Having attended, sponsored, exhibited and spoken at the DGIQ (Data Governance and Information Quality) 2016 Conference in San Diego, CA, our BackOffice Associates team engaged in the latest and greatest discussions in the world of data governance and data quality. Taking a glance at attendees, there was a wide variety of industries and company sizes represented, with executives looking to gain the competitive advantage of enforcing and setting an effective data governance solution. Conference topics reflected the mindset of many attendees based on topics such as, “How to get started with Data Governance” to “Governing data in the cloud.” There was quite a span in the levels of data governance maturity and expertise.
With the overwhelming variation of size and expertise in data governance, is there really a significant difference between small and mid-market data governance initiatives compared to those at the enterprise data level?
Data Governance Big and Small
When choosing the right data governance strategy, it is important to understand where your organization stands in the data journey, whether a small program or an enterprise-level deployment. In the end, it comes down to understanding the needs of your organization, the people involved in the decision-making process and of course budget.
At DGIQ, I met all types of industry representatives, titles, lines of businesses and more. Reflecting on those conversations, it dawned on me that many people and organizations were very interested in data governance, but many didn’t know what it actually meant, where they should start, how mature they currently are, or even how mature they want to be. While these findings may not be fully accurate to every organization, overall these were the most common realities when engaging with anyone from either an enterprise organization (over $1 billion yearly revenue) or a smaller mid-market organization.
Small to mid-market organizations typically start with defining their business terms and using a business glossary, and maybe some policy and metadata management thrown in there. Overall most organizations felt very comfortable that defining and managing their business terms and policies made them very mature from a governance standpoint.
Larger enterprises were concerned with policy enforcement through application and/or master data management and data quality. Solving immediate problems related to the creation and maintenance of their daily business operations data were the real drivers of their initiatives, as well as gaining more business value from governance.
Both are highly related to data governance and data stewardship yet their approaches and priorities are very different. This information begged me to question, why? Is it the price point of the vendors, is it vendor target customer analysis leading towards different marketing initiatives or is it market analyst recommendations?
Data Governance Learnings
Although they have their commonalities and differences, the market sizes can learn a great deal from one another. Larger markets should be more focused on the big picture within data governance and leverage the governance best practices and approaches that smaller markets with less budget and resources are able to implement. They should have a greater initiative to define and organize their business terms and policies in an agnostic area away from their actual systems of record and processing. Enterprises could use these concepts to be able to reuse ideas and bodies of work across projects (new system rollouts, sunsets, migrations, interfaces, archiving, mergers, acquisitions and divestitures). Thus increasing the business value of their initiatives and becoming a more mature organization.
Based on my experience in the enterprise realm, pharma/life sciences, oil and gas, chemicals and retail, I see many data governance practices that can be learned from smaller organizations.
On the flip side, while engaging with the smaller and mid-market data stewards, it seems that the definition of data governance is different to them. In the fewest words possible, governance is the establishment of policies and continuous monitoring of their proper implementation.
While enterprise organizations establish those policies in siloes through project efforts in various applications designed to solve a particular problem or provide a specific need, smaller organizations focus their efforts on defining and establishing standards and policies and the actual “continual monitoring of those (policies) to ensure their proper adherence” is more of a secondary goal. Perhaps it’s too expensive and the market has yet to provide an affordable means to an end here. However, in my conversations at and even before DGIQ, it was clear that the policy enforcement side of governance was not something on their roadmap and after discussing it with them, the value is obvious.
As a small to mid-market organization that can identify with this, ask yourself, are you getting all of the potential business value from the efforts in defining and organizing your terms, policies and standards? If you had a method for enforcing them and ensuring that everyone in your organization was following these policies across all data related projects and data architecture could you get that value? Small to mid-market organizations can learn a lot from the approach of larger enterprise organizations.
No matter the size of the company and their data set, everyone starts somewhere with individual goals per department or company. It’s a combination of goals and business processes that determines how you operate and optimize as one entity. Numbers may play an important role in determining your approach and maturity level of data governance as well as the amount of business value that you can achieve. At the end of the day, the overarching goals are the same—and although the approach is different, the two can learn a lot from the standard approaches of the other.
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