Four Starting Questions for Supporting Big Data Analytics

Loraine Lawson
Slide Show

Big Data: Not Just for Big Business Anymore

Big Data analytics is still an emerging area for organizations, so it’s not surprising that a lot of ambiguity arises around the best way to support the strategic use of data.

The discussion about how to drive adoption has mostly focused on the pros and cons of hiring a chief data officer. But after reading a McKinsey Quarterly analysis, it occurs to me that is probably putting the cart before the horse.

How to Mobilize Your C-Suite for Big Data Analytics,” which was reprinted recently on Information Management, walks us through a number of real-world Big Data analytics deployments. The examples show that how you go about leading Big Data analytics projects is actually more important than who leads them.


Still. That doesn’t change the fact that someone does have to lead these initiatives, and, as the report notes, that person should not be someone in “middle management.” Big Data analytics will require a hefty investment, and full support from senior executives.

The article explains how different organizations have approached adopting Big Data analytics. Some failed, some succeeded.

It turned out that what mattered was not whether you hire a CDO or create a center of excellence or assign the job to an existing leader. What made the difference was organizing in a way that fostered collaboration and buy-in from business leaders and users.

The report includes four excellent questions to ask as a better starting point when deciding how to structurally support Big Data analytics:

  1. Will a central customer or operational database be used across business units?
  2. Is there a compelling reason to build your own analytics tools and develop internal resources, rather than using a vendor solution? Hint: It depends on how transformative you believe analytics will be to your business.
  3. Can the current functional leader for each business unit manage the change or should the company dedicate new executive capacity specifically for the data-analytics change effort?
  4. If you decide you’d like a more targeted start, ask “Where could data analytics deliver quantum leaps in performance?” to pinpoint where to focus your efforts.

“Like any new business opportunity, data analytics will under-deliver on its potential without a clear strategy and well-articulated initiatives and benchmarks for success,” the report warns.

The full piece outlines six “top-team tasks” behind successful data analytics project. It’s a more studied approach, and should be helpful for any effort to shift to a data-driven culture.



Add Comment      Leave a comment on this blog post
Nov 20, 2013 2:23 PM CyberH CyberH  says:
Loraine, good insight. With the explosion of big data, companies are faced with data challenges in three different areas. First, you know the type of results you want from your data but it’s computationally difficult to obtain. Second, you know the questions to ask but struggle with the answers and need to do data mining to help find those answers. And third is in the area of data exploration where you need to reveal the unknowns and look through the data for patterns and hidden relationships. The open source HPCC Systems big data processing platform can help companies with these challenges by deriving insights from massive data sets quick and simple. Designed by data scientists, it is a complete integrated solution from data ingestion and data processing to data delivery. Their built-in Machine Learning Library and Matrix processing algorithms can assist with business intelligence and predictive analytics. More at http://hpccsystems.com Reply

Post a comment

 

 

 

 


(Maximum characters: 1200). You have 1200 characters left.

 

 

Subscribe to our Newsletters

Sign up now and get the best business technology insights direct to your inbox.


 
Resource centers

Business Intelligence

Business performance information for strategic and operational decision-making

SOA

SOA uses interoperable services grouped around business processes to ease data integration

Data Warehousing

Data warehousing helps companies make sense of their operational data