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Big Data: The Unknown Unknowns

Bruno Aziza Profile Pic
Posted by Bruno Aziza
June 22, 2017


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Industry leaders know that their challenges with data analytics are spread across 4 areas: confirmation (‘the things they know they know’); intuition (‘the things they don’t know they know’); inspection (‘the things they know they don’t know’); and revelation (‘the things they don’t know they don’t know). 

This last category is often the most tragic for organizations.  Luckily, we now have helpful resources from Tom Davenport's latest webinar on Data Strategy to key research data points on chief data executive's priorities.

You don't know what you don't know, until it's too late...

You've heard it from some of the largest companies at last week's DataWorks event: Industries are made and reinvented through Big Data.  If your company and leaders haven't embraced Big Data Analytics yet, you might be behind.   It's not too late though! Take a look at the 5 min video below to get a sense of the latest industry trends.  

Interested in more? Download a copy of the latest Big Data Maturity Survey here!

What you see is not always what you get...

As we revealed in this week's webinar with Tom Davenport of Harvard Business Review and Jim Tyo, CDO at Nationwide, 47% of executives believe their firms are at risk of major disruption in the next decade.  Which is probably why 58% of them claim they have invested over $100M in Big Data technology to anticipate the disruption (Source: NewVantage Partners' latest survey).

Are you getting value out of your investments so far? What are the steps you should take in order to put your team on the path to success?  Take a few minutes to hear what Tom and Jim have to say!

Interested in getting started earlier?  Download our Big Data "Cheat Sheet" today here!

Ay, Ay, Ay...you're not ready for AI...

Everyone is talking about Machine Learning (ML), Artificial Intelligence (AI) these days. But, as our customers Home Depot, Liberty Mutual, Canadian Tire and others proved last week at DataWorks, companies can rarely be successful with ML and AI if they don't have a solid analytics foundation.  This Harvard Business Review article (If Your Company Isn’t Good at Analytics, It’s Not Ready for AI) provides a great checklist for success.   Ask your team the following 3 questions:

  • Do you have automated processes in problem areas that cost significant money and slow down operations?
  • Do you have centralized data processes so that the way data is collected is standardized? (for more on this, read: Do You Need A [New] Data Strategy?)
  • Do you have a centralized way to control definitions and metrics while providing business users the freedom they need to innovate with data?  (for more on this, read: What is a “Semantic Layer” & Why Would You Want One?)

Finally, if you're feeling zealous, you might want to check out this blog on the "6 principles of Modern Data Architecture".  This blog is inspired from the work of one of our customers and it should come in handy!

 

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