Pillars of Data Governance Readiness: Enterprise Data Management Methodology
Facebook’s data woes continue to dominate the headlines and further highlight the importance of having an enterprise-wide view of data assets. The high-profile case is somewhat different than other prominent data scandals as it wasn’t a “breach,” per se. But questions of negligence persist, and in all cases, data governance is an issue.
This week, the Wall Street Journal ran a story titled “Companies Should Beware Public’s Rising Anxiety Over Data.” It discusses an IBM poll of 10,000 consumers in which 78% of U.S. respondents say a company’s ability to keep their data private is extremely important, yet only 20% completely trust organizations they interact with to maintain data privacy. In fact, 60% indicate they’re more concerned about cybersecurity than a potential war.
The piece concludes with a clear lesson for CIOs: “they must make data governance and compliance with regulations such as the EU’s General Data Protection Regulation [GDPR] an even greater priority, keeping track of data and making sure that the corporation has the ability to monitor its use, and should the need arise, delete it.”
With a more thorough data governance initiative and a better understanding of data assets, their lineage and useful shelf-life, and the privileges behind their access, Facebook likely could have gotten ahead of the problem and quelled it before it became an issue. Sometimes erasure is the best approach if the reward from keeping data onboard is outweighed by the risk.
But perhaps Facebook is lucky the issue arose when it did. Once the GDPR goes into effect, this type of data snare would make the company non-compliant, as the regulation requires direct consent from the data owner (as well as notification within 72 hours if there is an actual breach).
Considering GDPR, as well as the gargantuan PR fallout and governmental inquiries Facebook faced, companies can’t afford such data governance mistakes.
During the past few weeks, we’ve been exploring each of the five pillars of data governance readiness in detail and how they come together to provide a full view of an organization’s data assets. In this blog, we’ll look at enterprise data management methodology as the fourth key pillar.
Enterprise Data Management in Four Steps
Enterprise data management methodology addresses the need for data governance within the wider data management suite, with all components and solutions working together for maximum benefits.
A successful data governance initiative should both improve a business’ understanding of data lineage/history and install a working system of permissions to prevent access by the wrong people. On the flip side, successful data governance makes data more discoverable, with better context so the right people can make better use of it.
This is the nature of Data Governance 2.0 – helping organizations better understand their data assets and making them easier to manage and capitalize on – and it succeeds where Data Governance 1.0 stumbled.
Enterprise Data Management: So where do you start?
- Metadata management provides the organization with the contextual information concerning its data assets. Without it, data governance essentially runs blind.
The value of metadata management is the ability to govern common and reference data used across the organization with cross-departmental standards and definitions, allowing data sharing and reuse, reducing data redundancy and storage, avoiding data errors due to incorrect choices or duplications, and supporting data quality and analytics capabilities.
- Your organization also needs to understand enterprise data architecture and enterprise data modeling. Without it, enterprise data governance will be hard to support
Enterprise data architecture supports data governance through concepts such as data movement, data transformation and data integration – since data governance develops policies and standards for these activities.
Data modeling, a vital component of data architecture, is also critical to data governance. By providing insights into the use cases satisfied by the data, organizations can do a better job of proactively analyzing the required shelf-life and better measure the risk/reward of keeping that data around.
Data stewards serve as SMEs in the development and refinement of data models and assist in the creation of data standards that are represented by data models. These artifacts allow your organization to achieve its business goals using enterprise data architecture.
- Let’s face it, most organizations implement data governance because they want high quality data. Enterprise data governance is foundational for the success of data quality management.
Data governance supports data quality efforts through the development of standard policies, practices, data standards, common definitions, etc. Data stewards implement these data standards and policies, supporting the data quality professionals.
These standards, policies, and practices lead to effective and sustainable data governance.
- Finally, without business intelligence (BI) and analytics, data governance will not add any value. The value of data governance to BI and analytics is the ability to govern data from its sources to destinations in warehouses/marts, define standards for data across those stages, and promote common algorithms and calculations where appropriate. These benefits allow the organization to achieve its business goals with BI and analytics.
Gaining an EDGE on the Competition
Old-school data governance is one-sided, mainly concerned with cataloging data to support search and discovery. The lack of short-term value here often caused executive support to dwindle, so the task of DG was siloed within IT.
These issues are circumvented by using the collaborative Data Governance 2.0 approach, spreading the responsibility of DG among those who use the data. This means that data assets are recorded with more context and are of greater use to an organization.
It also means executive-level employees are more aware of data governance working as they’re involved in it, as well as seeing the extra revenue potential in optimizing data analysis streams and the resulting improvements to times to market.
We refer to this enterprise-wide, collaborative, 2.0 take on data governance as the enterprise data governance experience (EDGE). But organizational collaboration aside, the real EDGE is arguably the collaboration it facilitates between solutions. The EDGE platform recognizes the fundamental reliance data governance has on the enterprise data management methodology suite and unifies them.
By existing on one platform, and sharing one repository, organizations can guarantee their data is uniform across the organization, regardless of department.
Additionally, it drastically improves workflows by allowing for real-time updates across the platform. For example, a change to a term in the data dictionary (data governance) will be automatically reflected in all connected data models (data modeling).
Further, the EDGE integrates enterprise architecture to define application capabilities and interdependencies within the context of their connection to enterprise strategy, enabling technology investments to be prioritized in line with business goals.
Business process also is included so enterprises can clearly define, map and analyze workflows and build models to drive process improvement, as well as identify business practices susceptible to the greatest security, compliance or other risks and where controls are most needed to mitigate exposures.
Essentially, it’s the approach data governance needs to become a value-adding strategic initiative instead of an isolated effort that peters out.
