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Addressing Barriers to Success – Technology

By Richard Sharpe | 11/24/2015 | 4:59 AM | Categories: Web/Tech

This is the third posting of a series addressing the barriers to success in gaining sustainable value from analytics and big data.  The series centers around the primary pillars for having an effective solution: people, process and technology.  This posting is on technology.

There are a variety of reasons that technology can be a handicap when trying to apply analytics to provide meaningful business insights.  I have witnessed numerous examples for a variety of companies over the years.  But today we are going to focus on four areas that are pervasive today:


Lack of organizational confidence in the data:  when I was running CAPS Logistics we served over 16% of the Fortune 500 companies by supporting their network optimization and transportation and routing & scheduling needs.  Unfortunately, about half of the improvement opportunities identified through the use of technology would not be fully implemented because someone in the organization would take exception to the data that was used for the analysis.  Talk about a lack of ROI

Takeaway: To get value from analytics and big data, you must invest in a process to ensure that all organizational objections related to the data are addressed.


Analytical results that are not intuitive:  to be most effective, analytical applications must be designed for business users, not data scientists.  This means that the analytics are focused on a business problem defined by the business user and that the results are intuitive and meaningful for that user. 

Takeaway: Needing someone else to run the analytics or to interpret the outcome of the analysis places a real handicap to value.


Difficult to repeat:  we are all aware of the speed of change in the world we live in today.  The days are gone when a company can rely on the outcome of specific analytical work to remain valid for an extended period of time.  Today, the mode of operation is to refine plans by making incremental (“course corrections”) changes versus a large analytical study done every two years.

Takeaway: Analytics must be easily performed on an ongoing basis and must have the flexibility to be changed based on early efforts and insights.


Hard to measure financial impact:  to maintain senior level support of your analytics and big data initiatives, they have to deliver value.   Naturally, there are some initiatives that have intangible benefits but typical senior-level sponsorship is based on a plan demonstrating that the investment will provide measurable financial improvements.   

Unfortunately, many people don’t take the time to carefully consider how they are going to measure financial results until they are well into the initiative.  Do not fall into that trap.  First consider how you are going to baseline current performance.  Once defined, tie the anticipated areas of improvement back to the baseline for defensible measurements. 

Takeaway: Careful planning on how you will demonstrate value needs to be part of any analytics and big data initiative.    


The effective use of analytics and big data can drive significant competitive advantage.  Companies that undertake their initiatives by placing the proper amount of priority on people, process and technology will be the winners. 

I would love to hear your thoughts.

All the best,




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