Data Governance and Data Management: Contextualizing Data Governance Drivers, Technologies, and Tools

By Rupa Mahanti

This book delves into the concept of data as a critical enterprise asset needed for informed decision making, compliance, regulatory reporting and insights into trends, behaviors, performance and patterns. With good data being key to staying ahead in a competitive market, enterprises capture and store exponential volumes of data.  Considering the business impact of data, there needs to be adequate management around it to derive the best value.    Data governance is one of the core data management related functions. However, it is often overlooked, misunderstood or confused with other terminologies and data management functions. Given the pervasiveness of data and the importance of data, this book provides comprehensive understanding of the business drivers for data governance and benefits of data governance, the interactions of data governance function with other data management functions and various components and aspects of data governance that can be facilitated by technology and tools, the distinction between data management tools and data governance tools, the readiness checks to perform before exploring the market to purchase a data governance tool, the different aspects that must be considered when comparing and selecting the appropriate data governance technologies and tools from large number of options available in the marketplace and the different market players that provide tools for supporting data governance.   This book combines the data and data governance knowledge that the author has gained over years of working in different industrial and research programs and projects associated with data, processes and technologies with unique perspectives gained through interviews with thought leaders and data experts. This book is highly beneficial for IT students, academicians, information management and business professionals and researchers to enhance their knowledge and get guidance on implementing data governance in their own data initiatives.

• Language: English
• Publisher: Springer
• Release date: Sep 8, 2021
• ISBN: 9789811635830

Rupa Mahanti

Dr. Rupa Mahanti is a Business and Information Management consultant with has extensive and diversified consulting experience in different technologies, solution environments, business areas, industry sectors, and geographies.

Book preview

© The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2021

R. MahantiData Governance and Data Managementhttps://doi.org/10.1007/978-981-16-3583-0_1

1. Introduction to Data, Data Governance, and Data Management

Rupa Mahanti¹  

(1)

Strathfield, NSW, Australia

Data is a precious thing that will last longer than the system themselves.

—Tim Berners-Lee, Computer Scientist and Inventor of World Wide Web

Abstract

This chapter introduces the audience to the evolution of data, importance of data, data governance, and data management.

1.1 Evolution of Data

Technology, civilizations, and culture have evolved over history. However, what has not changed are facts. The way they have been stored, maintained, and transferred has evolved. With the passage of time, and evolution of technologies, civilizations, and culture, the methodologies used to capture, store, process, and use these facts have evolved. Similarly, data, that is, a representation of facts has had its own evolution cycle.

Until the advent of computers, limited facts were documented, given the expense and scarcity of resources, and effort to store and maintain them. In ancient times, it was not uncommon for knowledge to be transferred from one generation to another by the process of oral learning, in contrast to our current digital age, which has elaborate document and content management systems that store knowledge in the form of documents.

With the advent of computers and subsequent innovations in computing and industrial automation, a marked shift in data processing has resulted in the electronic recording and processing of data to support business operations. While electronic storage and processing of data started at the end of the nineteenth century, owing to the cost and limitations of storage, the amount of data that could be stored was relatively less, and data management as a discipline was less complex. Technology was seen as a means to reduce manual overhead to generate correct reports, and data was seen as a by-product.

However, with the advancement in technology, and decreasing cost of hardware, increasing volume of data could be stored, and with the internet age, it has culminated into what we can call an explosion of data.

1.2 Data and Its Governance

We are currently living in the digital age and data is the key differentiator. While physical and financial assets are valued, appear on an organization’s balance sheet, and are usually adequately governed, data assets are often the worst governed, least understood, and most poorly utilized key asset in most organizations. This is due to its abundance, difficulty in assigning a financial value to data, and its faulty perception that data is viewed as a considerable expense (Pierce 2007). This is despite the fact that data outlives systems and applications, and can be disruptive, if not governed effectively. However, with data being the driving force behind decisions and activities in most organizations in today’s age, data is an asset, and needs to be treated as such.

Organizations that have learned to value data and its governance have often learned it the hard way—when something goes wrong. An effective data governance system ensures that the data is treated as an enterprise asset by overseeing their use, aiding data discovery, and optimizing processes around their collection, protection, privacy, access, and usage.

1.3 Data Governance and Data Management

Data management is no longer restricted to the capture, processing, and storage of data for producing reports by technical teams but instead, it is a complex cross functional enterprise wide program and discipline, having several intertwined subdisciplines such as data security management, data architecture, data quality, master data management, reference data management, and data governance. Data governance is the adhesive tying together all these different data management sub-disciplines as shown in Fig. 1.1.

../images/504549_1_En_1_Chapter/504549_1_En_1_Fig1_HTML.png

Fig. 1.1

Data governance tying together the data management functions

1.4 Concluding Thoughts

Organizations have lots of data which can be grouped by similar characteristics—master data, reference data, metadata, and transactional data. Data can also be grouped in terms of restricted data, confidential data, private data, internal data, and public data. The different categories of data need to be managed adequately, and organizations generally have a lot of data initiatives running in parallel, for example metadata management to manage the metadata, master data management to manage master data, data security initiatives to ensure data is secure, data quality initiatives, and so on. These initiatives need to be aligned, dependencies need to be understood, and a working rhythm needs to be established. Data governance provides oversight to these initiatives, and helps in establishing data policies, roles, responsibilities, decision rights, processes, and metrics which facilitate implementation of good data management practices.

Reference

Pierce EM (2007) Designing a data governance framework to enable and influence IQ strategy. In: Proceedings of the MIT 2007 information quality industry symposium. http://​mitiq.​mit.​edu/​IQIS/​Documents/​CDOIQS_​200777/​Papers/​01_​08_​1C.​pdf. Last accessed 12 Dec 2018

© The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2021

R. MahantiData Governance and Data Managementhttps://doi.org/10.1007/978-981-16-3583-0_2

2. Data and Its Governance

Rupa Mahanti¹  

(1)

Strathfield, NSW, Australia

The world is one big data problem.

—Andrew McAfee

Abstract

This chapter introduces to the audience, the basics concepts related to data and discusses the proliferation of data, key terms related to data, and the organization of data. We also discuss the concept of assets, why data can be considered as an asset, the unique properties of data, the data asset life cycle, how data differs from other fixed assets, and the challenges of listing data in the balance sheet. While data is an enterprise asset it is not always treated as an asset which causes problems. Organizations need to classify their assets to manage them effectively. Data can be classified in different ways and these will be discussed in this chapter. Data governance is confused with other data related terms. Some of these are discussed in this chapter. We also discuss in detail the business drivers for data governance and data governance benefits. The people, process, and technology components of data governance will be discussed at a high level in this chapter.

2.1 The Data Deluge

We are living in the digital age which is characterized by sophisticated technology and huge volumes of data. Technology is creating or enabling the creation of more and more data to the extent that, now we are experiencing what we can call a data explosion. While data has always been collected and used, the mode, volume, entity characteristics being captured, and the purposes for which data are used have evolved over ages. The evolution of data can be divided into three eras:

Before the advent of computer and databases. Limited data related to transactions, events, entities, and individuals were captured and stored based on their criticality and need on a future date. Information and records were typically documented in paper files or registers, which were then filed in cabinets, but manual search and retrieval of information from these files was a time taking and tiresome process.

Post advent of computer and electronic storage prior to the Internet era. Data was collected and stored in electronic files and databases based on business requirements. The advancement of technology enabled increasing volumes of data to be captured, processed, and stored for operational, analysis, and reporting purposes.

The Internet era onwards. The Internet era was characterized by a further progress of information technologies, declining cost of disk hardware, and availability of cloud storage. Electronic capture, processing, and storage of large volumes of data through multiple channels became common. The advancement in technologies enabled sourcing and processing massive amounts of data from heterogeneous sources using various software tools and technologies (example, data warehousing tools, big data technologies, and reporting