First published in 2009, the DMBOK presented a coherent and comprehensive guide to best practices in data governance. It was updated in 2012 to reflect the explosive growth of data, security issues and new services such as the cloud, and to add several topics that were not included in the first edition.
If data really are a corporate resource, it would make sense to manage them as such. How can management continue to justify a situation where dark data outnumbers mission- and business-critical data, where data governance is weak, and big data continues to dominate the media? By contrast, old office furniture stored in a basement is bar coded and inventoried and shown as assets in the accounts.
In recent years, data governance and management have been codified into statute via The Foundations for Evidence-Based Policymaking Act2 (Evidence Act) that requires every executive branch agency to establish a Chief Data Officer (CDO) and identifies three pillars of work for which the CDO bears responsibility: data governance; the Open, Public, Electronic, and Necessary (OPEN) Government Data Act3; and the Paperwork Reduction Act4 (PRA).
Data lifecycle management is the development, execution, and supervision of plans, policies, programs, and practices that deliver, control, protect, and enhance the value of data and information assets throughout their lifecycles8. Data management in the context of this guide focuses on the data lifecycle as it moves through an AI project.
Data management activities start with identifying and selecting data sources, framed in the context of business goals, mission-defined use cases, or project objectives. As you identify data sources, have engineering teams integrate them into the overall data flow, either through data ingestion or remote access methods. Include a clear description of the data through relevant metadata with datasets as they are published.
One example of data lifecycle management is standardizing metadata captured for new data sources by populating a data card used to describe the data.9 Each dataset should contain common interoperable metadata elements that include, but are not limited to, the following:
While minimum tagging requirements vary across different organizations, the list above is provided as a general guideline. For operations that are highly specific or deal with high-impact or sensitive data, the receiving organization may need to capture more metadata fields earlier in the data lifecycle.
This section explores the concepts of data interoperability and integration, management and governance in more detail; highlighting some useful institutional tools and examples that can help practitioners in the development of their data management and governance strategies. It sets out the various institutional frameworks and models of data governance that exist, explains the need for oversight and accountability across the data value chain, and the need for effective legal and regulatory frameworks.
At its heart, this section extols the benefits of thoughtful planning, continuous strategic management and governance of data across its lifecycle, and consideration of user needs from the outset when striving to modernize IT systems and amplify the reusability and audiences of existing data.
The GSBPM developed by the UN Economic Commission for Europe (UNECE) on behalf of the international statistical community, is an excellent example of a systemic, coordinated and collaborative initiative that has established a common standards-based approach to business process development for official statistics. The model offers examples of good practice for handling data interoperability and integration issues from a data management perspective.
As repeated often throughout this Guide, interoperability is a characteristic of high-quality data that should be fostered across organizations; not just by computer scientists, technical experts, or IT departments within organizations. To embed data interoperability as a guiding principle across an organization requires careful planning of governance mechanisms, including appreciating the value and usefulness of oversight and accountability. The form that oversight and accountability will take depends on the size of the organization, the availability of resources, management structure, and the role of the organization in the broader data ecosystem.
This chapter will look at the relationship among privacy frameworks and data management, data governance, and data stewardship, highlighting how frameworks such as the OECD Guidelines and GAPP are used for personal information management. Included in this discussion will be a look at Privacy by Design (PbD), which supports and complements privacy engineering (Figure 3-1).
In a structured data management program, data stewards, who are domain or subject matter experts for each of these classes of data, work with data management experts to ensure that procedures, processes, standards, guidelines, and business rules for using such information support the goals and objectives of the enterprise. This is called data governance.
Understanding how data management frameworks (such as data governance and data stewardship) fit with privacy frameworks (such as GAPP and the OECD Guidelines) is key to organizational development. Such frameworks and guidelines help to create the necessary roles and responsibilities to build and maintain a privacy-aware and ready enterprise. Such understanding will also help to recognize and understand privacy policies at meta-use-case requirements for privacy engineering.
The world of privacy and data protection is uniquely complex. As the field evolves, and, concurrently ubiquitous computing is becoming the norm, it is indispensible to take a global approach to privacy and data protection while remaining aware of the significant discrepancies between the laws, regulations, guidelines, and sensitivities that exist and will remain at the micro level in each country or state.
Current work by international data protection authorities to define accountability is also establishing common definitions and best practices that help advance organizational PbD practices. Similar work is also under way in international standards groups to define privacy implementation, assessment, and documentation methods. The preparation, use, and publication, whether mandatory, contractual, or voluntary, of privacy impact assessments and privacy management frameworks are also on the rise. We are seeing the growth of standardized privacy evaluation, audit, and assurance systems, innovative co-regulatory initiatives, certification seals and trust marks, and other criteria. Enhanced diligence and accountability measures are consistent with the PbD emphasis on demonstrating results. The publication of successful case studies adds illustrative and educational value for others to emulate.
Data management is the process of ingesting, storing, organizing and maintaining the data created and collected by an organization. Effective data management is a crucial piece of deploying the IT systems that run business applications and provide analytical information to help drive operational decision-making and strategic planning by corporate executives, business managers and other end users.
The data management process includes a combination of different functions that collectively aim to make sure the data in corporate systems is accurate, available and accessible. Most of the required work is done by IT and data management teams, but business users typically also participate in some parts of the process to ensure that the data meets their needs and to get them on board with policies governing its use.
This comprehensive guide to data management further explains what it is and provides insight on the individual disciplines it includes, best practices for managing data, challenges that organizations face and the business benefits of a successful data management strategy. You'll also find an overview of data management tools and techniques. Click through the hyperlinks on the page to read more articles about data management trends and get expert advice on managing corporate data.
Data increasingly is seen as a corporate asset that can be used to make better-informed business decisions, improve marketing campaigns, optimize business operations and reduce costs, all with the goal of increasing revenue and profits. But a lack of proper data management can saddle organizations with incompatible data silos, inconsistent data sets and data quality problems that limit their ability to run business intelligence (BI) and analytics applications -- or, worse, lead to faulty findings.
Data management has also grown in importance as businesses are subjected to an increasing number of regulatory compliance requirements, including data privacy and protection laws such as GDPR and the California Consumer Privacy Act (CCPA). In addition, companies are capturing ever-larger volumes of data and a wider variety of data types -- both hallmarks of the big data systems many have deployed. Without good data management, such environments can become unwieldy and hard to navigate.
The separate disciplines that are part of the overall data management process cover a series of steps, from data processing and storage to governance of how data is formatted and used in operational and analytical systems. Developing a data architecture is often the first step, particularly in large organizations with lots of data to manage. A data architecture provides a blueprint for managing data and deploying databases and other data platforms, including specific technologies to fit individual applications.
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