Data Governance – A Better Approach to Value Creation
Governance – what is it? If you look it up, you would find multiple variations of what it is or could be or even should be. In most cases these definitions lend to that monolithic approach we are all so familiar (and frustrated) with. This boil-the-ocean approach typically includes everything from data architecture, modeling & design to consumption of the data. With these bigger efforts, Gartner has provided us insight that 60-85% of companies’ data-centric projects fail because of poor or absent data governance. It’s no wonder why so many organizations are demanding we find a new way to implement and operationalize a successful data governance program. One that generates value.
Great Data Minds believes a lean/agile approach to governance is the best operating model to move your data and analytic program forward. Take a continuous improvement approach. Identify the next most pressing problem you are trying to fix or risk you are trying to mitigate, build the right thing, and build the thing right. Execute with a product approach vs. the project approach. Governance considerations are an inherent part of the requirements for building data and analytic products. Since data continues to grow and the desire to utilize the data to enable analytics which provide intelligent information to the business continues to grow, a strong and pervasive governance mindset is a must. The key is to avoid creating monolithic organizational constructs and implementing invasive governance processes and workflows. Data and Analytic Governance is the responsibility of everyone in the organization from data producers to data curators to data consumers.
Utilizing building blocks to define your data governance touchpoints is a must to identify where that governance will contribute to the value of the data and analytic products over their entire useful life-cycle.
In the lean-agile mindset, there is the need for ensuring the “architecture runway” stays ahead of the scheduling of delivering new data and analytic products. What enabling technologies are needed to further the value generation from data and analytic governance across the organization? Technology to help move your data program forward.
Policies & Procedures:
Avoid building a siloed corpus of governance policies and procedures. Instead ensure the continuous ideation, definition, development, validation, integration, deployment, monitoring, refinement and disposal of data and analytic products include the necessary governance considerations as part of that product life-cycle. These procedures are intentionally integrated into the Agile Product Delivery core competency.
Oversight & Metrics:
Every Data and Analytic Product that goes through the Agile Product Delivery life-cycle has an intentional design around the creation of execution, usage and outcome telemetry. This telemetry data serves the purpose of enabling the creation of governance metrics which facilitate the continuous oversight over the creation and utilization of the data products and analytics products are in accordance with their intended uses. When the metrics indicate shift or deviance from expectations, those data and analytic products will be triggered for governance review.
Meta Data / Lineage:
Starting with critical data elements (CDE, define your data elements, come up with consistent nomenclature, document, utilize and adjust as you go. This will create your data glossary. When possible, identify and define the lineage of your data, this will allow for opportunities to upgrade, deprecate as you become more mature in your data program. Continue to review your data pipeline and add information to allow teams to better understand where the data is coming from, how it is being used and update this content into your data catalog. It’s not everything, but it is the critical things.
Data Security, Usage, Ethics:
Data security, usage and ethics are a component in your data strategy that cannot be forgotten. It is imperative that as part of your data program you understand how the data is being used, by whom and in what content. Note who should be using things and understand where all of these PII type data exists. With the ongoing regulations in the US and abroad, knowing your data is a corporate responsibility. This is a means of protecting your company and your clients/customers.
Data & Analytic Outcomes:
Keep a pulse on how people are feeling about the data they are receiving and compare to the prior temperature before the program was put into place. Determine what is trying to be solved and measure against that objective. Other things to consider is how well the data and analytics (BI & ML) products are generating the expected business value.
Incorporating defined data quality requirements for those critical data elements and implementing data quality business rules interrogations and providing visibility to the ongoing trends of data quality business rule adherence. Overall assuring that the quality of the data is constantly being managed as part of the data pipeline.
The Application of Governance:
Consider that you need to govern with an appropriately sized hammer, balancing the organization’s need to continuously innovate (try new things out, learn fast, pivot or preserve) against the organization’s need to protect the data and analytic products as valued assets. Organizations have an obligation to their stakeholders to protect their interests, be in compliance with the ever changing regulatory landscape and continue to govern with appropriate rigor to ensure continuous regulatory compliance, organizational reputation protection and ensuring the respect of the people and markets they serve.