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Episode  Summary:

In today’s episode, you hear from Doug Laney best-selling author and recognized authority on data and analytics strategy. Doug’s book, Infonomics: How to Monetize, Manage, and Measure Information for Competitive Advantage, was selected by CIO Magazine as the “Must-Read Book of the Year” and one of the “Top 5 Books for Business Leaders and Tech Save Innovators.”

Top 3 Value Bombs:

  1. You can’t manage what you don’t measure”, companies should be measuring the quality characteristics of their data, the economic values it provides, and how complete and accurate it is.
  2. Organizations should be sharing more data across business units,
  3. The quickest way to get your data scientists, to find another job is to make him or her curate and harvest data. There should be a team or person in charge of curating /procuring the data.

You’ll Learn:

5 Tenants to Start Monetizing your Data:

Throughout the conversation with Doug Laney, there were 5 main themes that are needed to start monetizing your data:   

  1. Strong Vision and Executive Buy-In: Organizations should be thinking beyond the data’s operational and reporting context. Most companies do not have a long-term strategy for how they will generate additional revenue and gain more value from their data.  Your data architectures should not be seen as your cost center but rather as a profit center. But to achieve that vision you must have executive buy-in that understands the importance of and potential for expanding the data assets of the organization. 
  2. Ability to Measure and Track Data: “You can’t manage what you don’t measure”, companies should be measuring the quality characteristics of their data, the economic values it provides, and how complete and accurate it is. Not only do you need to be able to catalog all your data and know what exists across the organization but you need to understand what data is driving the most value. Not all data is created equal. 
  3. High-Quality Data: Before you can start to monetize your data you need to have strong data governance, quality, integration, completeness, and accuracy to build on top of. Without clean trustworthy data, your ability to execute your vision will most likely fail. 
  4. Capable Architecture: While there is no “ONE” architecture, it does need to be flexible and scalable to meet your goals and vision. Monetizing your data is an iterative process, build out the processes and data pipelines that provide the most value first. 
  5. Sharing of Data Assets: Organizations should be sharing data across business units for examples:
    1. Incorporate customer support data into manufacturing
    2. Incorporate manufacturing data better in the sales function
    3. In addition to sharing data among internal departments, organizations should be looking for opportunities to share/expose/barter with customers, partners, or suppliers. Many organizations do not think beyond their immediate ecosystem of partners, suppliers, customers but should gain a competitive advantage. 

The quickest way to get your data scientists, to find another job is to make him or her curate and harvest data.

There should be a team or person in charge of curating /procuring the data. Most companies have a procurement department, right? They are procuring office supplies, office furniture or raw materials of some kind. But do companies have anybody focused on procuring data? No. If your leadership is truly on board and understands the value of data you should have someone focused on procuring data. There are trillions of websites, social media posts, public government data sets, data brokers, and commercially aggregated data.

The biggest hurdle companies face when trying to monetize their data is, giving it away for free.

If you are already providing data to suppliers, partners, or other organizations for free then you’re on the right track, but maybe you should/could be monetizing some of the data. You could provide more premium levels of insights, analytics, and predictions. This could in turn allow you to invest more in the architecture and snowball into providing even better data insights. 

Resources:

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