Digital Governance: It’s a Numbers Game (Metrics, KPIs, and ROI, that is)

If your organization is on a journey of digital transformation you need to intentionally and proactively manage, control and exploit the value of your information assets through optimized digital processes. 

The way to do this is through a structured mechanism for making decisions which include decision-making bodies and rules. 

When people think of data governance, they picture it as a group of people going into a room and making decisions about the information and how it is going to be managed, typically with a compliance goal in mind.  

The problem with that vision is it doesn't take into account that the world has evolved. Addressing how information is managed for compliance is only part of the picture. The bigger picture is making sure that information is being governed to a purpose that aligns with business objectives.

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Going beyond compliance

“Digital governance” is much more nuanced than getting people together to talk about how data is managed.

Governance is a mechanism for creating change, and decisions about those changes need to be driven by metrics.

Metrics indicate whether objectives are being achieved, and they can provide a path back to processes and content that may or may not be meeting customer needs. If they are not, then interventions can be launched to change the content or the way in which it is being delivered, or to improve processes.

Taking the organization’s governance initiatives beyond compliance defines digital governance.

At the top level is an enterprise strategy such as increasing revenue, reducing costs, excelling at customer service or bringing innovative solutions to market. Business unit objectives should align with this strategy, and business processes should support and enable those objectives. Underpinning the processes are the fuel of the business – the data and digital assets that create value for customers. The key is to determine what data, content or assets are needed to support each process, such as launching a campaign, creating proposals, providing customer support or designing new products. Compliance with data regulations is an outcome, and is one factor in making these decisions, but should not be the main driver of an overall governance strategy.

Triggering decisions with KPIs

What is the trigger for decisions that a governance council might make? If the situation is that a new campaign is being launched for a particular segment or new audience, there are a cascade of issues that have to be thought through.  A governance working group tasked with creating the machinery of this campaign may require new terminology, data sources, content structures, or campaign assets in order to correctly manage the experience associated with this launch.  The full lifecycle of assets needs to be considered from cradle to grave – from creation to disposition as records that may need to be maintained based on regulatory requirements.

Metrics can be obtained at different levels, from data measures to content quality, process performance, and business objective metrics. These form a thread among the levels, which indicates how the information assets are performing. Data and content should be supporting business objectives; however the way to determine if all the machinery is working effectively, the organization needs baseline measures and KPI’s for processes, and decision-making triggers based on those KPI’s. 

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Getting the right people discussing the right issues

Building this kind of decision making machinery achieves two objectives. First, it ensures that the correct people are at the table.  Business leaders care less about data and more about the results that data is enabling. The processes that should be measured are the ones that are the most important to stakeholders. Business users need to identify those initiatives and then the owners of those initiatives need to understand how digital tools will support them.

"When governance processes are designed correctly, a chain of trust relationships is established throughout the organization..."

The second objective in setting up governance and decision-making in this way is to create an effective process for remediation.  When governance processes are designed correctly, a chain of trust relationships is established throughout the organization that allows for troubleshooting when there are lapses in data quality, or when a function or process is not performing as expected.  Establishing these mechanisms is not trivial, but once they are established, the same processes are repeated for projects large and small. When measures are out of alignment, this triggers examination of the problem and an initiative (from a small fix to a major overhaul) is launched.

Focus on the customer

A framework for all of this is the customer lifecycle. Basing governance and metrics on what is important to the customer grounds this approach in reality.  The lifecycle covers all the processes that serve the customer: from awareness through conversion and long-term engagement, including recommending the company to others. Customers learn about the company through outbound and inbound marketing, and there is a whole set of metrics around just that function. At each stage, the company needs to understand how the customer is interacting, and this is done by taking measurements.

For example, when developing marketing content for customer segments, the message needs to be framed differently to present the appropriate value proposition.  For a building supply retailer, there may be different value propositions for contractors versus do-it-yourselfers. Different tools are used at different stages of the lifecycle and they are tuned using content that articulates the brand promise for that target. The goal of increasing awareness may be achieved via email marketing, SEO, onsite search, personalization or a combination of all of the above. Each tool will impact a particular stage of the lifecycle and generate data that is indicative of the process performance. 

The stakeholders who care about those metrics will be the ones involved in decisions about data, content, architecture, quality, remediation, and so on.  Since the decisions directly impact their ability to deliver on the process that they own, they will pay attention to those decisions. (Of course governance structures need to be designed so that the correct level of decision is made by the correct level of the organization. Strategy decisions are not made at the tactical level and vice versa.)

Data and tool savvy business users

Business users need an understanding of what tools are used at each stage of the lifecycle and the inputs that will be needed.  They also need to be savvy about the metrics that these tools provide.  In the marketing stack, there are metrics at multiple levels of granularity.  The question to always ask is “So what?”  What does the metric mean and what should the owner do when the value is high or low?  What is the measure telling the organization to do? 

Building out the dashboards linked to decision making is at the heart of metrics driven governance.

The number of visitors, conversions, purchases, and bounces along with click paths each provide insights about the user’s “digital body language.”  Metrics-based governance is about reading that language and triggering the appropriate interventions.

Measurement is what determines whether the customer is succeeding at achieving their goals or not. If the customer drops off the site, metrics will be an indicator of whither the content is not right and the customer is not understanding it, whether they are having a problem with navigation or whether there is some other issue.

Interventions and feedback mechanisms

The results of these analyses lead to a determination of what interventions are needed, and those interventions feedback to the decision-making process. If the strategy is to increase awareness, then the impact of outbound email, the call center, or other actions can provide clues. Understanding the mechanics of the tools and how user behaviors can be influenced requires a level of fluency that can be difficult to gain given the rapid changes in customer experience technology. The choice of technology should always be guided by the engagement strategy at each step of the journey, which in turn will guide the analytics for each process and help identify what is important about each objective.

Imagine that customers coming to the site can’t find what they need.  It may be that the company is not segmenting audiences properly, or not using terminology aligned with their objectives.  This will ultimately impact conversion. One lever to pull might be how the taxonomy is designed and how navigation is presented.  Or customers may be navigating to a certain point and then not making selections.  This outcome could result from how the product attributes and characteristics are presented. 

Many organizations have designed effective taxonomies but allowed them to drift over time or have not controlled the changes and evolution that are necessary over time.  When this occurs, there are multiple downstream impacts to the customer experience. To realize their full value, multiple design elements, data elements, content and the taxonomies themselves need to be evaluated on a regular basis.  The digital ecosystem is dynamic, requiring ongoing evaluation and tuning in order to perform effectively and meet the changing customer needs and business objectives.

The governance groups make decisions about whether certain changes are appropriate, but this is not necessarily a simple process. First they must kick off the small projects that will determine the validity of the new requirement, or whether fundamental organizing principles need to be changed. This in turn leads to working sessions to discuss use cases, determining what can and should be measured, and then defining the right intervention.

In short, there is a lot of structure and process to jump through, but that’s reality. It’s important to slow down, see what you are doing, assess the strategy engagement process, evaluate the available tools and see how they can be managed to target the conversions on the website. If the desired outcome does not take place, then change something—perhaps the navigational strategy, or the way in which content is surfaced. Measurements need to be taken again, and a new cycle through the decision-making process initiated to see if further change is needed.

Aircover: the CDO and the CMO partnership

The digital ecosystem is complex, with many changing elements.  Customer experience is where data meets user engagement technology.  In prior eras, the processes and departments that supported customers lived in silos and contributed to a fragmented customer experience.   Customer expectations have raised the bar, and increasingly, decisions need to be made at the leadership level.  This process is more complex than a single C executive can handle, which is why the CIO’s job is being segmented into multiple areas of responsibility.  The data ecosystem itself is so complex that it requires the attention of a specialist – the Chief Data Officer (CDO).  Marketing technology is requiring a Chief Marketing Officer (CMO) who is also technically savvy.  Increasingly the C suite needs to collaborate across major functional responsibilities to create an optimal digital experience.

The CMO and CDO should work together to design metrics and decision-making processes so that the right things are measured. The CMO is the person to articulate metrics for customer experience outcomes, and ensure that the correct instrumentation is implemented to harvest data streams as input to metrics dashboards. The CDO or  equivalent is responsible for ensuring that the correct, high quality data sources are aligned to the appropriate processes.

Governance initiatives always need leadership and sponsorship at the most senior levels of the organization.  Designing, building and implementing metrics-driven governance based on the customer lifecycle requires collaboration at the C suite and a productive relationship between the business and technology organizations.

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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.