If you are looking for two words within organizations that inspire all the excitement and joy of going to the dentist, search no further than “data governance.” Business leaders, and more than a few analytic leaders, view this discipline as simply a tax to the system, an overhead and administrative burden associated with doing business and managing data. Something to be endured but surely not something to be counted upon for material improvement in the business. Yet, despite its lack of appeal, data still reigns supreme in terms of organizational value.
Data comprises between 20-25% of an organization’s enterprise value on average, placing data among the most important asset classes across companies of every type. Despite this figure, data still does not get the respect it deserves. With regards to maintenance, management, and quality controls, data is not treated the same as other assets, from which companies expect to generate a return. Investments in technology infrastructure, inventory, or property, plant, and equipment (PP&E) are considered revenue generating precursors, and decisions related to the acquisition and management of these assets are prioritized based on the potential business value each investment will yield. When it comes to data and the ongoing governance of the asset, activities are viewed as expenses – not investments – and focus is given to processes, frameworks, and remediation instead of prioritizing efforts (i.e., investments) around value driving use cases. Is it time to recast data governance as a data monetization strategy?
Data governance (DG) has long been executed with the nuance and subtlety of a sledgehammer. The creation of a DG organization, and its subsequent implementation, has typically meant asking for significant business input (data stewards and data domain team members) and then introducing to them an infinite supply of data fields to define and classify. Executing on this monolithic DG vision meant slowly creating a road to nowhere for the business. The majority of DG efforts are fading away with business leaders asking themselves: When will all of this “governance” end? When do we see a return on investment for our time? How does this connect to my business objectives?
Data Monetization vs. Data Governance
Data monetization and DG are generally viewed as two independent disciplines driving separate objectives. In this context, data monetization is not just about packaging your data and/or insights to create an external revenue stream. It is also about quantifying the value of data and/or insights for an organization even if the data is just used for internal consumption. If DG is supposed to increase the value of an organization’s data asset, then it should be about monetizing data rather than just about defining policies, procedures and standards to improve data management and data quality.
Most DG initiatives are launched to better manage and maintain the vast amounts of data every organization has collected. Such initiatives take on a very tactical approach through documentation of data definitions or data lifecycle. While important, those approaches do not always provide the much-needed quick business value.
Data governance done correctly is hyper-focused on value but rarely framed in this manner. Companies nearly always begin their journey by boiling the ocean with non-value-added activities that leave little time to actually solve anything.
Data governance is, above all else, an enabler to achieve your business outcomes and should always be oriented in this manner.
Where to Start?
DG organizations first need to prove their value. The best DG organizations are consistently identifying and prioritizing data management issues with the highest business value. Common roadblocks range from lack of customer data to conduct effective digital marketing to call center processes that don’t consistently collect the necessary data. Bring oversight and leadership from your DG organization to meet challenges head on. Data issues that involve system changes, process redesign, and change management must be appropriately managed by others. DG must lead the way and consistently seek to increase the value of an organization’s data. This is the purpose of a DG organization. Doing so naturally aligns the vision and activities of DG with the business leaders they serve. For many, this is revelatory. The act of not worrying about every data element, especially those which are causing no harm or data management issues, is cathartic. It also creates the capacity to focus and become action oriented.
Taking the Right Steps
One of the critical activities required for successful data governance is to identify and assemble key leaders who will champion the data governance charter. Over time, other stakeholders will start to disengage because they don’t see a clear purpose in the overall initiative and start to question the value of their time and roles. Stem this risk with buy-in from the top.
Work top-down instead of bottom-up. Find critical value-driving use cases. Focus on accelerating business growth as your primary objective, and identify the most valuable data asset that can help achieve that growth, validate your assumptions, define success criteria and measure success. If you can’t quantify the increase in growth or increase in data value, re-think, and dig deeper.
Remind your team and stakeholders what’s in it for them every single time. A proven approach to getting data governance off the ground is to determine which drivers and opportunities can be enabled by increasing the value of data. Define the overall vision of data governance around increasing the value of data. All other tactical activities related to better data management are byproducts of the broader vision. Define stakeholder roles as the governance committee that’s accountable for educating their teams and executing this vision. Align this vision to overall organization strategy and their respective performance metrics. Remember, these are not data governance goals, but business growth goals.
For DG leaders, the hardest part is to think outside of just the data itself.
We all understand that data definitions, data lineage, and data policies are squarely within our responsibility, but recasting DG as “Unlocking Data Value” changes the optics. Business leaders are the experts in their respective departments but often look at data issues as challenges to work around – not through. Doing so often reduces the effectiveness of that department either through additional cost via workarounds or missed opportunities without the necessary insight or piece of data. Break through this mindset by becoming a cheerleader of value. Strive to work closely with your IT, operations, change, and business leaders to instill a cultural focus on how the correct data will reduce costs, increase revenue, or improve compliance. Enabling these business outcomes will ensure that the DG program is never viewed as unnecessary bureaucracy – but rather an important strategic enabler.