C-level executives at Fortune 1000 firms said that reducing time to insight was a more important driver than saving costs when it came to making big data business investments, according to a survey conducted by NewVantage Partners and published in January 2016. The survey report also states that organizations felt a need to learn quickly and act faster, with 83.5% of survey respondents naming factors related to speed, insight, and business agility as the primary reasons for big data investment.

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How to achieve business value through big data

Companies are looking for hard dollar gains in the categories that appear on their income and balance sheet and that justify big data project return on investments (ROIs). The primary ways to achieve business value through big data are:

  • increase revenues through the identification of new business opportunities or ways to monetize data;
  • shift gears deftly when a major supplier or a major market is threatened, which can be expedited if the right predictive analytics are used;
  • predict market moves and consumer buying habits; and
  • transform the way that businesses operate so they can be more customer centric.

How to assign a dollar value to what you are doing with big data

“Putting a dollar value on data is a very tricky endeavor,” writes James Kobielius, a Big Data Evangelist for IBM, in an IBM blog post. He continues: “Data is only as valuable as the business outcomes it makes possible, though the data itself is usually not the only factor responsible for those outcomes. How can we tie this back to putting a monetary value on big data?”

Kobielius talks about the “four Vs” of big data as critical elements when it comes to placing value on it. The big Vs are:

  • volume-based data, which enables you to extract maximum insights from the largest amount of data;
  • velocity-based data, which enables you to extract these insights quickly;
  • variety-based data that is aggregated from multiple big data channels; and
  • veracity-based data that is current and accurate.

It doesn’t matter how much data you have, or even if you have data of the highest quality that can be processed in an instant, if you don’t have a strong business case to apply it to. This is what differentiates organizations that get the most value out of their big data from those that are still struggling to see results.

Use cases of big data ROI successes

Here are several use cases where companies have transformed their businesses and obtained hard dollar returns on their big data investments.

  • Supply chain safety and theft enables companies, with the help of item-placed sensors and business intelligence, to reduce in-transit theft rates of supplies from 50% to 4% and to detect when the environmentals or seals on shipment containers have been compromised.
  • Logistics tracking and routing use business intelligence and machine-based data/sensors to optimize delivery routes and driver habits for fuel savings and better service.
  • Collections work at companies can be avoided by learning more about customers who are behind on their payments through big data aggregation and business intelligence that can predict who in good faith can pay their debts with a little help–and then helping these customers keep their purchases and keeping companies from having to write off defaults.
  • Buying habits and preferences of consumers are now better understood and have led to increased sales.
  • Predictive maintenance is enabling urban tram systems to stay online, reroute traffic where necessary, and flash adviser alerts to customers over their mobiles while repair crews are dispensed to replace faulty components before the components actually fail.

How to deliver big data results in 6 months

As big data become a mature discipline within organizations, you can expect it will command the same expectations for results as other IT projects; in others words, executives will expect to see tangle business results within a six-month timeframe. To attain these results, organizations should:

  • outline a very specific business case that the big data project will deliver value to;
  • make sure that key business stakeholders in the organization are supportive of the project;
  • define project metrics and monitor for results; and
  • pull the plug as soon as it seems a project will not work as planned.

For best results, the project should be able to produce an ROI that can be stated in hard dollars.