On a recent flight home after some successful discussions on data-driven customer experience, I was reminiscing about my own personal non-linear career journey —moving from customer relationship management to business intelligence and data governance and back again to customer experience and automated decisioning — but always focusing on the benefits of using data to transform. Those musings (which included a small pat on the back) went something like this:

It has been a long haul. Thirty plus years of hand-holding, cajoling, teaching and shouting from the soapbox about the power of data. But technology capacity has finally caught up to the vision taught to me by my initial mentor (in the mid-1980s). He was the founder of a company that developed the first truly cognitive name and address matching software and his vision was that future data and technology breakthroughs would allow us to interact with our customers as if we really “knew” them. I felt both excitement and pride at this. Excitement because that day is clearly today. Pride because I believe I have succeeded in pushing the boulder at least a little way up the hill towards helping companies understand and act on this vision.  

Then reality set in. 

Climbing Data's Mount Everest

I read an NVP report on big data with a forward by Thomas Davenport and Randy Bean and found this sentence, “Most companies are still not data-driven and will not be anytime soon.” Yikes. The hill is bigger than I imagined.

Despite the fact that 92% of the survey respondents said they were increasing investment in big data and AI, the report contained several alarming statistics that could signal potential failure for some of these investments:

  • Only 31% have a data-driven organization, and these numbers are going in the wrong direction, moving downward from 37.1% and 32.4% feeling they had data-driven organizations in 2017 and 2018 respectively.
  • 72% of survey participants report they have yet to forge a data culture.
  • 53% state they are not yet treating data as a business asset.
  • 52% admit they are not competing on data and analytics.

Actually, I am not all that surprised at these numbers. I know the hill is big. Years of data governance consulting engagements resulted in a list of gotchas that I still use today when talking about data governance and management:

  • Often the culture doesn’t support centralized decision making.
  • Business considers data to be an IT issue.
  • Business sees data governance as an academic exercise.
  • Only part of the problem is tackled — business and IT do not work together.
  • The ROI for the data asset is not clear and participants can’t link data to business value.
  • Key resources are already overloaded and can’t take on governance related activity.

These gotchas are not technology based — but rather organizational and cultural. Which are much bigger issues to solve — because while deep pockets can throw money at technology problems and sometimes succeed, money alone cannot change culture. 

The NVP report underscores this point quite well. They say that executives cite multiple impeding factors — 95% of which appear attributable to cultural and organizational issues rather than technology challenges. The issues sited by their respondents parallel my go-to data governance challenge list almost exactly:

  • Lack of organizational alignment: 40.3%
  • Cultural resistance: 26.3%
  • Understanding data as an asset: 13.9%
  • Executive leadership: 7.9%
  • Technology: 3%

Related Article: Data-Driven Decisions Need Context

Building a Data-Driven Culture

Fortunately, there are ways to overcome these challenges and start building a data-driven culture.

Align Initiatives (Both Data and Analytics) With Corporate Strategies 

Where strategy goes, data and analytics must follow. Demonstrating ROI, elevating data governance beyond academia, associating data to business value — these can all be accomplished by ensuring that initiatives have a clear tie to corporate strategy and objectives. Strategy maps are a great way to begin. They contain four related components that get progressively more detailed: vision, key strategies, enabling initiatives and required information. 

Learning Opportunities

  • Vision identifies what the company aspires to be. 
  • Key strategies explore the key focus areas that must be addressed to achieve the vision and describe expected business outcomes. 
  • Enabling initiatives highlight data and analytics related projects needed to achieve desired business outcomes and designate key business stakeholders. 
  • Required information identifies the data and analytics capabilities necessary to complete the initiative.

Building a strategy map and prioritizing data and analytics efforts from the results can go a long way towards meeting the alignment and cultural resistance issues identified in the NVP report. It associates data with value, lays out a plan for implementing data and analytics capabilities that is blessed by business leaders, and it gets leadership on board for data and analytics efforts.

Related Article: Is Your Company Data-Driven or Data-Informed?            

Shore Up Your Data Governance and Management Programs

Where data goes, governance must follow. Otherwise, quality degrades, silos proliferate, data consolidation and preparation time explodes and chaos reigns. A large integrated health care provider-insurer once told me, “We are the largest user of disk storage in the world.” This was a dubious distinction at best. For them, data was not treated as an asset — but rather as a necessary but poorly treated afterthought.

To have a data-driven culture, data must be thought of as an asset — and must have all the same processes and procedures surrounding it as do the other corporate assets. In their book "Customer Data Integration," Jill Dyché and Evan Levy coined what I consider to be the gold standard definition for data governance: “the organizing framework for establishing strategy, objectives, and policies for corporate data.” Data governance programs that follow the tenets set out in this definition will establish business stakeholders as information owners and position enterprise data issues as cross-functional — both of which are critical to resolve data-oriented problems. 

Equally important is data management, the operational complement to data governance. Data management teams develop and implement the detailed block and tackle processes needed to carry out governance-defined corporate data policies. Primary focus areas typically include data discovery, metadata management, data quality and data architecture — all critical to any data intensive initiative such as customer experience or regulatory compliance. 

Data governance and management programs can ensure you have the right mix of business and technology leaders at the table and will put together the basic building blocks needed to treat data as an asset and establish and sustain a data-driven culture.   

Final thoughts? Pick a starting point, however small, and run with it. Because when it comes to data-driven activity, success can beget success. Forbes Insights found that benefits from data-driven customer experience can actually help alleviate organizational and cultural issues, while also developing an analytic culture. Faster decision making, greater confidence in decisions (and analytics) and better collaboration between departments were a few of these benefits.

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