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A lot of focus has been placed on advanced and predictive analytics, and rightfully so. I have written many posts and have spoken publically on the merits of advanced analytics for several years now.

 

What I find disorienting and misleading are marketers harping on how important it is to adopt advanced analytics right now. The thing that they just don’t get (or maybe they don’t want to get?) is that an organization will need to transcend a series of analytical maturity levels before they can truly capitalize on the benefits of advanced and predictive analytics.


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Source: MIT Sloan Management Review

In my last post, I introduced the longitudinal study that MIT Sloan Management Review has been conducting over the past five years. From 2010 to 2012 they indicated that 67% of those surveyed believed that analytics gave their organizations a competitive edge. In 2013, that figure stabilized at 66% revealing the so called ‘Moneyball Effect’ where leaders lost their competitive edge that they once enjoyed because followers matured and made analytics core competencies. In 2014, that trend continued, falling to 61%.

 

But why?


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By now, most of you have probably heard of, or read, the book entitled Competing on Analytics by Thomas Davenport that demonstrates how some of the most successful organizations in the world have made analytics a core capability and integral to their strategic planning. MIT Sloan has been tracking this phenomenon since 2010 echoing Davenport’s findings. From 2010 to 2012 they indicated that 67% of those surveyed believed that analytics gave their organizations a competitive edge. However, in their last installment of their longitudinal study, something interesting happened. Something that I like to call the ‘Oakland Athletics Effect’.

 

Findings from the annual MIT analytics study


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In 2013, Gartner conducted a survey on Big Data Adoption in Supply Chain Industries and found that adoption has been flat and is lagging behind the overall adoption rate of other industries such as banking, insurance, and retail to name a few. Gartner ascertained that these characteristics pertaining to the Supply Chain industry are attributable to an inherent lack of understanding of what Big Data truly is and a fundamental lack of the required skill sets. This, in essence, is the challenge facing the Supply Chain industry.

 

In parts one, two and three of this four part series on Big Data, we looked at what makes data “big”, how it can benefit organizations that apply the right analytics, and the implications of doing so, respectively. In the closing segment of this series, we focus our attention on examples of how elements of Big Data can be leveraged. The challenges identified by Gartner translate to opportunities regardless of your organization’s analytical maturity level.

 


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Big data and big data analytics pose a series of implications and challenges.  Organizations that seek to become analytical competitors must have an established analytics culture consisting of  well trained employees who are using the right enabling technologies.  However, these organizations face challenges maintaining consumer privacy while they collect and use sensitive information.

 

In parts one and two of this four part series on big data, we looked at what makes data ‘big’ and how it can benefit organizations that apply the right analytics.  In part three, we will look at the cultural, technological, and ethical implications of big data and advanced analytics.


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Big data is all around us.  As we have seen, big data is characterized by its volume, velocity, and variety (the infamous three ‘V’s).  Great, you have a lot of data…now what?  Well, these untapped ’dark data assets’ give rise to vast opportunities for those organizations that seek new ways to compete.  Studies have shown that organizations that compete on analytics by focusing on their core competencies fare much better than those who do not.  Some have gone so far as to call big data the ‘new oil’.  In part two of this four part series, we will take a closer look at big data analytics.

 
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With the advent of big data, organizations are beginning to recognize the impact that big data and analytics can have on their ability to compete in their respective industries.  In a recent study by MIT and the SAS Institute, 67% of leading organizations firmly believe that analytics give them a competitive advantage.  This recognition has revealed that it is not only about the volume, velocity and variety of the data at hand, but having the right culture, skillsets, and technologies in place, while respecting the privacy of consumers.  This post will be the first of a four part series aimed at demystifying the term ‘big data’, and touching on opportunities, implications and challenges related to big data.


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In my July post, I introduced the ‘Hierarchy of Supply Chain Metrics’, which is a framework of supply chain metrics conceived by Gartner, the world’s leading information research and advisory company.  The model provides 3 tiers of integrated metrics to assess, diagnose, and correct supply chain performance, and is a great example of what constititutes a supply chain scorecard.

 


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It has been said that you can’t manage what you can’t measure.  In the world of analytics, this is our daily mantra.  However, as succinct as the  statement may be, you can’t help wondering if it is missing something;   a little something called perspective.

 


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Since 2010, Gartner, the world’s leading information technology research and advisory company, has been publishing an annual report entitled ‘Gartner Supply Chain Top 25‘ which ranks organizations that demonstrate leadership in supply chain management.  In each of these reports, the ‘Hierarchy of Supply Chain Metrics’ is positioned as the ideal set of metrics to measure supply chain operational performance.  To emphasize Gartner’s stance, the subtitle reads: ‘The Metrics We Wish We Had’.


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