by Nancy Couture

Assess before you leap

Apr 26, 2018
AnalyticsBusiness IntelligenceData Management

Organizations are ensuring their enterprise data management initiatives with proper data governance.

risk assessment - challenge - danger
Credit: Thinkstock

I came across this quote by Yogi Berra the other day, and it truly encapsulates the intent behind this article:

“If you don’t know where you are going, you’ll end up somewhere else.”

As organizations strive to continue responding to market changes and opportunities, there is an increasing need for solid operational and strategic information to better support the business. As a result, many organizations are embarking on enterprise data initiatives to modernize their capabilities. Before committing to or starting work on a large enterprise data management (EDM) initiative (typically a significant investment), organizations are increasingly recognizing the advantages of ensuring proper data governance is in place, or implemented along with these major initiatives.

Data governance is the exercise of decision-making and authority for data-related matters, ensuring a proactive approach to managing data in support of your business strategies and vision. Ultimately, the goal of data governance (DG) is to transform the organization itself – people, process and technology – to optimize the value of data on an ongoing basis.

In addition to increasing external pressure and opportunities, there are several additional reasons for data governance, including:

  • regulatory compliance
  • organizational growth that makes cross-functional decisions around data difficult
  • increasing complexity in the scope and types of data in the organization that may require management

Since every organization’s business intelligence and enterprise data management requirements are different, as well as their organizational goals, decision style and culture, the optimal mix of people, processes and technologies to govern data solutions may vary. So a discovery phase is a great way to start. 

The discovery phase can be a very short-term, low-cost effort to understand the current state of your organization’s data governance readiness and culture. Several key deliverables can be developed as part of this phase, including the recommended operating model (people, process, technology) to support data governance, a charter that describes the who/what/how of data governance for the organization, training & communication plans, and both tactical next steps as well as a longer term roadmap to provide a more strategic view of the program. As a result of this discovery phase, the organization will have the background and information to move ahead more quickly and effectively. 

Some key benefits for performing this initial discovery and sharing it with leadership across the organization:

  • Creates an understanding of current state, which may not be well understood across the organization (many leaders, especially in the business, may not be aware of the risks and opportunities that exist across the organization due to lack of policies and accountabilities, or poor data quality with no remediation in place)
  • Identifies potential risks and red flags so that mitigation plans can be defined (an example is ensuring that data is classified appropriately in any new development effort to ensure appropriate access and use)
  • Builds awareness and unilateral support, since both business and IT organizations are involved
  • Assists in the development of a larger strategic plan around enterprise data management (data governance can start with one initiative, then grow into an enterprise-wide capability)
  • Develops a realistic approach, options and estimates to manage expectations (organizations may move into data governance without understanding the scope of the need)
  • Results of a discovery phase can be acted upon quickly, or even concurrently (often, there are tools and processes that can be identified and developed very early in the discovery phase to enable a quick transition to implementation)

Many organizations have had data governance assessments done, either internally or by consultants. Some of these have been successfully followed with actual implementation of recommendations and road map. However, many have resulted in no active follow through, or unsuccessful fizzles. 

A statistic from Dataversity when they polled at a recent conference regarding data governance initiatives:

  • 14 percent were on their first attempt
  • 36 percent were on their second attempt
  • 23 percent were third or more times

A key success factor is for the executive support and funding to be in place before the discovery phase of a data governance initiative even begins. The expectation at the executive level should be that discovery and implementation meld at some point so that the organization moves right into implementing the data governance operating model without a pause. Ideally, data governance is embedded into the new development efforts.

Not as ideally, but frequently the case is that the discovery phase is needed to define the business value and obtain that executive support and funding in order to embark on a data governance capability. If this is the case, it’s important that the discovery results include recommendations and justifications that are compelling. Keep in mind the following:

  • Focus on how data governance delivers business value. What are the risks that are minimized, the savings in costs and productivity, and the visible improvements to the business? Make these quantitative wherever possible.
  • Align the data governance roadmap and priorities with the organization’s strategic objectives. This will resonate with senior leaders who are working to achieve these objectives.
  • Demonstrate in your recommendations how data governance can be embedded into existing work efforts and processes, without too much additional expense or additional resources.
  • Recommend an iterative data governance approach, starting with one initiative or with one data domain to develop a good foundation of people, process and technology, then leveraging lessons learned and continuous improvement to incorporate it into additional areas across the organization

By following these guidelines and recommendations, you and your organization can better define the roadmap ahead, have a plan for how to move along that roadmap, and identify where your ideal milestones are to improved enterprise data management.