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2018 | Buch

Evolution to Modern Data Management Kapitel lesen Erstes Kapitel lesen

Modern Data Strategy

verfasst von: Mike Fleckenstein, Lorraine Fellows

Verlag: Springer International Publishing

Enthalten in: Springer Professional "Wirtschaft+Technik" , Springer Professional "Technik" , Springer Professional "Wirtschaft"

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Über dieses Buch

This book contains practical steps business users can take to implement data management in a number of ways, including data governance, data architecture, master data management, business intelligence, and others. It defines data strategy, and covers chapters that illustrate how to align a data strategy with the business strategy, a discussion on valuing data as an asset, the evolution of data management, and who should oversee a data strategy. This provides the user with a good understanding of what a data strategy is and its limits.

Critical to a data strategy is the incorporation of one or more data management domains. Chapters on key data management domains—data governance, data architecture, master data management and analytics, offer the user a practical approach to data management execution within a data strategy. The intent is to enable the user to identify how execution on one or more data management domains can help solve business issues.

This book is intended for business users who work with data, who need to manage one or more aspects of the organization’s data, and who want to foster an integrated approach for how enterprise data is managed. This book is also an excellent reference for students studying computer science and business management or simply for someone who has been tasked with starting or improving existing data management.

Inhaltsverzeichnis

Frontmatter

Data Strategy Considerations

Frontmatter
Chapter 1. Evolution to Modern Data Management
Abstract
Over the past decades, information technology management has evolved to a realization that data, not just systems and software applications, must be managed. This evolution began with the desire to automate manual tasks. It progressed to integrating independently automated tasks, advanced to large-scale centralized data management applications, and finally evolved into several closely related data management domains, including data quality, data governance, data architecture, master data management, analytics, and others that can be managed in alignment with business goals.
Mike Fleckenstein, Lorraine Fellows
Chapter 2. Big Data and Data Management
Abstract
Big data is used to describe data so large, so complex a mix of structured and unstructured data, and so fast changing that it cannot be managed by conventional means; big data is often described in terms of the 3 V’s—volume, variety, and velocity. Due to big data’s size, our demand for real-time information, and the many different ways in which data might be stored, including within documents human ability to effectively manage data is further being challenged.
Mike Fleckenstein, Lorraine Fellows
Chapter 3. Valuing Data As an Asset
Abstract
We frequently hear the slogan “manage data as an asset.” The implication is that data has worth, and as such, should be treated as a valuable asset. But what is required to manage data as an asset? Organizations understand that data is vitally important to their enterprise, to their ability to manage their finances, execute customer service, and improve their operations. Yet what it means to manage data as an asset is too rarely discussed and not well documented, and most organizations don’t account for their data assets as they do for their other assets. In fact, managing data as an asset has many similarities to managing other assets. Of course, there are some differences because data is intangible and consumed differently than physical assets. However, similarities abound.
Mike Fleckenstein, Lorraine Fellows
Chapter 4. Physical Asset Management vs. Data Management
Abstract
With the growing significance of data as a key component of doing business, organizations are treating data more and more like an asset. Although organizations realize that data holds value, no formal or agreed-upon approach currently exists for data valuation. Thought leaders in the data space have proposed several different approaches to data valuation. They include traditional asset valuation concepts based on cost (e.g., the cost of third-party data), fair market value (i.e., the amount an entity is willing to pay for data or an organization that brings with it access to data), and future revenue (e.g., insight into ongoing consumer behavior through data). More indirect approaches to data valuation highlight risk and cost associated with lack of data quality and data management. These approaches express the value of data in terms of costs incurred when an organization is unable to comply with regulations or adapt to changing demand, due to the poor quality of data and data management.
Mike Fleckenstein, Lorraine Fellows

Data Strategy

Frontmatter
Chapter 5. Leading a Data Strategy
Abstract
Clearly organizations are collecting, creating, and exchanging more and more data. They have a vested interest, and increasingly realized interest, in tapping the potential of this data to increase revenue, to decrease costs, and to help them manage risks. However, this requires a different way of thinking and a maturity of data management that many organizations don’t yet have and must grow over time. Organizations can make a fair amount of progress in specific data management domains from grassroots efforts and IT-driven efforts, but organizations usually reach a point where to really move forward, to make that quantum leap of realizing the benefits from their data, business and IT need to collaborate, think about data as an asset and a business enabler, and apply some degree of discipline to define how to:
  • Move the data maturity of the organization forward and
  • Coordinate existing data-related projects to ensure they support each other, all while
  • Furthering the business goals of the organization
Mike Fleckenstein, Lorraine Fellows
Chapter 6. Implementing a Data Strategy
Abstract
Today there is much excitement about using analytics against reams of data to gain better insight. The hope is that more data will keep us from guessing about what is most suitable for our target audience because it allows us to focus on nuanced solutions tailored to our audience. Similarly, there is excitement about accessibility to data. Between storage in the cloud and access by way of mobile devices, the promise of information at our fingertips becomes more and more real. Entire cities are even building public infrastructures to facilitate this type of instant access. For example, in 2014, Google provided the city of San Francisco with complimentary wi-fi access in select public places. In an even more recent example, New York City began converting its outdated phone booths to a city-wide net of ultra-high-speed, free, wi-fi kiosks, in 2016.
Mike Fleckenstein, Lorraine Fellows
Chapter 7. Overview of Data Management Frameworks
Abstract
As we have seen, a data strategy is the coordinated approach of executing multiple data management domains to help manage revenue, cost, compliance, and risk. This chapter presents an overview of two data management frameworks that embody these domains. These frameworks describe the various domains and their relationships, and they serve as valuable resources for comprehending their components. They are useful additional references for understanding individual data management domains and, in the case of the CMMI DMM model, assessing the organization’s maturity in a given area.
Mike Fleckenstein, Lorraine Fellows

Data Management Domains

Frontmatter
Chapter 8. Data Governance
Abstract
The DMBOK places data governance at the center of its data management framework. So foundational to data management is data governance in the view of the DMBOK that without it, none of the other data management domains will succeed. The DMBOK defines data governance as: “the exercise of authority and control (planning, monitoring, and enforcement) over the management of data assets. The data governance function guides how all other data management functions are performed.”
Mike Fleckenstein, Lorraine Fellows
Chapter 9. Data Architecture
Abstract
Data architecture is a blueprint for how data is stored and flows across the enterprise over its life cycle. This sounds pretty simple. However, it is more intricate than it sounds. The focus of data architecture goes beyond individual systems to understand how data flows across systems; it goes beyond individual data stores to understand where the data is created, where it is maintained, how it is shared, and how it is changed across data stores. The focus of data architecture is also not on the hardware infrastructure, but rather on how the infrastructure network works to deliver trusted information.
Mike Fleckenstein, Lorraine Fellows
Chapter 10. Master Data Management
Abstract
Master data management (MDM) focuses on ensuring that the data that is most important to the organization is well defined. Master data is singled out from other data because the organization considers it mission critical. Master data is typically a small dataset that is uniquely defined throughout the enterprise. Commonly cited master datasets are for customer and for product. But master data can be any entity sufficiently important to an organization that needs to be succinctly defined. As such, it can be a dataset of locations, employees, events, statuses, facilities, or any other entity that the organization deems critical to define in the same way.
Mike Fleckenstein, Lorraine Fellows
Chapter 11. Data Quality
Abstract
Data quality refers to the level of quality of data available to the business user. Data quality has many definitions, but data is generally considered high quality if “they are fit for their intended uses in operations, decision making and planning.”
Mike Fleckenstein, Lorraine Fellows
Chapter 12. Data Warehousing and Business Intelligence
Abstract
Traditionally data warehouses were built to reflect “snapshots” of the enterprise-level operational environment over time. In other words, a certain amount of operational data was recorded at a particular point in time and stored in a data warehouse. Originally, such snapshots were typically taken monthly. Today, they are often taken multiple times per day. Data warehouses provide a history of the operational environment suitable for trend analysis. This allows analysts and business executives to plan for the future based on recent trends. Answers to such questions as how have revenue or costs evolved over time and the ability to “slice and dice” such data are typical functions asked of a data warehouse.
Mike Fleckenstein, Lorraine Fellows
Chapter 13. Data Analytics
Abstract
Analytics combines the use of mathematics and statistics with computer software to derive business knowledge from data. Analytics can be segmented into four types—descriptive, diagnostic, predictive, and prescriptive—as described by Gartner. Descriptive analytics focuses on describing something that has already happened, and suggesting its root causes. This involves the observation of historical data to distinguish the patterns or “descriptions of past events” in order to gain insight by implementing searchable databases. Descriptive analytics, which remains the lion’s share, typically hinges on basic querying, reporting, and visualization of historical data. Descriptive analytics is typically what is executed by traditional business intelligence platforms.
Mike Fleckenstein, Lorraine Fellows
Chapter 14. Data Privacy
Abstract
This chapter on data privacy focuses primarily on the public sector. Although data privacy is critical and evolving in both the private and public sectors, the perspectives, mandates, and drivers differ somewhat between these sectors. This is in contrast to many other areas of data management, which are addressed in similar ways in both the public and private sectors. That said, the chapter offers the lay-reader a respectable overview of data privacy.
Mike Fleckenstein, Lorraine Fellows
Chapter 15. Data Security
Abstract
Data security is critical and evolving in both the private and public sectors. Although the perspectives, mandates, and drivers differ somewhat between these sectors, there are also commonalities in the areas addressed. This chapter focuses primarily on the public sector. This contrasts with other areas of data management, which are addressed in similar ways in both the public and private sectors. This chapter offers the lay-reader an overview of data security. For the purposes of this book, the term “data security” is used throughout to reference what is known in other communities as “information security” or “cybersecurity.”
Mike Fleckenstein, Lorraine Fellows
Chapter 16. Metadata
Abstract
“Organizations that don’t know what information they have, or need, are unable to leverage information as an asset.” Metadata ensures data is visible, trusted, and usable. Metadata also facilitates data sharing. One might ask: Is data “shared” if it’s exchanged or published without the necessary metadata to understand it? Data should be shared. This is a recognized data management principle. After all, the value of data is limited when it remains in isolated pockets built to meet local needs. Shared, integrated data results in consistent and improved decisions and customer service. There is general agreement that data is only “shared” if the metadata necessary to understand and use it effectively is also provided. This has implications for the importance of metadata management programs and also for data governance and for “open” datasets published by the government.
Mike Fleckenstein, Lorraine Fellows
Chapter 17. Records Management
Abstract
Records are “data or information in a fixed form that is created or received in the course of individual or institutional activity and set aside (preserved) as evidence of that activity.” What gives data their recordness—what allows us to sometimes think of data as records—are data’s relationship to the activities that created them and in turn their evidence of those activities. How institutions conceive of “records” varies greatly. Some institutions understand records broadly, considering all documents to be records. Other institutions think of records more narrowly, considering only the documents that have been formally declared as a record in an authoritative recordkeeping system as records. In this chapter, we take a relatively broad view of records to be any document with information fixed in any form that has a relationship with any business activity. Records can come in a variety of formats. Traditionally, we think of records as formal documents like an agenda or meeting minutes. The definition of records we use in this chapter is a broader notion of the term. Records can be a website, an instant message conversation, a voice mail, videos or surveillance tapes, an email, or a dataset. To properly manage records, it is important to understand the full information infrastructure and the variety of formats used within an organization.
Mike Fleckenstein, Lorraine Fellows
Backmatter
Metadaten
Titel
Modern Data Strategy
verfasst von
Mike Fleckenstein
Lorraine Fellows
Copyright-Jahr
2018
Electronic ISBN
978-3-319-68993-7
Print ISBN
978-3-319-68992-0
DOI
https://doi.org/10.1007/978-3-319-68993-7

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