Click to learn more about author Avi Kalderon.
Some businesses are swimming through their “lake” of big data. Some feel like they are drowning. The difference, often, is the ability to actively adapt data governance and data management capabilities right from the start. Here are five ways data governance can make big data manageable and empower business to make smarter, faster business decisions.
- Who’s in Charge — and Who Owns it?
The key big data stakeholders/decision makers need to be recognized upfront. Roles may change as the data moves through your ecosystem and during its lifecycle, but they should be well understood, nonetheless. As you embark on your big data initiatives, identify these stakeholders as soon as you can, and be prepared to refine and iterate as you go.
Meanwhile, data is being generated by individuals, but is owned by the firm. Businesses need to actively managing the ownership issue to ensure that a committed business owner is soon identified. Organizations must establish timelines and regular check points, and begin to measure the area being governed with key milestones.
- Getting Data Aboard and Downstream
Too often, data governance is primarily focused only on inbound data quality improvements, resulting in downstream analytic environments that still receive inconsistent data. Avoid this problem with good onboarding processes and follow-up to ensure data is properly cataloged and easily discoverable. Establish simple, easy to follow methods to ensure your users are part of the governance process and contribute their knowledge to the effort. Data governance is more than a small group of people tasked with making sure that the quality of data coming into the organization is good. It’s an enterprise effort where many can contribute.
- Smart Adapting to Big Data
Data governance and data management processes should adapt to support the needs of big data users and big data technology. This becomes especially true as your organization starts looking at new data types whether they are semi-structured or completely unstructured. Defining, classifying and incorporating them into your data dictionary is more complex.
Apply adaptive governance processes that can support various degrees of rigor and oversight to match. One way is to ensure your reference information architecture is updated to support big data concepts such as unstructured data streams and that everyone is trained on these new additions. Another is to make sure that metadata management capabilities are enhanced to include/correlate all of the basic metadata components as well as support rich data types in the form of ‘tagging’.
- Hurdling the Middle-Management Wall
Governance is increasingly well received at senior management levels, but middle management is almost always skeptical. They have real deliverables, with hard dates to meet, and limited resources. They are often the cause of slow adoption and maturity. Return on investment is often hard to define for any governance activities at the enterprise.
The business value of data governance is different at this level. A good data governance implementation will identify key integration points with existing processes and strategic initiatives that have a well-defined value proposition and help the organization understand how to leverage well managed data to their advantage. Use early adopters as data champions. Get the word out as to what works, and even what doesn’t work. Help make the business value clear.
- Get Data-Practical
Too many struggles of implementing data governance stem from purism – when the assembled team wants everything done by the book, but the organization lacks the time and buy-in to support their demands and still show positive business results. These purists also often want a level of detail that requires major resources to comply; projects and operational areas just cannot absorb these additions in one fell swoop and funding often becomes a stumbling block.
The answer is to be practical. Use an onboarding process that teaches the simple skills necessary to do the job and concentrate only on data elements that matter. Help the business and I/T resources learn how to make governance work as a natural extension of their current work. Prioritize what needs to be done and at what pace. Taking a value-driven incremental approach to data governance will help you calibrate your program to the pace your organization can handle it while showing gradual improvements that build momentum.