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Making the most of retail analytics

124579-Consumer__Spri_selected-pages.pngIn retail, there’s no shortage of data. The challenge isn’t finding data, but gleaning actionable information that will increase profitability. Using information to increase operational efficiency has been a goal for years, but the new frontier is a better understanding of the consumer for improved delivery on the brand promise.

 

While the focus on operational efficiency remains, there are new opportunities to use Big Data analytics to monetize retail data. In order to better understand these opportunities, we recently spoke with Cindy J. Rogers, Principal Consultant, Retail Industry Analytics and Data Management, Strategy Services for HP. 

Maximize Business Value of Information

Maximize Value.jpgBy Mike Mansur

Worldwide Competency Lead for HP Global Methods for Business Intelligence

 

In the past few blogs of this series, we have looked at key success factors of information strategy and the importance of collaboration. In this edition, I want to focus on using a corporate information management (IM) strategy not just to drive measurable business value, but to maximize that value.

Information Management & Analytics in the Energy industry

So, I work out of my home office which faces our backyard with a lot of greenery and shrubs – something refreshing to look at and rejuvenate ourselves during a work day.  However, once in a while, my eyes do glance down at the meter next to the air-conditioning unit that tracks the energy consumption in our household on a regular basis. In the past, the technician from the utility company would pay a monthly visit to get the readings. However, this is not the case anymore. You see – our electric company is now smarter – they have smart meters which send data at a much higher frequency than man ever collected such information. However, is our electric company geared up to handle this onslaught of data? I wonder.

It is a big world for big data after all

In the Information Week Global CIO blog, Patrick Houston says that big is bad when it comes to data, questioning the appropriateness of the term big data. Houston highlights the risk of the term being taken literally by the not-so-technical folks. Big data will continue to spread with emerging associative terms like big data expert, big data technologies, etc. I also see other reactions to this term like the one in this post from Allison Watterson on What do you mean big data, little data is hard enough. Why has big data gained this broad adoption so fast?

What do you mean big data, little data is hard enough?

IM&A.jpgLately there is a lot of talk about “big data.” So I decided to attend a session at HP Discover called “Harnessing Big Data” to learn what this is all about. 

 

I quickly learned that there are four main dimensions of big data:

  • Volume
  • Velocity
  • Variety
  • Complexity
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