Achieving Enterprise Data Awareness is a natural maturity progression within the governance domain. At this stage, companies rightly see Data Governance and Information Governance as a large metadata puzzle. Governance implementations can be defined as a series of business rules (metadata) using terms such as system of record, subject area, Data Owners, and Data Stewards. Projects typically involve the collection of these business rules where (it?) lies hidden in a spreadsheet or other metadata repository. There are no short cuts (think metadata tools) to gaining awareness of your data. Solving the metadata puzzle will greatly enhance the ability of Data and Information Governance to capture business value, empower IT, and reduce organizational risk.
Operating with awareness is a hallmark of the Lean Thinking discipline. This takes us back to our days of working with manufacturing clients who had no clue about what was stored where. Our engagements were focused on the data aspects of ERP systems as the clients moved toward Lean Manufacturing in the hope of survival in a changing world. In past columns, we have discussed the art of materials management on the factory floor and how the same principles directly apply to data inventory. The goal in all cases is to have high confidence in the inventory, including classification, availability, quality, ownership, and use.
The concept of Lean Manufacturing is decades old, but is still in its infancy as it relates to Enterprise Data Management. The path to enlightenment starts with answering basic questions on data landscape, including:
- What is the governance relationship between a business and their data?
- Can you differentiate between your critical data lineage and the technical noise?
- Within the stewardship model, which department is accountable for key data?
- Where is all confidential data stored, and who has access to it?
- What is the relationship between structured and unstructured data?
- Why haven’t our governance tools delivered their promised results?
At Meta Governance, we utilize an Awareness Matrix which offers a visualization of the relationship between business and their data. This matrix is simply the intersection between a subject area of data (domain), an organization unit, and a governance role. To help organizations adapt to changing business needs this intersection is defined as metadata in a database. In previous columns we have discussed the power of this Awareness Matrix as it applied to Enterprise Risk Management and Operations.
Registered Governance Stakeholders is the next term in our arsenal to enable Enterprise Data Awareness. Any of the various governance roles such as owner and consumer of a subject area of data would be considered a stakeholder. Registration initially occurs when the department acknowledges their dependency on the data. Registration is finalized when there is an intersection in the database of this class of governance metadata. Mature governance implementations treat this registration as a formal process, something built into the corporate culture. This creates a lasting covenant between the owners and consumers of data.
Years ago, Data Sharing agreements were the norm. This was an outstanding idea to cement the relationship between producers and consumers of data. A formal contract included employee performance goals and targets. But alas, these controls were doomed to fail often because of the dynamic relationships in business that are impossible to track in a spreadsheet or procedure. Despite high expectations that these contracts would form the basis for an effective Data Governance communication plan expectations were rarely met.
The value of failure is what we can learn from it. Researching the experience of these Data Sharing agreements has led to insights into key aspects of data awareness. These folks were on the right path, but their implementation failed to leverage the power of metadata and relational database technology. Today, this technology creates the Awareness Matrix. The discipline of registering stakeholders creates a framework for an effective communication plan driven by metadata to target notification. Like any social media blitz, various forms of AI can be leveraged to really hone your message to reach specific stakeholder interests.
The last major component of awareness deals with data quality. Think minimum quality standards, the critical measurement in Lean Manufacturing which we now apply to data quality. The minimum quality is defined in the eyes of the consumer. Consumers in this case being Data Scientists, Risk Managers, and even External Auditors or Regulators. These people will help you set the minimum quality standards for your organization. Striving for a level of data quality that exceeds the minimum standards just consumes vast amounts of time, resources, and money. The Pareto effect, or the 80/20 rule, is alive and well in the world of Lean Thinking.
Imagine a world where awareness of the current state of data quality that is dynamically distributed to your Registered Governance Stakeholders is based on their actual business needs and quality expectations. This level of governance metadata integration is key to getting more from your hidden data assets, improving response time and accountability, and reducing operational risk. These are real results to be gained through Enterprise Data Awareness. In the next column, we will expand on integration data quality standards and approaches under the umbrella of Lean Governance.