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The New Definition of ROI: Return on Information

ROI knobBig data technologies exist to extract value from a huge jumble of information in a cost-effective way. Creating readily understood graphs and charts out of enormous piles of numbers taken from big data is how we find trends in the business, as well as develop strategies to address those trends and maximize profit margins. There is much to gain and much to lose in how well we manage this, and according to June Manley, the benefits we derive from big data are a new type of ROI—return on information, which she breaks down into three components:

  1. Insight
  2. Time-to-value
  3. Total cost

To start out, the first component demands being able to see the big picture:

Insight is the value extracted from all types of information. The value of that insight is derived from how much data that an organization analyzes, how deeply it analyzes that data and how many people benefit from the analytics. Would your analysts prefer to make decisions based on analyzing100 percent of relevant data, or make “gut calls” based on a thin slice of data? Insight is about analyzing 100 percent of relevant data and ensuring that everybody understands what should drive critical enterprise decisions. It’s a matter of skimming the surface rather than truly understanding what your data is telling you.

Time-to-value meanwhile is how long it takes to get insight into the hands of the people who most need it. If relevant insight is provided after an important decision has already been made, then the insight is useless and becomes just another case of squandered resources. The time spent to send information should be shorter than the time spent to use it. Total cost then comprises capital and operational expenditures for storing, managing, and analyzing data. Since you will pay for all the data you choose to store whether or not it is being used right now, you might as well actively monitor costs to better understand the data’s full value.

Manley believes return on information will become the defining measure of success within the next five years. Learning to make optimal use of all the numbers spit out by big data could mean the difference between being the leader of the pack and being left in the dust.

About John Friscia

John Friscia is the Editor of Computer Aid's Accelerating IT Success. He began working for Computer Aid, Inc. in 2013 and continues to provide graphic design support for AITS. He graduated summa cum laude from Shippensburg University with a B.A. in English.

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