.5	90
[1,2,3,4,5,6,7,8,16,19,32,34,35,36,37,38,39,40,53,61,122,123,124,125,126,127,128,129,130,131,132,133,134,135,136,137,138,146,147,148,149,150]	1	0.5	1	1	0.5	4	80
[14, 25-28,30, 33,44, 51,54- 60, 68, 71-80,95-110,119,121,141-145]	0.5	0.5	0.5	0.5	1	3	60
[9-11, 22, 25, 35,41- 43,46- 50, 62-64, 68-70,81-93,120,122,140]	1	1	1	1	1	5	100

For the evaluation of certainty in the body of evidence for this systematic review, the GRADE (Grading of Recommendations Assessment, Development and Evaluation) framework, specifically version 3.0, has been employed in figure 12. This framework facilitates a structured assessment of evidence quality across various domains

The certainty of evidence across key outcomes was assessed using several critical factors. First, we evaluated precision by considering the sample sizes and confidence intervals in the studies. Larger samples and narrower confidence intervals indicated higher certainty in the evidence, suggesting more reliable estimates. Additionally, we assessed consistency by comparing results across studies. High consistency, where studies demonstrated similar effects, enhanced the certainty of our findings. In cases of heterogeneity, we carefully analyzed potential sources and their influence on overall conclusions.

We also used an adapted version of the Cochrane Risk of Bias tool to evaluate the risk of bias. Studies with low risk contributed significantly to higher certainty. Furthermore, directness was assessed based on how well study populations, interventions, and outcomes aligned with our research questions. High directness impr[14,25–28,30,33,44,51,54–60,68,71–80,95–110,119,121,141–145oved our confidence in the evidence.

Figure 12. Certainty assessment procedure.

Figure 12. Certainty assessment procedure.

In assessing the certainty of evidence for this review, High Certainty was assigned to evidence from well-regarded research that produced precise and reliable findings di-rectly addressing the review questions, indicating that the evidence is very likely to re-flect the true effect. Moderate Certainty was applied when there were reasonable doubts about consistency, precision, or research design, but these concerns did not undermine confidence to a high degree. This suggests that while the evidence is likely to reflect the true effect, some potential for substantial change remains. Low Certainty was noted when there were significant concerns about potential biases, irregularities, or imprecision, which reduced the degree of confidence in the findings. Lastly, Very Low Certainty was given to results that were highly indirect, of low quality, or where there were major issues with bias, inconsistency, or imprecision, indicating a very low likelihood that the evidence reflects the true effect and substantial uncertainty. Independent reviewers conducted the certainty assessments, and disagreements were resolved through consensus discussions. We also contacted study authors when necessary to clarify details and strengthen our assessments. A summary of the results is presented in Table 5, providing an overview of the certainty of evidence across key outcomes. Standard GRADE terminology, such as "Database technologies probably improve operational efficiency," was used to clearly communicate the level of certainty.

3. Results

As depicted in figure 13, this section outlines all important factors that shape or influence the results, factors such as study selection, risk of bias, certainty evidence, which all play a pivotal role in paving the way to any conclusions that can be drawn.

Figure 13. Driving factors involved in Results.

Figure 13. Driving factors involved in Results.

3.1. Study Selection

The studies selection process of was operated as illustrated in Figure 14. The research papers were amassed from electrical engineering research paper data sources with the assistance of the keywords that were mentioned in previous “Scholarly Work Search Phrases” section. These research papers were gathered stringently in line with the conditions of the inclusion and exclusion criterion presented in the previous section. The results search yielded approximately 4,521 research papers across all considered research data sources, and their titles and abstracts were surveyed. As demonstrated by the Figure 15, the collected research papers comprised of 150 research papers in total, of which 45 from Google Scholar, 66 from Web of Science and 30 from Scopus. Out of the 218 research papers, 2 were book chapters, 38 conference papers, 2 dissertations, and 174 journal articles. All research papers that seemed to have duplicate research studies were excluded. Therefore, the remaining 150 research papers were qualified for full-text review and were incorporated in this systematic analysis process.

3.2. Eligible Studies Attributes

Hundred and fifty eligible research studies were published between 2014 and 2024, comprising of 4 book chapter, 26 conference papers, 2 dissertations,11 thesis and 107 journal articles. Table 9 illustrate the number of research papers published by year in the last decade. There has been fluctuations in growth numbers in publications since 2014 as shown in Figure 16 . Although there has been an emergence of many research studies in Database and Data Warehouse Technologies, a comprehensive systematic review Evaluating the Impact of Database and Data Warehouse Technologies on Organizational Performance have not been conducted.

Table 9 presents the distribution of study publications by year and type, covering a total of 150 publications from 2014 to 2024. Many publications are journal articles (107), followed by conference papers (26), with smaller contributions from book chapters (4), dissertations (2), and theses (11). The years with the highest publication counts are 2016 and 2019, each with 22 and 17 publications, respectively, while the year 2021 had the fewest publications (4). Overall, journal articles remain the dominant type of publication, reflecting a consistent preference for this medium in academic research.

Figure 17. The share of research publication by country.

Figure 17. The share of research publication by country.

The amount in count of countries actively participates to publishing research papers on Database and Data Warehouse Technologies were also considered. According to the context where the research study was carried, the published studies were categorized as represented in Figure 17. An appreciable amount of the collected research papers has been 358 contributed by the researchers from India (papers, 10.67%), United kingdom (14 papers, 9.33%), Japan (7 papers, 4.67%), and South Africa (4 papers, 4%). On the lower end of the spectrum, countries like Bangladesh, Haiti, Nepal, and Taiwan contribute 0.67% each, possibly reflecting limited resources or research output in this specific domain. This reflects a concentration of research in economically advanced countries, which are likely to have more substantial technological infrastructure supporting database and data warehouse systems. There is also significant geographical diversity in the dataset, indicating global interest in the subject matter, though the extent of contributions varies greatly.Table 10 provides an overview of various studies on database and data warehouse technologies in relation to organizational performance. These studies focus on understanding how these technologies are adopted and implemented across different industries, their role in enhancing data-driven decision-making and operational efficiency, and the impact of employee training on technology integration and business outcomes. Common methodologies used in these studies include literature reviews, case studies, surveys, and quantitative analyses, such as regression models and Structural Equation Modeling (SEM). Key outcomes indicate that database and data warehouse technologies significantly improve business performance, including operational efficiency, data accuracy, and strategic decision-making capabilities. However, challenges such as high costs, system complexity, and a lack of skilled personnel are commonly identified. Recommendations from these studies frequently emphasize the need for simplified systems, enhanced training programs, and better integration of these technologies into existing business processes. These insights aim to guide organizations in leveraging database and data warehouse technologies effectively to improve overall performance and competitiveness.

3.3. Risk of Bias in Studies

Table 11 evaluates several studies on databases and data warehousing using the Newcastle-Ottawa Scale, assessing three key criteria: Selection, Comparability, and Out-come/Exposure. Each study reviews a total start count rating, which classifies them as either, Low (1-4 stars), Moderate (5-7 stars) or High quality (8-9 stars). Most studies scored well in Selection and outcome/exposure, with some variability in Comparability, show-casing different levels of methodological approach across the studies.

When assessing the impact of Big Data on the performance of small and medium-sized enterprises (SMEs), it is essential to understand the methodologies employed in the research studies, as these directly influence the credibility and applicability of the findings.

Figure 18 highlights that surveys are the predominant research method, comprising 44.30% of the studies. Surveys are efficient in collecting large volumes of data and can provide valuable insights into trends and perceptions within SMEs. However, they are prone to biases stemming from self-reporting and may struggle to capture complex interactions within organizational contexts. Case studies account for 33.56% of the research. They provide detailed insights and context-specific understanding, which can be beneficial in exploring the nuances of Big Data's influence on SME performance. However, case studies often lack generalizability, making it challenging to apply findings broadly across different contexts. Experimental designs represent 20.13% of the studies. These methods allow for establishing causal relationships and testing hypotheses under controlled conditions. However, the limited external validity can hinder the applicability of findings to real-world scenarios, as the results may not fully reflect the complexities of actual business environments. figure-experimental designs, making up a mere 2.01%, offer another avenue for investigating causal relationships but are similarly constrained by challenges in ensuring rigorous control over variables, which may lead to potential biases.

The data indicates a notable underutilization of alternative methodologies such as document analysis and statistical evaluations. This lack of diversity in research methods can limit the understanding of long-term trends and measurable impacts of Big Data on SME performance, as quantitative methods and historical data analysis provide valuable insights into patterns over time. The predominance of surveys and case studies suggests a reliance on context-specific data, which may introduce biases and limit the generalizability of the findings. To mitigate these risks, future research should consider employing a mixed-methods approach that incorporates a broader array of methodologies, including increased use of experimental designs and quantitative statistical evaluations. This multifaceted approach can enhance the robustness and credibility of conclusions drawn regarding the impact of Big Data on SMEs, ultimately leading to more actionable insights and recommendations for practitioners.

3.4. Results of Individual Studies.

Figure 19 reveals that the focus on various types of database systems is quite distinct, with a clear dominance in relational database technologies. A significant 48.99% of the studies concentrate on relational databases, NoSQL databases, making up 34.90% of the research cloud databases account for 14.77% of the research studies and High-Performance Computing (HPC) constitutes the smallest portion of the research, at just 1.34%, but still represents a critical niche area. The distribution of research topics reflects the ongoing evolution in database technologies, with relational databases maintaining their foundational role, while NoSQL and cloud databases capture increasing interest due to their adaptability in modern data-intensive applications.

Figure 20 depicts that the majority of the studies, 48.32%, focus on data marts And with OLAP systems following closely, accounting for 25.50% of the studies, The ETL (Extract, Transform, Load) tools represent 24.16% of the research, underscoring their role in managing the data integration process. a very small portion of the research addresses CRM systems, data mining, and data warehousing directly, each contributing just 0.67%. This could indicate either a lack of focus on these topics within the reviewed research or that these areas are considered foundational, with most studies assuming their presence rather than focusing on them in detail.

3.5. Results of Synthesis

3.5.1. Characteristics and Risk of Bias among Contributing Studies

In the systematic review conducted, various data collection methods were employed to gather information from the analyzed papers. The results in Figure 21 indicate a diverse approach to data collection, with each method contributing uniquely to the overall findings. The most frequently utilized method was document analysis, accounting for 34.46% of the total data collection efforts. This suggests that a significant portion of the research relied on existing literature and documents, which is a common practice in systematic reviews as it allows researchers to synthesize previous findings and establish a comprehensive understanding of the topic at hand. Following document analysis, surveys emerged as another prominent method, representing 31.08% of the total data collection. Surveys are particularly valuable in gathering quantitative data from larger populations, enabling researchers to identify trends and patterns that may not be evident through qualitative methods alone. The integration of surveys into the systematic review highlights an effort to capture a broad range of perspectives and experiences related to the research question. Interviews and observations were also employed but to a lesser extent, with interviews comprising 17.57% and observations 16.89% of the total methods used. Interviews provide in-depth insights into individual experiences and opinions, allowing for a nuanced understanding of complex issues that may not be fully captured through quantitative measures alone. Observations, while less frequently used than other methods, offer direct insight into behaviors and interactions within natural settings, adding another layer of richness to the data collected.

3.5.2. Results of Statistical Synthesis.

As illustrated in Figure 22, the synthesis of included studies reveals a predominance of quantitative analysis methods, employed in 62.67% of the studies. This strong preference for numerical and statistical evaluations highlights a robust focus on assessing the impact of database and data warehouse technologies through measurable outcomes. In contrast, thematic analysis, which delves into qualitative patterns and themes, was utilized in 37.33% of the studies. This indicates a significant, though less predominant, role for qualitative insights in understanding the nuances of technology impacts.

3.5.3. Investigating Heterogeneity

In the conducted analysis of various papers for the systematic review, the results reveal a significant distribution among four major technology providers: Microsoft SQL Server, IBM, AWS Redshift, and Oracle. Figure 23 indicates that Microsoft SQL Server holds a dominant position with a substantial 54% representation in the reviewed literature.

This suggests that it is widely recognized and utilized within the context of the studies analyzed, potentially due to its robust features, extensive support, and integration capabilities within enterprise environments. Following Microsoft SQL Server, IBM accounts for 18% of the references found in the papers. This indicates a notable presence but suggests that it may not be as prevalent as Microsoft SQL Server in this body of work. IBM’s offerings are often associated with advanced analytics and data management solutions, which could explain its relevance in specific contexts within the systematic review. AWS Redshift has a representation of 14.67%, reflecting its growing adoption in cloud-based data warehousing solutions. As organizations increasingly migrate to cloud infrastructures for scalability and flexibility, AWS Redshift’s role is likely to expand further in future research. Lastly, Oracle comprises 13.33% of the references noted in the analysis. While it has a smaller share compared to its competitors, Oracle remains a key player known for its comprehensive database solutions and enterprise-grade performance.

3.5.4. Sensitivity Analysis Results

In the context of the analysis conducted on various papers for a systematic review, the results indicate a significant distribution among small businesses, SMEs (Small and Medium Enterprises), and startups. Figure 24 indicates that small businesses account for approximately 36.67% of the total analyzed titles, while SMEs represent a larger portion at 43.33%. Startups, on the other hand, comprise 20.00% of the total count.

This distribution highlights the prominence of SMEs in the industry context, suggesting that they play a crucial role in economic development and job creation. The categorization into small businesses, SMEs, and startups is essential for understanding their respective impacts on the economy. Small businesses often serve as the backbone of local economies by providing employment opportunities and fostering innovation at a community level. They are typically characterized by their limited scale in terms of revenue and number of employees but can be highly influential in niche markets. SMEs encompass a broader range of enterprises that may have more resources than typical small businesses but still operate below certain thresholds defined by national or international standards. Their larger share in this analysis (43.33%) underscores their importance in contributing to GDP and employment across various sectors. SMEs often benefit from government support programs aimed at enhancing competitiveness and sustainability. Startups represent a dynamic segment within this landscape, accounting for 20% of the analyzed titles. These entities are generally characterized by their innovative approaches and potential for rapid growth. However, they also face unique challenges such as securing funding and navigating market entry barriers. The relatively smaller percentage reflects both the high failure rate associated with startups as well as their emerging nature within specific industries. As depicted in figure 25, many of the studies, accounting for 44.67%, are centered around data warehouse systems, indicating a strong research interest in the design, implementation, and optimization of these systems.

This reflects the growing importance of data warehouses in managing large-scale data and supporting advanced analytics in modern enterprises. Database technologies represent the second-largest category, comprising 26% of the collected research papers. This suggests that, while data warehouses are a key focus, there is still considerable interest in the foundational technologies that underpin data storage, retrieval, and management systems. Research in this area may explore advancements in database architectures, query optimization, and security features. Business technology and SMEs performance, both accounting for 14.67%, highlight a growing focus on the practical applications of these technologies within organizational and business contexts. These studies are likely exploring how innovations in database and data warehouse systems can improve overall business efficiency, decision-making processes, and the operational performance of small and medium-sized enterprises (SMEs).

3.6. Reporting Bias

Figure 26 offers graphical representation of the breakdown of study types and revealed a diverse methodological approach among the included researches. Of the studies reviewed, 39.01% employed quantitative methods, indicating a strong focus on numerical data and statistical analysis. Qualitative studies comprised 33.33% of the sample, highlighting a significant emphasis on in-depth, descriptive insights into organizational performance. Mixed-methods studies accounted for 27.66%, reflecting a balanced integration of both quantitative and qualitative approaches. This distribution underscores the comprehensive nature of the review, incorporating a range of perspectives and methodologies to assess the multifaceted impact of database and data warehouse technologies.

The risk of bias was assessed across these studies using established tools, such as the Newcastle-Ottawa Scale (NOS). The studies demonstrated varying levels of risk, with many quantitative studies having moderate to low risk due to their robust statistical methods, while qualitative studies often faced challenges related to subjectivity and bias in interpretation. As depicted in Figure 27, the synthesis of included studies reveals a predominance of quantitative analysis methods, employed in 62.67% of the studies.

This strong preference for numerical and statistical evaluations highlights a robust focus on assessing the impact of database and data warehouse technologies through measurable outcomes. In contrast, thematic analysis, which delves into qualitative patterns and themes, was utilized in 37.33% of the studies. This indicates a significant, though less predominant, role for qualitative insights in understanding the nuances of technology impacts.

3.7. Certainty of Evidence

Figure 28 shows the analysis of business performance metrics that reveals a notable emphasis on operational efficiency, which constitutes 47% of the evaluated metrics. Cost savings follow at 32%, while revenue growth is reported in 19% of the studies. Customer satisfaction, though important, is the least represented metric, accounting for only 1%. This distribution indicates that operational efficiency is the most frequently assessed outcome in relation to these technologies, highlighting its critical role in performance improvements. Conversely, the minimal focus on customer satisfaction suggests a potential area for further investigation to fully understand the breadth of impacts these technologies have on organizational performance.

As shown in Figure 29, when it comes to IT performance, scalability is a big deal. In fact, it accounts for 38% of the metrics analyzed. This means that being able to scale up or down easily is super important.

Right behind scalability is data accuracy, which makes up 34% of the metrics. This shows just how crucial it is to make sure the information being used is correct and reliable. Query performance, at 28%, may not be as high on the list, but it's still essential. This metric looks at how well the technology can handle and process requests for data. IT performance is a complex issue that can have a big impact on how well an organization functions. By focusing on scalability, data accuracy, and query performance, businesses can ensure that their IT systems are running smoothly and effectively.

Data analysis techniques utilized across the included studies indicate a predominant use of quantitative analysis methods, employed in 62.67% of the studies. This approach suggests a strong preference for numerical and statistical evaluation of the technologies' impact. In contrast, thematic analysis, which focuses on identifying and interpreting patterns and themes within qualitative data, was used in 37.33% of the studies. These findings highlight a significant emphasis on quantitative methods for assessing performance impacts, while thematic analysis also plays a notable role in understanding the qualitative aspects of database and data warehouse technologies.

3.8. Key Findings and Strategic Implications for Business Leaders.

The systematic review highlights several critical insights regarding the impact of database and data warehouse technologies on business performance. These insights provide valuable strategic guidance for business leaders aiming to enhance their organization’s efficiency, reduce costs, and gain a competitive edge. The study highlights the importance of data warehouse systems and relational databases in modern business for advanced analytics and operational efficiency. These systems centralize data sources, enabling better decision-making and access to high-quality data. The latest database technologies, like NoSQL and cloud databases, offer scalability and flexibility, enabling seamless operations and quick adaptation to data management strategies. Top IT performance indicators include scalability, data accuracy, and query performance, emphasizing the need for robust and timely data systems.

The research also established that geographical and organizational variability significantly influences the benefit levels derived from database and data warehouse technologies. Indeed, larger organizations with more sophisticated supporting technological infrastructures have seen overall better performance and increased decision-making capability. This points out the need for fitting approaches depending on the size and maturity of an organization. The small ones or those still at the initial stages of digitization would need more bespoke solutions that answer their specific business needs, while larger entities have the chance to reap more from matured systems to their benefit regarding data-driven decisions. The results show that database technologies are constantly evolving and hence businesses should adopt strategies that fit within their operational and technological landscape.

Strategic investments in data warehouse systems and modern technologies, such as NoSQL and cloud databases, offer scalable cost-effective solutions, reducing operational expenditure by optimizing storage. These technologies avoid redundancy and provide flexible pricing models based on actual usage, scaling infrastructure costs in direct relation to business growth. The same is further enhanced in operational efficiency through optimized relational databases and ETL tools that help expedite data processing and ensure accuracy for speedy decisions. Advanced analytics will help drive insight into customer behavior and market trends, thereby securing a competitive advantage by making better decisions and offering innovation to improve customer service. Moreover, scalability is important, whereby cloud-based and NoSQL databases guarantee flexibility and speed in terms of meeting the ever-growing demands of data storage and applying business for long-term success. Each category in this Table 12 offers strategic recommendations for business leaders looking to improve their operations.

Cost efficiency entails the enablement of scalable NoSQL and cloud technologies that reduce fixed costs, allowing companies to pay only for what they use. Operational benefits arise from optimized relational databases and ETL tools, in that these raise data processing speeds, improve accuracy, and further accelerate performance. Advanced analytics from data warehouses would also improve customer impact by showing rich insights into improving customer satisfaction and loyalty. Scalability ensures investment in adaptable solutions to handle the growth of data efficiently, thus enabling businesses to be agile and responsive to market demands. These findings help to align IT investments with key performance metrics, driving operational efficiency to competitive advantage.

3.9. Decision-Making Framework for Implementing Database and Data Warehouse Technologies.

The implementation of database and data warehouse technologies is a critical decision for small and medium enterprises (SMEs) looking to improve data management, optimize operations, and drive business intelligence. However, the decision-making process is highly variable, depending on the unique characteristics and constraints of each SME. Factors such as the size of the enterprise, industry, data complexity, growth plans, budget, and technological expertise all play significant roles in determining the most suitable solution.

For smaller enterprises, the need for cost-efficient, easy-to-manage systems often takes precedence, while medium-sized firms may require more robust, scalable solutions to handle larger data volumes and multi-functional business processes. Additionally, industry-specific requirements, particularly in regulated sectors like healthcare or finance, introduce further complexities such as data security, compliance, and integration challenges. In Table 13, we propose outlines of how these considerations influence decision-making, providing SMEs with a comprehensive framework to select the most appropriate database or data warehouse technology based on their individual needs.

The decision-making framework for SMEs highlights the dynamic interplay between technical, financial, and operational factors, underscoring that a one-size-fits-all approach is impractical. Different types of SMEs—whether small, medium-sized, data-intensive, or transactional—require tailored solutions that balance cost, complexity, and functionality. Budget-conscious SMEs often opt for cloud-based or open-source database solutions that minimize upfront costs, providing them with the flexibility to expand as they grow. These solutions are particularly attractive for enterprises with limited IT infrastructure, allowing them to focus on immediate business needs without investing in expensive, on-premises systems. In contrast, growth-oriented SMEs plan for scalability, choosing technologies that can evolve with their expanding data needs. This forward-thinking approach ensures that they are not burdened by costly migrations or system overhauls in the future.

For data-intensive businesses, the decision-making process revolves around the need for advanced analytics and large-scale data processing. Here, the focus shifts to data warehouse technologies that can integrate with existing business intelligence (BI) tools and handle complex queries, making real-time reporting and decision-making more efficient. These businesses prioritize performance, scalability, and integration capabilities, often choosing cloud-based data warehouses that provide flexibility and agility. Transactional businesses, on the other hand, require real-time processing and quick access to data, favoring databases that offer fast transaction speeds and reliable performance. Their focus is less on large-scale analytics and more on day-to-day operational efficiency. This distinction illustrates the importance of understanding the business’s core data needs when selecting between a database and a data warehouse. Industry compliance is a pivotal factor for SMEs in regulated sectors, such as healthcare, finance, or legal services. These industries require robust data governance, security features, and adherence to strict regulatory frameworks like GDPR or HIPAA. SMEs in these sectors prioritize systems that can meet compliance demands without compromising operational flexibility, often leaning toward on-premises solutions that give them greater control over data security and privacy. Technological expertise also significantly impacts the decision-making process. SMEs with skilled IT teams may choose more complex, customizable systems that can be tailored to their specific requirements, while those lacking such expertise gravitate toward vendor-managed solutions that reduce the burden of system maintenance and technical oversight.

3.10. Best Practices for Successful Implementation of Database and DW Technologies

Implementing database and data warehouse technologies in SMEs is a strategic decision that requires careful consideration of each business’s specific needs, resources, and long-term goals as proposed in Table 14. Whether a small enterprise with limited re-sources or a data-intensive business requiring advanced analytics, the selection and execution of these technologies must align with the operational and regulatory environment. Best practices such as clear goal setting, robust data governance, and security controls ap-ply broadly but are adapted differently based on factors like business size, industry, data complexity, and technical expertise. This customized approach ensures that the chosen systems not only meet current demands but also support future growth and scalability, performance tuning, security updates, and data backups. Finally, identification and selection of technologies that would scale the organization's growth ensure that the system does not face performance-related challenges with increased data volumes and user demands.

The variation in best practices across different types of SMEs underscores the im-portance of a tailored approach to database and data warehouse implementation. Small enterprises typically prioritize cost-efficiency and simplicity, while medium-sized and growth-oriented businesses focus on scalability and long-term flexibility. For da-ta-intensive companies, the ability to handle large-scale analytics and performance optimization is crucial, while transactional SMEs emphasize real-time processing capabilities. Regulated businesses must place security and compliance at the forefront of their decision-making, integrating strict data governance strategies. The technological expertise within the SME also plays a significant role; tech-savvy businesses are more likely to customize their systems, while less technical companies rely on vendor-managed solutions. This diversity highlights the need for SMEs to adopt a decision-making process that aligns with their specific operational context, ensuring that the chosen technology delivers both immediate value and future readiness.

3.11. Metrics and KIPs for Measuring Performance of Database and DW Technologies.

The performance of database and data warehouse technologies in SMEs can be optimized by using relevant metrics and KPIs that align with the specific characteristics of each business type as proposed in Table 15. These metrics help measure system reliability, scalability, data quality, security, and overall business value. As SMEs vary in size, growth potential, industry requirements, and technological capacity, the importance of these KPIs also shifts. Customizing the selection of performance indicators ensures that each SME can monitor and enhance its database and DW systems in a way that supports its operational and strategic goals.

The diversity of SMEs requires a tailored approach to measuring database and data warehouse performance. Small enterprises benefit most from focusing on simplicity and basic operational efficiency metrics, such as uptime and resource utilization, while me-dium-sized businesses must prioritize scalability, data accuracy, and concurrency to manage increasing data volumes. Data-intensive SMEs rely heavily on ETL efficiency and system performance metrics, as their operations depend on handling large datasets effec-tively. Transactional SMEs need real-time metrics like query response time and through-put to ensure seamless operations, while regulated SMEs prioritize security and compli-ance KPIs to meet industry standards. Growth-oriented businesses emphasize scalability, ensuring their systems can expand as their needs evolve, while low-growth SMEs focus on maintaining stability with basic performance indicators. Tech-savvy SMEs take a more sophisticated approach, tracking advanced KPIs such as elasticity and resource utiliza-tion, while non-tech-savvy SMEs rely on vendor-managed solutions, focusing on vendor uptime and security compliance. These distinctions highlight the need for a customized, KPI-driven strategy to optimize database and data warehouse performance in SMEs.

3.12. Customizing The Database and Data Warehouse Technologies for Different SME Industries.

The selection and customization of database and data warehouse technologies for SMEs vary significantly depending on the industry. Different industries have unique op-erational requirements, data complexities, compliance needs, and customer engagement models. As a result, businesses in sectors like healthcare, finance, retail, and manufactur-ing need to adopt tailored technology solutions that address their specific data manage-ment, security, and scalability needs. By choosing the appropriate database and data warehouse technologies, SMEs can enhance their efficiency, gain actionable insights, and remain competitive in their respective markets using the proposed Table 16.

The customization of database and data warehouse technologies for different indus-tries highlights the diverse data management challenges SMEs face. Retail and e-commerce SMEs focus on real-time data analytics to track sales and customer behaviors, benefiting from cloud-based solutions for scalability and cost-efficiency. Healthcare and finance SMEs, on the other hand, prioritize security and regulatory compliance, opting for on-premises or hybrid solutions that safeguard sensitive data. Manufacturing and logistics SMEs require integration with IoT systems to monitor real-time data from equipment and supply chains, enabling predictive maintenance and operational optimization. Similarly, media and telecommunications SMEs rely heavily on NoSQL and graph databases to handle vast amounts of unstructured and streaming data, driving customer engagement and content personalization. Each industry’s approach to selecting and customizing these technologies ensures that SMEs can leverage their data for improved decision-making, enhanced customer experiences, and streamlined operations.

3.13. Proposed Industry-Specific Frameworks for Database and Data Warehouse Technologies

Table 17 customizes the framework for various industries, covering the essential considerations for selecting and deploying database and data warehouse technologies, such as data complexity, compliance needs, performance requirements, and scalability. Each industry has unique data needs, compliance standards, and operational goals, which are reflected in this customized framework.

Each industry faces unique challenges when it comes to data management, compliance, and performance requirements. Retail and e-commerce industries rely heavily on scalable cloud data warehouses integrated with customer interaction data and real-time analytics to optimize customer experience and inventory management. Healthcare and finance, with their stringent compliance regulations, require highly secure, encrypted database technologies paired with scalable on-premise or cloud-based warehouses to ensure data privacy and regulatory compliance. Manufacturing and logistics sectors prioritize real-time data processing from IoT sensors and predictive analytics for operational optimization. In contrast, media and entertainment businesses focus on high-performance databases capable of handling unstructured streaming data and user preferences, ensuring seamless content delivery and personalization.

a. Real Case Studies and How They Relate to the Database and Data Warehouse Performance

The application of database and data warehouse technologies in small and medium-sized enterprises (SMEs) has been explored through several real-world case studies, which provide insights into the practical implementation and performance of these systems. These cases reinforce the findings of the systematic review by highlighting how the adoption of these technologies contributes to operational efficiency, scalability, and compliance. Through the examination of both successes and challenges, the case studies offer a comprehensive understanding of the technological landscape for SMEs, aligning with broader academic insights. One case study involves a small retail company that successfully integrated a cloud-based data warehouse to manage real-time sales and inventory data. Before the implementation, the company faced challenges with data fragmentation and slow retrieval speeds, which hampered decision-making processes. After adopting a cloud solution, they were able to centralize data from multiple sources, enabling faster data access and more accurate sales forecasting. The scalability of the cloud system allowed the company to handle seasonal peaks in demand without significant infrastructure upgrades. This example directly links to the findings of the systematic review, which emphasize the importance of performance and scalability in retail, particularly for real-time analytics.

Another case study focuses on a healthcare provider that adopted a HIPAA-compliant data warehouse to manage electronic medical records (EMR). Prior to implementation, the provider struggled with regulatory compliance and data security issues, particularly regarding patient confidentiality. The introduction of encrypted data storage and secure access protocols resolved these concerns, while the system's predictive analytics capabilities improved patient care by identifying at-risk individuals early. This case mirrors the systematic review's findings that highlight data sensitivity and regulatory compliance as key priorities in the healthcare industry, underscoring the importance of security in database systems. In the manufacturing sector, a small electronics company implemented a database system integrated with IoT sensors to monitor equipment performance in real time. Before the system was introduced, the company experienced frequent equipment downtime, leading to costly delays in production. The new system enabled predictive maintenance by analyzing sensor data, significantly reducing unplanned downtime and improving operational efficiency. The case aligns with the systematic review’s insights into the role of databases in supporting real-time data processing and predictive analytics in manufacturing environments.

A financial institution offers another relevant case study. The company transitioned from a traditional on-premises database system to a cloud-based solution to handle real-time financial transactions and manage risk. The previous system struggled with high transaction volumes and increasing compliance demands, but the cloud solution provided the scalability and security required to meet these challenges. The new system also incorporated real-time fraud detection mechanisms, enhancing both operational efficiency and customer trust. This case directly relates to the systematic review’s findings, which highlight the critical need for high performance, scalability, and regulatory adherence in the finance industry. Table 18 summarizes these real-world case studies and illustrates the key metrics before and after database and data warehouse implementation.

These case studies provide clear examples of how SMEs have leveraged database and data warehouse technologies to overcome common challenges in their industries. The systematic review's emphasis on performance, scalability, and regulatory compliance is reflected in each case, demonstrating the practical relevance of the research. These cases not only validate the academic findings but also offer a roadmap for SMEs looking to implement similar technologies to achieve competitive advantage and operational efficiency. The comparison table helps visualize key performance improvements across different industries, while the case study timelines can illustrate the various phases of implementation, from initial challenges to final outcomes. Each case offers important lessons for SMEs, showing how the right technological solutions can drive both short-term efficiency gains and long-term sustainability.

b. Roadmap for SME Businesses and Policy Recommendations.

The proposed roadmap in Table 19 ties the technological evolution of SMEs to industry-specific needs and integrates policy recommendations that can help businesses scale, compete globally, and operate sustainably. Retail SMEs benefit from integrating advanced CRM and ERP systems to enhance customer engagement and optimize inventory management, with government policies focused on supporting digital marketing and e-commerce expansion. Healthcare SMEs require secure and compliant data management systems, with governments incentivizing the adoption of AI for diagnostics. Finance SMEs need support for adopting AI and blockchain to enhance fraud detection and secure transactions, while manufacturing SMEs can increase productivity through IoT and predictive maintenance.

In the logistics and supply chain sector, government subsidies for adopting advanced tracking and AI-based route optimization can enhance global supply chain management. Similarly, energy SMEs should receive incentives to adopt renewable energy technologies and IoT systems for real-time grid monitoring, ensuring sustainability and regulatory compliance.

To successfully implement database and data warehouse technologies, SMEs need a structured roadmap that outlines clear steps for short-, medium-, and long-term goals. This roadmap will guide businesses through key phases such as assessment, implementation, scaling, and optimization while also considering the challenges and resources needed to adopt these technologies as shown in Figure 30. This leads to the development of policies by governments and industry associations to assist SMEs in adopting data technologies. Financial support may be available through subsidies, tax incentives, or grants that cover part of the initial costs associated with acquiring technology, training, and data migration. Training and education through online courses, certification, and workshops will help SMEs understand the benefits accrued and the best practices concerning database technologies. Public-private partnerships could further enable collaboration between SMEs and technology vendors to develop appropriate solutions. Clear guidance on the issue of data privacy and security compliance, especially in high regulated verticals such as healthcare and finance, would better support SMEs in handling the respective compliance obligations. This will also put SMEs in a position where they can make informed choices regarding their technological investments

The proposed roadmap to the implementation of SMEs covers four steps: adopting database and data warehouse technologies through initial assessment, implementation, scaling, and optimization. Initial assessments allow for identifying needs and challenges, while at this stage, transitions are made from old legacy systems to modern databases or data warehouses. This phase scales database capabilities for increased demand without major overhauls. Therefore, the optimization phase chiefly focuses on long-term improvement by enabling the SMEs to leverage advanced analytics, driving innovation, and thereby maintaining competitiveness. Policies recommend reducing financial barriers while making available necessary resources.

4. Discussion

• General Interpretation of the Results in the Context of Other Evidence

The results of this systematic review reveal that database and data warehouse technologies have a significant impact on organizational performance, with a strong focus on enhancing customer satisfaction, operational efficiency, and competitive advantage. The emphasis on relational databases and data warehousing systems aligns with broader trends in the literature, which highlight the foundational role of these technologies in modern data management. Studies focused on NoSQL databases and cloud computing also underscore the growing interest in scalable and flexible systems, a key concern in the current technological landscape. The prominence of business intelligence systems and their integration with knowledge management aligns with other studies that emphasize how data-driven decision-making boosts organizational performance. This review's findings also support the growing body of evidence showing that business intelligence and data analytics systems are instrumental in achieving long-term business sustainability and gaining a competitive edge. While the literature largely reflects the use of these technologies in developed regions, this review highlights a critical need for more research in developing economies to fully understand the global impact of these technologies.

• b. Limitations of the Evidence Included

A notable limitation in the included studies is the geographical concentration of research, with most studies originating from developed regions. This creates potential challenges in generalizing the findings to less economically developed regions where the infrastructure for database and data warehouse technologies might be less advanced. Additionally, there is an over-representation of studies focusing on customer satisfaction, while employee satisfaction and other internal organizational metrics are less frequently analyzed. This suggests a gap in the literature regarding how these technologies influence internal operations and employee experiences. Another concern is underrepresentation of specific technologies, such as High-Performance Computing (HPC) and CRM systems, despite their importance in certain industries. The limited attention to these areas indicates either a lack of research focus or challenges in implementation that are not well-documented.

• c. Limitations of the Review Process Used

The review process utilized stringent inclusion and exclusion criteria, which helped maintain focus but may have excluded some relevant studies. For instance, the reliance on specific keywords in the search strategy might have unintentionally missed research that employs different terminology or conceptual frameworks. Furthermore, the exclusion of non-English studies could have led to an incomplete representation of global research trends. The lack of automation tools and manual efforts to contact study investigators for missing information introduced some limitations. Although this approach enhanced the accuracy of the review, it may have led to potential biases in study selection or interpretation. Also, the use of the Newcastle-Ottawa Scale (NOS) and GRADE 3.0 frameworks provided a robust quality assessment but may not capture all nuances, especially in newer or interdisciplinary studies.

• d. Implications of the Results for Practice, Policy, and Future Research

The results indicate that organizations should prioritize investments in data warehouse systems and relational databases, as these technologies have been shown to significantly enhance operational efficiency and customer satisfaction. Organizations may also benefit from incorporating NoSQL and cloud database systems to enhance scalability and flexibility in managing large datasets. The results suggest that businesses aiming to improve customer satisfaction, a key driver of success, should focus on data-driven decision-making through business intelligence systems.

From a policy perspective, there is a clear need for governments and regulators, especially in developing countries, to invest in technological infrastructure that supports the adoption of database and data warehouse systems. Encouraging the use of these technologies in small and medium-sized enterprises (SMEs) through incentives and subsidies could lead to greater economic growth and business performance. Furthermore, standardization of data management practices could help reduce variability and enhance the implementation of these systems across industries.

Future research should focus on addressing the geographical bias observed in the current evidence by conducting more studies in developing regions to understand the challenges and benefits of adopting database technologies in these contexts. Moreover, there is a need to explore employee satisfaction and internal organizational outcomes, as they are often overlooked in favor of customer-centric metrics. Expanding the scope of research to include High-Performance Computing and CRM systems could also uncover new insights into niche technological areas. The intersection between business sustainability and competitive advantage deserves further exploration to understand how organizations can leverage database technologies for both short-term and long-term benefits. This would be particularly valuable for businesses in industries undergoing rapid technological transformation.

5. Conclusion

This systematic review provides a detailed assessment of the impact of database and data warehouse technologies on organizational performance, based on a comprehensive analysis of 150 studies conducted between 2014 and 2024. The review highlights a predominant focus on customer satisfaction and operational efficiency, with substantial interest in data warehouse systems and relational databases. However, there is a notable imbalance in research emphasis, with less attention given to employee satisfaction and foundational topics like CRM systems and data mining. The findings reveal a concentration of research in economically advanced countries, which may limit the generalizability of results to less developed regions. The dual focus on competitive advantage and business sustainability underscores the strategic importance of these technologies in achieving both immediate and long-term organizational goals. The predominance of quantitative methods and the emphasis on scalability and data accuracy in IT performance metrics reflect a strong inclination towards numerical evaluations and technical performance aspects. This methodological diversity, encompassing quantitative, qualitative, and mixed methods approaches, ensures a robust understanding of the multifaceted impacts of these technologies. Future research should aim to address the gaps identified, particularly in exploring employee-related outcomes and foundational technologies. Additionally, there is a need to broaden the research scope to include a more diverse range of geographic and economic contexts to provide a more comprehensive understanding of the global impact of database and data warehouse technologies. This review serves as a foundational study for advancing knowledge in this field and guiding future research directions.