Data Economy


Economics studies a market’s supply and demand curves, trying to understand the changes to each curve and its overall effect on the market.  If we borrow from that concept, we are constantly moving to a new economy with an ever-growing supply of data being created each day.  What is a little less known is all the different activities that can occur on the demand curve within the data economy?  One thing is certain: businesses need to evolve to be able to have an advantage in this new economy.  They are all moving to learn how to effectively utilize the data they have internally, along with the data that exists outside their organization that has value for their business.  In this post we will explore three key areas I believe are important for organizations to understand in this new economy: Data’s growth, analytics’ effect on business, and preparing to take advantage.  So, grab a cup of coffee and let’s talk about the new economy.

Data’s Growth

I have recently heard on many occasions the new buzz of “data is the new oil”, which is trying to simplify so many analogies about its value and how it needs to be “refined” to create even more value.  But as I hear the phrase more and more, one part of the analogy doesn’t work. Oil had a large boom, but it is not a renewable, growing resource.  This is opposite to data, which is growing each day with every keystroke, tweet, website visit, Alexa request, and the list goes on.  Each new device or application just expands the amount of data that exists in the market.  Tools and technologies have even been created to handle the ever-expanding problem. Whole areas of the data tool ecosystem have bloomed, creating big data, data lakes, data streaming, and more. These technologies are being used by more organizations to harness the volumes of data that continue to increase. This growth and the tools that allow for its use have also led to a large untapped potential source of value for businesses. The following image depicts how a retail bank traditionally uses data vs. the data it could be using through the establishment of a data lake.

data lakes

The combining of all these different internal and external data sources opens up many more opportunities for an organization to produce analysis and use it for many different business decisions and functions.

Analytics Effect on Business

As the example illustrates for the Retail Bank industry above, the effect of data and its analysis is having dramatic impact on how businesses operate their core business practices, with dramatic changes in the sales, marketing, and R&D operations. With data about customers’ habits and experiences, marketing and sales teams can effectively target customers, generate more potential leads, retain the existing customers and understand behaviors that can lead to potential cross-sell opportunities. These new capabilities are paralleled in the R&D business function, where data can be used to model and predict potential outcomes along with creating more efficiencies in the process. One such example that we have experienced is an engine oil manufacturer using previous engine test data to model how a new oil formula would perform in an engine test without having to execute the test. This allowed for results to be created faster along with saving cost of over $25,000 dollars because an engine did not have to be rebuilt and laid out on the test harness. Analytics is affecting multiple industries and impacting them in many different ways, as shown by the McKinsey Research below:

data and analytics

Preparing to Take Advantage

So, how can an organization like yours start or continue to prepare itself to analyze and use data to create advantages in this new economy? There is no silver bullet that allows an organization to go from zero to ready. Our experience and industry research suggest that the biggest challenge is aligning leadership within an organization so it can improve its maturity with data. To explore that further, the top 3 hurdles organizations have from an industry survey are[1]:

  • Ensuring senior management involvement (42%)
  • Designing an appropriate organizational structure to support data and analytics (45%)
  • Creating effective architecture and infrastructure (36%)

Being aware of these obstacles is helpful but should be combined with an understanding of where your organization is when it comes to data and analytics maturity. Getting to the bottom of this understanding requires a cross-functional view of the organization to understand how data is used by the business and maintained by the information technology department. Getting ready or creating more value from the new data economy may look like a daunting task but can be accomplished by any organization.

If you would like to learn more about how we work with organizations to help them find opportunities to create value out of their data, take a look at these following case studies:

Additionally, reach out to me on LinkedIn here, I am always willing to spend time talking about challenges and potential approaches. Everyone should be able to unlock the hidden potential they have in their organization data.

[1] McKinsey Global Institute: The Age of Analytics: Competing in a Data-Driven World

About The Author

Josh Bartels is UDig's Chief Technology Officer. He has been leading data and consulting engagements for over 10 years. Josh believes bridging the gap between business and technology departments in any organization is key to generating success and staying competitive.