Literature Review on Big Data Management and IS Leadership

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Literature Review

Topic: Big Data Management and Analytics and the Role of IS Leadership

1. Introduction

There is a new era in the business sector, where data has become an essential asset that can lead strategic decision making, operational efficiency, and long term competitive edge. The growth of data, which is now measured in terms of its volume, velocity, and variety (big data), has provided opportunities as well as posed challenges to organizations operating in different industries. As such, managing and using big data in a better way have become the key for organizations to stay competitive and be responsive to the time when the market conditions are constantly changing (Mikalef et al., 2018).

The big data management and analytics landscape has become an integral part of information systems (IS) leadership and its significance is growing. IS leadership, for instance, the CIOs (Chief Information Officers) and CDOs (Chief Data Officers), are the ones who are responsible for merging the data and technology strategies with the organization's overall goals, setting data-driven decision-making, and creating a data-centric culture (Davenport & Dyché, 2013). The

focus of the literature review is on the overview of the main aspects of big data management and analytics, the role of IS leadership in this sphere, and the emergent trends and good practices.

2. Enterprise Architecture Frameworks and Big Data Management

Enterprise Architecture (EA) frameworks, which are based on a structured approach to the design, planning, and administration of the complex systems and processes of an organization, are used to create such systems. They guide the way data is stored, processed, and analyzed, ensuring the efficient utilization of big data within the organization. The Open Group Architecture Framework

The Open Group Architecture Framework (TOGAF) is probably the most popular of the enterprise architecture frameworks on the market. The four architecture domains of TOGAF that are business, data, application, and technology are the most important to big data management (Hashmi, 2014). The data architecture domain, such as the one that defines the structure of an organization's data assets including logical and physical data, is also responsible for the data management resources. These areas are pivotal in the process of solving the extensiveness, variety, and speed of big data (Raptis et al., 2019).

agency. In the FEA’s case, the data reference model is a unified approach to depicting the data resources and information flows within an organization. This can be achieved by integrating and managing big data (Bui, 2017).

The enterprise architecture frameworks, in addition, assist in the addressing of technical and infrastructure requirements for managing big data. For example, the technology architecture part of TOGAF, like the hardware, software, and network infrastructure, is required to support big data analytics applications (Hashmi, 2014). The technology architecture is aligned with the organization's data and business requirements by EA frameworks, which then enable the infrastructure to deal with the high volumes, speeds, and varieties of big data.

EA systems promote a holistic and cross-functional approach to data management due to that it is very important for drawing the right conclusions from big data. The TOGAF business architecture domain, for example, lays out the organization's significant business processes, strategies, and objectives which create a context for big data initiatives (Hashmi, 2014). The EA frameworks can do this by bringing together the business, data, and technology viewpoints, which provides the basis for a more comprehensive and strategic approach to big data management.

3. Strategic Planning for Big Data Management and Analytics

Strategic planning for big data management and analytics has to be the key for organizations to align data-driven initiatives with their strategic business objectives. Research shows that the strategic planning process involves four key phases: SWOT analysis, IS vision, IS architecture, and strategic initiatives (Ward & Peppard, 2016). The IS assessment phase is aimed at the identification of the current state of the organizations's IS resources, including data management capabilities. This phase serves as an important part of the process of understanding the company's data landscape, recognizing data silos, and evaluating the data quality, accuracy, and accessibility (Davenport & Dyche, 2013). Such data can then support efforts to define the IS vision and architecture.

• The IS architecture phase is the stage that sets out the rules and the designs which are to be adopted by the organization for the future use of data and analytics resources. The data management phase may include decisions on the data infrastructure, integration

platforms, analytical tools, and governance systems that will guarantee the efficient and ethical utilization of data (Ward & Peppard, 2016).

• The strategic initiatives identification phase involves specifying the specific actions and the projects that the organization must implement to get to its IS vision and to support the strategic objectives of the organization. During that period, certain measures might be taken to ensure quality of data, integration of data, advanced analytics capabilities and creation of data-driven applications and services (Ward & Peppard, 2016).

Successful strategic planning of a big data management and analytics process first implies that there is a strong alignment between the organization’s business strategy and its data and analytics strategy. IS leaders, whether CIO or CDO, are the ones who facilitate alignment between the organization's strategic goals and data/analytics initiatives. This alignment is ensured so that the organization's data and analytics initiatives are directly in line with its strategic goals (Davenport & Dyché, 2013).

Data Management Disciplines and Big Data

The proper management of big data nowadays implies that organizations need to deal with several data management disciplines, such as data collection, processing, quality, warehousing, communication, governance, and analysis (Oracle, 2022). IS leaders should guarantee that the organization shall be equipped with the needed infrastructure, tools, and procedures that allow it to collect, process, and store the data effectively and reliably.

Data quality

The diversity of big data and its complexity may possibly result in numerous quality problems connected to data, such as inconsistencies, inaccuracies, and incompleteness. Data quality is one of the main factors in processing big data and as a result, data-driven decisions are accurate. (Merino et al., 2016). IS leaders have to maintain a close working relationship with data stewards and subject matter experts to design good governance structures, data quality standards, and data cleaning processes.

Data warehousing and communication

With the increasing storing of data by organizations, the issue of establishing efficient data storage and communication becomes a necessity. Data warehousing technologies such as data lakes and cloud based storage can offer the required scalability and performance in terms of handling the big data (Krishnan, 2013). IS leaders who run the data warehousing infrastructure must be sure that it is built to satisfy the specific needs of big data, which involves storing and processing both structured and unstructured data.

Data governance

Research shows data governance must be implemented effectively to ensure that the ethical, legal and regulatory dimensions of big data are properly managed. Such policies and procedures must be established to regulate data ownership, access, security and privacy (Tallon et al., 2013). In setting up a data governance model, the IS department, in collaboration with the organization's legal team and compliance officers, should evaluate all applicable regulations to ensure that the big data use adheres to such regulations and ethical principles.

Data analysis

The main importance of big data is the competitive intelligence as well as the essential information which can be obtained from it. Sophisticated analytic methods including machine learning, predictive modeling, and natural language processing are required for the extraction of meaningful outputs from big and intricate datasets (Mikalef et al., 2018). It is the responsibility of IS leaders to provide the organization with the requisite analytical tools, expertise, and skills to make use of big data and to have data-driven decision making.

4. The Role of IS Leadership in Big Data Management and Analytics

Big data management and use require organizational leadership and collaboration. IN this setting, CIOs and CDOs are crucial because they align data and technology initiatives with company goals (Davenport & Dyché, 2013). IS leaders must create and communicate a compelling data and analytics strategy that aligns with the business plan. This plan should outline the organization's long-term big data management and analytics goals, essential capabilities and resources, and governance structures to ensure ethical and successful data use (Davenport & Dyché, 2013).

This may include investing in data

integration and warehousing technology, learning advanced analytics skills, and partnering with

external data and analytics providers (Mikalef et al., 2018). Collaboration with business stakeholders: IS executives and business stakeholders must

collaborate on big data management and analytics. IS leaders must collaborate with functional

leaders like CMOs, CFOs, and COOs to understand their data and analytics needs and align the

organization's data and technology strategies (Davenport & Dyché, 2013). IS executives must successfully communicate big data's value and impact to the organization's

leadership, shareholders, and other key stakeholders. This may involve showing how data-driven

insights improved decision-making, operational efficiency, and competitive advantage (Mithas et

al., 2012).

The literature review has established several key aspects regarding big data management and analytics, as well as the role of IS leadership in this domain:

1. Enterprise architecture (EA) frameworks, such as TOGAF and FEA, provide a structured

approach to designing, planning, and managing the complex systems and processes

involved in big data management. These frameworks emphasize the importance of data

architecture, technology architecture, and the integration of business, data, and

technology perspectives.

2. Strategic planning for big data management and analytics is essential for aligning data-driven

initiatives with an organization's overall business objectives. The four-phase

process (IS assessment, IS vision, IS architecture, and strategic initiatives identification) helps

organizations develop a comprehensive and strategic approach to big data management.

3. Effective big data management requires addressing a range of data management disciplines, including data collection, processing, quality, warehousing, communication, governance, and analysis. Robust data governance frameworks are crucial for ensuring the ethical, legal, and regulatory aspects of big data are properly managed.

4. IS leaders, such as CIOs and CDOs, play a pivotal role in aligning data and technology

strategies with business objectives, fostering a data-driven culture, driving data governance and ethics, and investing in data and analytics capabilities.

Discredited: The literature review did not find any major discredited findings or approaches in the field of big data management and analytics. However, some aspects that have been challenged or are subject to ongoing debate include:

1. The effectiveness of traditional data management practices in handling the volume, velocity, and variety of big data. Conventional data warehousing and processing techniques have been questioned, leading to the emergence of new technologies and approaches.

2. The ability of organizations to fully capitalize on the potential of big data, as there are still significant challenges related to data quality, integration, and the development of advanced analytics capabilities.

Accepted: The literature review has reinforced the widely accepted notion that big data management and analytics are critical for organizations to maintain a competitive edge, drive operational efficiency, and support strategic decision-making. The growing importance of data as a strategic asset is widely acknowledged, and the need for effective IS leadership in this domain is well-accepted.

Areas of Controversy or Conflict

The literature review did not uncover major areas of controversy or conflict among different schools of thought. However, some emerging areas of debate and discussion include:

1. The role and responsibilities of the Chief Data Officer (CDO) versus the Chief Information Officer (CIO): There is ongoing discussion about the delineation of responsibilities between these two roles and the optimal organizational structure for data and analytics leadership.

2. The balance between centralized and decentralized data management approaches: Some organizations favor a more centralized, enterprise-wide data management strategy, while others advocate for a more decentralized, business unit-level approach. The optimal balance is a subject of debate.

3. The ethical and regulatory implications of big data usage: As organizations grapple with growing concerns around data privacy, security, and the ethical use of data, there is an ongoing discussion about the appropriate frameworks and guidelines for governing the use of big data.

Problems or Issues That Remain Unsolved

The literature review identified several problems or issues that remain unsolved in the field of big data management and analytics.

1. Data integration and interoperability: Effectively integrating data from diverse, heterogeneous sources and ensuring seamless interoperability across an organization's data ecosystem remains a significant challenge.

2. Talent and skill shortages: Organizations continue to struggle with the development and retention of data scientists, data engineers, and other specialized talent required to leverage big data effectively.

3. Scalability and performance limitations: As the volume and complexity of big data continue to grow, organizations face challenges in ensuring the scalability and performance of their data management and analytics infrastructure.

4. Achieving a truly data-driven culture: Despite the recognized importance of data-driven decision-making, many organizations struggle to foster a culture where data is consistently and effectively incorporated into all aspects of the business.

5. Demonstrating the tangible business value of big data initiatives: Quantifying the Return on Investment (ROI) and articulating the direct business impact of big data projects remains a challenge for many organizations.

Emerging Trends and New Approaches

The literature review identified several emerging trends and new approaches in the field of big data management and analytics:

1. Increased focus on data governance and ethics: Organizations are placing greater emphasis on developing robust data governance frameworks and implementing advanced data security measures to address the ethical and regulatory aspects of big data usage.

2. Adoption of cloud-based data management and analytics platforms: The use of cloud-based solutions, such as data lakes, data warehouses, and analytics platforms, is becoming more prevalent as organizations seek scalability, flexibility, and cost-effectiveness in their big data management and analytics capabilities.

3. Emergence of the Chief Data Officer (CDO) role: The growing importance of data as a strategic asset has led to the rise of the CDO role, with these individuals responsible for leading the organization's data management and analytics strategies.

4. Integration of big data with other emerging technologies: Organizations are increasingly integrating big data management and analytics with technologies such as the Internet of Things (IoT), artificial intelligence (AI), and machine learning (ML) to capture, process, and derive insights from a wider range of data sources.

5. Focus on data literacy and upskilling: Organizations are investing in data literacy programs and upskilling initiatives to develop the necessary skills and expertise within their workforce to leverage big data effectively.

6. Emergence of data-driven business models: Some organizations are exploring the integration of big data management and analytics into their core business models, creating new revenue streams and services based on the insights derived from data.

These emerging trends and new approaches reflect the evolving nature of the big data management and analytics landscape, as organizations strive to unlock the full potential of their data assets and stay competitive in the digital era.

Emerging Trends and Best Practices in Big Data Management and Analytics

As the field of big data management and analytics continues to evolve, several emerging trends and best practices are shaping the ways organizations approach and leverage this critical asset.

Increased focus on data governance and ethics: With growing concerns around data privacy, security, and the ethical use of data, organizations are placing greater emphasis on robust data governance frameworks. This includes the development of clear policies and procedures for data ownership, access, and usage, as well as the implementation of advanced data security measures and ethical AI guidelines (Tallon et al., 2013).

Adoption of cloud-based data management and analytics platforms: Cloud-based solutions are becoming increasingly popular for big data management and analytics, as they offer scalability, flexibility, and cost-effectiveness. Organizations are leveraging cloud-based data lakes, data warehouses, and analytics platforms to handle the volume, variety, and velocity of big data (Krishnan, 2013).

Emergence of the Chief Data Officer (CDO) role: The growing importance of data as a strategic asset has led to the rise of the Chief Data Officer (CDO) role. CDOs are responsible for leading the organization's data management and analytics strategies, ensuring data governance, and driving data-driven decision-making (Davenport & Dyche, 2013).

Integration of big data with other emerging technologies: Organizations are increasingly integrating big data management and analytics with other emerging technologies, such as the Internet of Things (IoT), artificial intelligence (AI), and machine learning (ML). This integration enables organizations to capture, process, and derive insights from a wider range of data sources, leading to more comprehensive and actionable business intelligence (Fosso Wamba et al., 2015).

Focus on data literacy and upskilling: As the demand for data-driven decision-making grows, organizations are investing in data literacy programs and upskilling initiatives to develop the necessary skills and expertise within their workforce. This includes training employees on data interpretation, visualization, and storytelling, as well as fostering a culture of data-driven decision-making (Davenport & Dyche, 2013).

Emphasis on data-driven business models: Some organizations are exploring the integration of big data management and analytics into their core business models, creating new revenue streams and services based on the insights derived from data. This can include the development of data-as-a-service offerings, predictive maintenance solutions, or personalized product recommendations (Mikalef et al., 2018).

Conclusion

The effective management and utilization of big data have become critical for organizations to

maintain a competitive edge, drive operational efficiency, and support strategic decision-making.

Enterprise architecture frameworks, strategic planning processes, and robust data management

disciplines are essential elements in the big data management and analytics landscape.

IS leaders, such as CIOs and CDOs, play a pivotal role in aligning data and technology strategies

with the organization's business objectives, fostering a data-driven culture, and driving the

adoption of best practices in big data management and analytics. As the field continues to

evolve, emerging trends, such as increased focus on data governance and ethics, cloud-based

platforms, and the integration of big data with other emerging technologies, will further shape

the ways organizations leverage this critical asset.

By embracing a strategic, holistic, and data-centric approach to big data management and

analytics, organizations can unlock the transformative potential of their data and position

themselves for long-term success in the digital era.

References

Davenport, T. H., & Dyché, J. (2013). Big data in big companies. International Institute for Analytics, 3(2013), 1-18. https://www.academia.edu/download/32021923/Big-Data-in-Big-Companies.pdf

Fosso Wamba, S., Akter, S., Edwards, A., Chopin, G., & Gnanzou, D. (2015). How 'big data' can make big impact: Findings from a systematic review and a longitudinal case study. International Journal of Production Economics, 165, 234-246. https://www.sciencedirect.com/science/article/pii/S0925527314004253

Krishnan, K. (2013). Data warehousing in the age of big data. Newnes. https://books.google.com/books?hl=en&lr=&id=8ngws8f_lNsC&oi=fnd&pg=PP1&dq=Krishnan,+K.+(2013).+Data+warehousing+in+the+age+of+big+data.+Newnes.&ots=gWH5f2bgIA&sig=FdP6TZe-sRf6bQ2pTXxDogIdF1k

Merino, J., Caballero, I., Rivas, B., Serrano, M., & Piattini, M. (2016). A data quality in use model for Big Data. Future Generation Computer Systems, 63, 123-130. https://www.sciencedirect.com/science/article/pii/S0167739X15003817

Mikalef, P., Pappas, I. O., Krogtie, J., & Giannakos, M. (2018). Big data analytics capabilities: a systematic literature review and research agenda. Information Systems and e-Business Management, 16(3), 547-578. https://link.springer.com/article/10.1007/s10257-017-0362-y

Mithas, S., Ramasubbu, N., & Sambamurthy, V. (2011). How information management capability influences firm performance. MIS Quarterly, 35(1), 237-256. https://www.jstor.org/stable/23043496

Bui, Q. (2017). Evaluating enterprise architecture frameworks using essential elements. Communications of the Association for Information Systems, 41(1), 6. https://aisel.aisnet.org/cais/vol41/iss1/6/

Raptis, T. P., Passarella, A., & Conti, M. (2019). Data management in industry 4.0: State of the art and open challenges. IEEE Access, 7, 97052-97093. https://ieeexplore.ieee.org/abstract/document/8764545/

Tallon, P. P., Ramirez, R. V., & Short, J. E. (2013). The information artifact in IT governance: toward a theory of information governance. Journal of Management Information Systems, 30(3), 141-178. https://www.tandfonline.com/doi/abs/10.2753/MIS0742-1222300306

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