Are You Looking For A New Information Governance Framework?
A few months ago, while addressing a room full of IT and business professional at an Information Governance conference, a CFO said – “… if we designed our systems today from scratch, they will look nothing like the environment we own.” He went on to elaborate that they arrived there by layering thousands of good and valid decisions on top of one another.
Similarly, Information Governance has also evolved out of the good work that was done by those who preceded us. These items evolve into something that only a few can envision today. Along the way, technology evolved and changed the way we interact with data to manage our daily tasks. What started as good engineering practices for mainframes gave way to data management.
Then, with technological advances, we encountered new problems, introduced new tasks and disciplines, and created Information Governance in the process. We were standing on the shoulders of data management, armed with new solutions to new problems. Now we face the four Vs of big data and each of those new data system characteristics have introduced a new set of challenges driving the need for Big Data Information Governance as a response to changing velocity, volume, veracity, and variety.
Before I answer this question, I must ask you “How comprehensive is the framework you are using today and how well does it scale to address the new challenges?”
While there are several frameworks out in the marketplace to choose from. In this blog, I will tell you what questions you need to ask yourself before replacing your old framework with a new one:
Q. Is it nimble?
The focus of data governance practices must allow for nimble responses to changes in technology, customer needs, and internal processes. The organization must be able to respond to emergent technology.
Q. Will it enable you to apply policies and regulations to data brought into the organization by a person or process?
- Public company: Meet the obligation to protect the investment of the shareholders and manage risk while creating value.
- Private company: Meet privacy laws even if financial regulations are not applicable.
- Fulfill the obligations of external regulations from international, national, regional, and local governments.
Q. How does it Manage quality?
For big data, the data must be fit for purpose; context might need to be hypothesized for evaluation. Quality does not imply cleansing activities, which might mask the results.
Q. Does it understanding your complete business and information flow?
Attribution and lineage are very important in big data. Knowing what is the source and what is the destination is crucial in validating analytics results as fit for purpose.
Q. How does it understanding the language that you use, and can the framework manage it actively to reduce ambiguity, redundancy, and inconsistency?
Big data might not have a logical data model, so any structured data should be mapped to the enterprise model. Big data still has context and thus modeling becomes increasingly important to creating knowledge and understanding. The definitions evolve over time and the enterprise must plan to manage the shifting meaning.
Q. Does it manage classification?
It is critical for the business/steward to classify the overall source and the contents within as soon as it is brought in by its owner to support of information lifecycle management, access control, and regulatory compliance.
Q. How does it protect data quality and access?
Your information protection must not be compromised for the sake of expediency, convenience, or deadlines. Protect not just what you bring in, but what you join/link it to, and what you derive. Your customers will fault you for failing to protect them from malicious links. The enterprise must formulate the strategy to deal with more data, longer retention periods, more data subject to experimentation, and less process around it, all while trying to derive more value over longer periods.
Q. Does it foster stewardship?
Ensuring the appropriate use and reuse of data requires the action of an employee. E.g., this role cannot be automated, and it requires the active involvement of a member of the business organization to serve as the steward over the data element or source.
Q. Does it manage long-term requirements?
Policies and standards are the mechanism by which management communicates their long-range business requirements. They are essential to an effective governance program.
Q. How does it manage feedback?
As a companion to policies and standards, an escalation and exception process enables communication throughout the organization when policies and standards conflict with new business requirements. It forms the core process to drive improvements to the policy and standard documents.
Q. Does it Foster innovation?
Governance must not squelch innovation. Governance can and should make accommodations for new ideas and growth. This is managed through management of the infrastructure environments as part of the architecture.
Q. How does it control third-party content?
Third-party data plays an expanding role in big data. There are three types and governance controls must be adequate for the circumstances. They must consider applicable regulations for the operating geographic regions; therefore, you must understand and manage those obligations.