Data governance has finally taken root within many organizations, been adopted internally through changes to cultural behaviors, mature processes, interactions and management skills. As organizations begin implementing data governance; many are wrestling with internal politics, naysayers, bad data, resource gaps, process, and skills gaps, and changing business priorities. Governance programs reach a natural plateau at about the two year mark. Initial successes can create complacency or worse overconfidence in an organization’s management of the data. Often the depth and breadth of the changes truly necessary to have a significant impact on the business have only been partially achieved at this stage, especially in large organizations.
Implementing data governance is a lesson in patience and fortitude, and requires a willingness to achieve success one small step at a time. Here are some of the worst terrible two types of organizational behaviors that actually impede the implementation process. Some of these well entrenched bad behaviors often get temporarily ignored as select groups adopt and develop their governance skills. This type of progress breeds confidence, sometimes too much so, which leads to the first focus area to watch: Overconfidence.
My company worked with one client and saw good progress overall in governance, especially for shared data, but as they sought to implement data governance in an operational area, they hit a roadblock. A strong business owner was then identified, but he felt already sufficiently entrenched in data governance. The problem was that his governance was primarily focused only on inbound data quality improvements, with no proactive stewardship (such as improving data standards, improving Meta data for lineage, and transparency). The result was that the downstream analytic environments were still receiving inconsistent data. In short, overconfidence in capabilities and the resulting lack of consistency was a major stumbling block for them.
The best way to avoid this problem is to have good, consistent, and required onboarding processes and follow up, and for management to establish simple non-punitive measures to ensure the new business unit has begun to adopt the common data governance processes, skills, language, etc. and to understand that data governance is more than just making sure that the quality of data coming into the organization is good.
The next focus area is Data Ownership.
We all know good data governance is predicated by good, business driven data ownership. In practical reality, it can often be difficult to identify a single correct data owner. So, it’s common to want to start with what you know, which is often who key stakeholders and stewards are. While it’s okay to start here, you can’t let this go on indefinitely. We saw another client make great progress with essentially stewards driving governance for key customer data, with the assumption that they had a data owner named. The problem was that the designated owner was not actively engaged in governing the data, and wasn’t completely convinced they were the correct long term owner.
The problem seems obvious from this vantage point, but in the middle of a data governance implementation, the simple decision of making some progress with stewards, without a strong committed business owner was tactically correct. In fact, one could say this shouldn’t be done, but it is often a way to help uncover a natural business data owner. No, the real issue here was not actively managing the ownership issue to ensure that a committed business owner was soon identified. Organizations must establish timelines and regular check points, and begin to measure the area being governed with key milestones like identifying owners to ensure progress is being made, and exposing the activities to the business community. Make it transparent. Keep senior management involved.
The third area to watch out for, is the presence of Purists, or as some call them, the data zealots.
The best way to understand this issue is to consider another example. In one large financial services company, they were struggling with their data governance implementation. It had been started a few years earlier and they thought they had done a great job getting things going. They had established a data governance committee and had common tools and processes. But, they were not gaining a lot of traction with the business or technology. Upon further examination, their problems stemmed from a few areas. They had ownership issues and lacked clear business understanding of business value (more on this later), but their biggest problem was Purism.
What was the issue? The team they had in place wanted everything done by the book. They would engage a business area, demand stewards and owners be identified, and dictate how quickly data standards should be developed. They were driving hard, but getting nowhere.
The organization lacked the skills, understanding, and business buy in to support their demands. Additionally, the purists wanted a level of detail that would require major resources to comply; projects and operational areas just cannot absorb these additions in one fell swoop.
So what can organizations do? Take a practical approach. Use an onboarding process that teaches the simple skills necessary to do the job. 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. Make business and I/T drive the workload and timing. There are a lot of good reasons for them to want to include data governance in their data management activities, so helping them understand this and directing them. If it’s forced on them, be prepared to lose them.
The fourth focus area: Business Value for middle management.
Interestingly, governance is well received at the 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. Management may hear things, like “my project dates can’t be missed, and I can’t take this on, or my team has never done it this way,” and the accompanying passive aggressive approach of acknowledging the needs, but doing nothing about it.
The business value of data governance is different at this level. Middle managers want to understand what is expected of them, and more importantly, how it will help them get their jobs done better and faster, and how it will improve data quality. A good data governance implementation will identify key integration points with existing processes and help the organization understand how to leverage them 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. Together organizations can improve their capabilities and increase adoption.
The final focus area is on Oversight.
While seemingly obvious, what needs to be overseen changes as the organization matures. For instance, early on it’s easy to measure simple onboarding. Over time the need to add Meta data and data quality metrics will arise. Companies will also be looking to assess data impacts in project oversight and in technology activities.
The biggest problem during the terrible twos is not recognizing the inflection points requiring changes in oversight. In fact it’s common to see governance teams at this stage reacting to the environment around them as issues or business priorities emerge.
To mitigate this, organizations need to be proactive, have a small team either dedicated or dynamic depending on scale and capacity that gets together regularly to assess the companies progress and identify ways to move the ball forward. Think of these teams as data governance support groups. They work at the request of the data governance steering committee and need to be kept informed as to progress toward maturity. They will help with onboarding, process development and improvement, metrics, etc.
The truth is these problems are normal and usually present from the early stages of data governance implementations. But at this awkward second year phase, organizations have to carefully and purposefully identify what is still acceptable and what is getting in their way. Be practical, set objectives and manage to them, but adjust them as needed with business guidance to ensure you keep key stakeholders engaged and focused on achieving the benefits of your data governance initiative.
About the Author
Paul Bergamo, General Partner/Practice Leader, NewVantage Partners
Paul Bergamo is a General Partner/Practice Leader with NewVantage Partners, a business and technology strategy firm specializing in data and analytics. Paul leads the firms practice in the areas of data governance, IT governance, and process architecture. He brings extensive experience in the health care and insurance arenas. Paul served as Chief Technology Officer for Liberty Mutual from 2000-2005. He was previously Chief Technology Officer for Aetna. Paul has been recognized by CIO Magazine as a Top 100 CTO. He is a frequent author and speaker on issues in data governance and IT effectiveness.