Four Critical Elements of Metrics-Driven Information Governance

The importance of information governance is recognized by most organizations, both for its operational value and its role in supporting such functions as compliance and risk management. Success depends on having programs in place for assessing the effectiveness of their governance programs in achieving their strategic goals. Here are four critical steps of a metrics-driven information governance program:

  1. Picking the right metrics
  2. Mining user behavior through search analytics
  3. Integrating data sources
  4. Promoting culture change


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Picking the Right Metrics

The best way to decide which metrics matter is to define what success would look like. This varies depending on the organization’s mission. Success might be:

  • for a customer to buy a product, or reach a certain dollar value of purchases;
  • a point along the way, like asking for more information or signing up for a rewards card;
  • or, customers’ interactions with web content, the catalog, and the product pages, if the goal is to drive new leads.

The goal will determine the performance metrics and guide the selection of analytics.

Once success is defined, the next step is to look at the digital traces. There are events that can be measured and tracked within the organization’s information systems. The beauty of digital content that it is possible to watch much of the customer’s journey through an application or a website, from search to purchase to how often the customer calls for help. Does the “electronic body language” of the customer indicate that they wanted to buy a particular product? Are people drilling down and getting more information and generating indicators of intent to purchase? Quality metrics are the key to implementing metric-driven data governance.

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Mining User Behavior through Search Analytics

One typical source of metrics are searches conducted by users on a company’s website. These are a prolific source of data that can be used to provide input to how data should be structured and organized to maximize its value to customers. Examples of search metrics include:

  • number of results for particular queries,
  • number of queries that are producing no results,
  • results where users do not click on anything but instead execute another query,
  • coverage of search terms within a taxonomy, and
  • various types of relevancy metrics.

Judicious use of analytics technology can provide significant insights and a quick return on investment.

Since relevancy is in the eye of the beholder, these tests should be run on search results with specific users and use cases in mind. Search metrics provide additional data points for governance teams when reviewing the performance of web content and architectural constructs. 

Data helps settle disagreements about how to govern information, overriding opinions that turn out not to be justified.

Integrating Data Sources

Metrics-driven governance programs become the point of information integration across numerous systems.  Average order size is harvested from transaction systems, but to correlate average order size with customer segment requires data from CRM and customer master files.  Being able to correlate average order size with customer segment for a particular campaign and web site offer requires integration across marketing management systems and web analytics in addition to CRM, transaction, and customer master data. IT policies and procedures for integration should align with data governance programs.

Increasingly the customer’s success depends not only on content, but also on data from various back end systems as well as interaction with the customer service organization.  The customer may need to access account history, submit trouble tickets, pay bills online, look for maintenance information, connect with other users, voice frustrations or positive experiences, research multiple options, and complete a transaction using multiple devices.

IT, marketing, line of business, analytics, social media, information architecture, and user experience teams are increasingly being organized under a digital experience department or function.  Metrics-driven governance processes are driving working agendas through dashboards that integrate multiple data and analytics sources and various levels of granularity for different audiences and purposes. Implementing the right technology to support these initiatives is essential. 

Promoting Culture Change

One of the biggest challenges with any enterprise innovation is culture change, and doing so for a data-driven information governance program is no exception. Each participant needs to understand how the governance process is relevant to their job and the organization’s objectives. Employees who do not see governance as being part of their job, and focus solely on their primary roles such as sales or customer relationships, will not be motivated to take an active role in completing tasks such as filling in data about a product.

The need for change management should be anticipated and included as part of the emerging data governance framework.

Many different types of actions are required to maintain a metrics-driven governance system. Although much of the data needed for assessments is collected automatically by enterprise systems, some of the data that is required in order to obtain metrics must be entered by employees. The best strategy is to engage the stakeholders early in the process so they are on board with all the behavioral changes that will be required. However, if people have not bought into the process, then a change management project might be required to develop appropriate training, socialization and communications programs.

An organization establishing or evolving governance should evaluate various governance models to determine what will work best for their culture and processes. A democratic model gives everyone a vote but making decisions can be difficult. Multi-domain models with an enterprise-wide steering committee help drive strategy but can be stifling. A gatekeeper model uses a single decision-making body that is responsible for all decisions and offers consistency, but can produce bottlenecks. Whatever model is used should provide accountability and transparency so that key stakeholders understand the standards, purpose, and policies of the new initiative.

These and other models each offer their own advantages and drawbacks; sometimes hybrid models that use components of each are the best for a particular organization.  There are multiple roles and stakeholder types in each of these models.  Some people are involved producing metrics, and others are consumers of the reports. Each organization needs to structure its governance in a way that allows for the flow of metrics to the correct level of decision maker in order to catalyze action and achieve the desired business results.

The philosophy of metrics-driven governance can be applied to any part of the business where value is generated by facilitating a cross-functional collaboration focused on specific, measurable outcomes. In each part of the business, however, people need to understand the value of governance and the processes required to make it work.

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Meet the Author
Seth Earley

Seth Earley is the Founder & CEO of Earley Information Science and the author of the award winning book The AI-Powered Enterprise: Harness the Power of Ontologies to Make Your Business Smarter, Faster, and More Profitable. An expert with 20+ years experience in Knowledge Strategy, Data and Information Architecture, Search-based Applications and Information Findability solutions. He has worked with a diverse roster of Fortune 1000 companies helping them to achieve higher levels of operating performance.