Data governance – Proving value

How exactly does data governance make a difference?

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In this day and age, companies should now know that data governance is an imperative if you leverage data for any type of analytics. However, I still run into organizations that hold back budgeting for data governance, looking for specific, expected business value justification.

Any data governance capability should be based on the objective of optimizing the value of data.

What can you do with your organization’s data with data governance in place that you cannot do or cannot do as well without?  How does data governance make a difference? 

One of the challenges is that data governance needs to be coupled with other capabilities to actually realize the value. For example:

  • Implementing metadata management with a data governance framework will allow increased visibility into what data is available, where it’s located, and what it means. It allows organizations to put some order to the chaos of data today
  • Data quality management along with a data governance framework will result in improved data quality and also increased confidence in the data and any resulting analytics outcomes

According to Gartner, data is valued at 20-25% of enterprise value. Studies have identified countless opportunities to leverage data to improve your business. So, developing a business case for data governance is certainly possible. It just takes a thoughtful approach.

Data can support what I call the three universal executive drivers to:

  • Increase revenue. This can be done by having a focus on continuing innovation and performance. Some examples include improved business cycle times or service levels, increased customer retention, improved customer engagement.
  • Manage costs. Costs can be decreased or even avoided. Some examples include improved resource and process efficiencies, digital instead of manual processes, automation, simplification, enhanced analytics capabilities.
  • Manage risk. Ensure survival through attention to risk and vulnerabilities, e.g. compliance, security and privacy. Identify the risks your organization faces by not doing data governance. Some examples include regulatory and data privacy fines, risk of bad decisions, loss of competitive position.

While identifying the business value of a data governance program, it’s important to include both qualitative and quantitative value.

So how do you go about it?

Research

Research industry related opportunities, KPIs and financial news. Review competitor activities and ensure a strong understanding of your customer. Understand the types of analytics that are currently being done in your industry to identify and take advantage of opportunities. Relate these to the key strategic goals of the organization to identify the highest priority opportunities that require data.

Interview

Interview key decision makers and subject matter experts to validate and/or identify the highest priority opportunities, based on the organization’s strategic goals. Discuss the current state of the data needed for each of these opportunities. Is it usable as is?  Is it documented and well defined?  Does it have a high level of quality, e.g. validity, accuracy, completeness, etc. Hone in on the data that’s most critical to address the identified opportunities. Most likely, you will discover that there are a variety of data quality issues that would impact a successful result.

Document the opportunities

List each of the opportunities and collect pertinent information.

Opportunity description

  • Business challenge it addresses
  • Solution approach
  • Data needed
  • Current data challenges
  • Impact of data challenges
  • Level of effort to address data challenges
  • Impact if data were readily available with good quality

Some typical example opportunities that are relevant across most organizations:

Opportunity description: Email marketing optimization

  • Business challenge: improve Digital Marketing presence
  • Solution approach: understand how to approach contact frequency and prioritization scores

Opportunity description: Identify cross-sell opportunities

  • Business challenge: customer optimization
  • Solution approach: develop a cross-sell model to determine what additional products a customer may be interested in

Add information about what data is needed and what data challenges there may be that impact the ability to take advantage of these opportunities. By collecting this type of information, you can then proceed to make informed decisions on which opportunities to quantify to support the highest priority data challenges that will resonate with leadership.

Assess the opportunities

For each opportunity, develop a prioritization based on a Hi/Lo assessment. Which opportunities have the highest strategic value with the lowest work effort to address?  As part of this prioritization, identify and assess the data and the data management capabilities that would be needed to be successful.

For example, an opportunity identified above is to optimize email marketing by understanding ideal contact frequencies, developing prioritization scores, and identifying appropriate triggers. This opportunity would require specific data and resources to perform data analytics and modeling.

  • What’s the current state of that data?  
  • Would data quality management improve the validity of the data and improve any outcomes?
  • Can the modeling and analytics be done readily, or does it require a lot of searching for the correct sources of data?
  • Would readily available business and technical metadata help to improve your organization’s ability to leverage the data for modeling and analytics?

Define scope

Once a subset of opportunities has been defined and prioritized, the scope of a data governance program can be identified. Often, data governance programs start with:

  • Metadata management – business and technical metadata allow users to more readily find and understand what data is available.
  • Data quality management – can result in a higher confidence in the data being leveraged to ensure that analytic models are correct, and opportunities are realized.
  • Policy management – provides a way for organizations to standardize their approach to data use, promote consistency and reduce risk of using data inappropriately.

Regulatory compliance can also be a starting point, since data governance can provide a framework for ensuring that enterprise data is managed in a way that supports data privacy and other regulatory requirements associated with data.

Identify quantitative and qualitative benefits

The results of the research, interviews, identification and assessment of opportunities and scope can lead to the identification of benefits.

Sample qualitative benefits include:

  • Improved decision making. Well governed data is more discoverable, making it easier to find useful insights. It also means that decisions will be based on higher quality data, enabling greater accuracy.
  • Enhanced data quality.
  • Reduction in reporting and analysis errors.
  • Increased business competitiveness.

Sample quantitative benefits include:

  • Reduction in time spent by knowledge workers in finding and acquiring information.
  • Elimination of redundant hours spent across knowledge workers looking for the same or similar data.
  • Reduction in rework and rationalization due to poor data quality.
  • Enhanced productivity, since data is well understood and more readily leveraged.
  • Reduction or elimination of fines as data is better managed to support regulatory compliance.
  • ROI associated with specific analytics initiatives can be quantified, based on the assumption that improved data availability and quality support these.
  • Elimination of impacts due to financial restatements.

Finally, pull together your business value case, making sure you take the following into account:

  • Ensure a proposal and a scope that readily aligns to key strategic goals of the organization.
  • Include qualitative benefits that resonate with your audience. Generic benefits are much less effective. Don’t hesitate to add in a key example or two of data quality horror stories.
  • Include quantitative benefits that are conservative enough to be believable. Often, we’re tempted to provide very large numbers that may be difficult for executives to believe.
  • Align the benefits to either risk reduction, revenue generation or cost management.

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