How do you measure and invest smartly in the value case of your data assets?

Building a business case for data governance is vital to secure buy-in of key stakeholders and begin to create and recover value.

Nowadays, companies have at their disposal a myriad of advanced information disciplines that could support their journey to becoming a data-driven organization, as well as increase value creation for their businesses (e.g. data mining, big data analytics, artificial intelligence, Internet of Things). However, to leverage these benefits, good quality data must be aligned and delivered as required for the above data initiatives to succeed. In this way, data governance becomes an essential enabler for the success of any business endeavor.

In our view, the main challenges consist in the data debt generated by organizations through years of data mismanagement, negligence of IT infrastructure, unclear data ownership, and a myriad of confusing analytics solutions. To deal with these challenges and complexity, tomorrow’s data leaders need a clear methodology to triage and invest in the right data initiatives. In this context, the ability to foresee and deliver on the organizational and financial promise becomes more critical than ever before.

The business case for data governance

Organizations often struggle to create a business case for data governance and management. Successful implementation relies on an enterprise-wide transformational change in mindset and culture and this, in turn, requires commitment from the organization’s leadership. A sound business case secures the full support of CEOs, board members and senior executives.

Let’s explore why data governance is so important with an example. Imagine building an innovative finance forecasting model based on data from different systems. You’ve invested heavily in the extract, transform and load (ETL) jobs, a data lake/data warehouse, data models, and business intelligence solutions. You’re expecting this to be a transformative project. But if the quality of the data is poor, the results generated by the finance forecasting model won’t be reliable. If you have not defined data owners that can explain the source of data and different quality incongruences, your forecasting model will not be scalable and reliable – or create value for your organization.

Even if you do succeed in creating value through data, how do you measure it? The characteristics of data as an asset are quite different than those of our traditional tangible assets like cash, people, inventory. Data can be copied easily; however, does not deplete by being used or stored. In this sense, ascribing a value to it by applying traditional asset valuation approaches prescribed by data management standards is still an abstract exercise that leaves room for interpretation and does not provide a clear path forward. So, how do organizations measure the value of data today?

• Replacement value: cost of restoring all the data you have in case you lost it
• Operational value: cost of storage and processing of your data
• Industry value: cost your competitor is ready to pay for it
• Commercial value: value lost by the organization if data is lost

All of these fall short of showing tangible benefits and, thus, seldom support approval decisions for funding data management. As opposed to the usual “better data, better decisions, better performances” unsubstantiated promises, a good data governance business case can ensure the full commitment of the organization’s leadership, which in turn will determine its success.

With the value recovery approach, we can start with the leading question: what value debt occurs in your organization due to bad data management? Here you can either choose a data-centric (e.g., customer, supplier domains) or process-centric approach (e.g., purchase-to-pay, order-to-cash). The analysis in each case leads you to the lost value. In other words, how you recover value by reducing the leakage due to bad data management. Three guiding questions can be used as a starting point to select the business processes in scope:

• Do they drive your key financial incomes such as sales, profit and working capital?
• Are they responsible for money in/out of your company, placing them at the center of audit scrutiny?
• Are they data-intensive and do they offer the right level of detail for traceability of root causes?

After aligning the business processes in scope, the theory of constraints can be applied to identify and scope the main pain areas. Examples include inappropriate use of master data while pricing; duplicate invoices; and an abundance of credit memos due to incorrect use of master data. The aim is to end up with different buckets with an assigned monetary value, enabling a focused approach to address the biggest losses. The advantage of this approach is quantifying the negative impact of bad data on your business. However, it comes at a price; this approach can become very cost-intensive depending on an organization maturity with regard to data analytics.  

The value creation approach, on the other hand, is concerned with the organization’s ability to fully derive value from its data assets. This is about asking: how can I unlock value from my data assets? Companies need to implement the right data governance and management behaviors. This means that they are measurable, consistent in approach and supported by the right technological and people capabilities.

This can be achieved by breaking down different data governance dimensions (e.g., data quality, data accessibility, data representativity, data security) into leading metrics, work processes and capabilities.

The chart below shows how an organization can quantitively analyze the value of data quality in the customer domain. The process can equally be applied to other domains and governance pillars.

The objective of this exercise is to continuously derive the status quo of your organization’s data governance and select the top three priorities based on the ease of implementation (dependent on the organization’s maturity, people skills and available technology) as well as impact (how many data governance dimensions are enabled and potential generated value). This approach is rather qualitative in nature as it derives its criteria from the characteristics of an ideal data-driven organization. However, it still offers its benefits:

• Consistency: a common language across the organization
• Repeatable: used repeatedly to reach outcomes in a similar manner
• Scalable: agile approach to go fast from pilot to roll-out
• Autonomous: able to become a self-service fully owned by the organization over time
• Value-driven: offering tangible business cases for improvement and innovation
• Transformative: improving data governance capabilities towards a data-driven enterprise

Ultimately, having more concrete information about the value of data – beyond the mere cost aspect – supports the business case for data governance and management. Is your company ready to unlock value from your data assets and reduce leakage from poor data management?