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Trends and Outliers

TIBCO Spotfire's Business Intelligence Blog

12/03
2013

Not All Big Data is Created Equal – Best Practices for Taking Action

The volume of data that’s now available to decision makers is mind-boggling. In fact, 2.5 quintillion bytes of data is being collected every day and the growth in data is simply astounding: 90% of all of the data that’s ever been created has been generated in just the past two years.

shutterstock 1519206711 300x271 Not All Big Data is Created Equal – Best Practices for Taking ActionHowever, experts warn that more data doesn’t necessarily result in better decision making.

“Even the best data doesn’t lead to the right decisions if you don’t know how to look at it,” says Rachel Kennedy, associate professor and director of the not-for-profit Ehrenberg-Bass Institute.

A big part of the challenge is that not all big data is created equal. Certainly, the quality and integrity of data that’s being gathered and used can have a significant impact on the operational or business outcomes that are generated.

For instance, dirty data costs US businesses more than $600 billion annually, notes The Data Warehousing Institute.

In practical terms, let’s say a transactional consumer packaged goods company has data on millions of existing customers and prospects across multiple geographies.

If just 10% of that information is corrupted in some way – e.g., redundant or erroneous customer information such as incorrect addresses or contact data – the company may well be wasting millions of dollars creating multiple targeted offers for people or offers for consumers who are unreachable.

As practitioners will acknowledge, certain data sets will deliver more bang for the buck than other data sets. Data visualization tools can bring these discoveries to light, enabling IT and business leaders to better identify the types of data that are bringing the greatest returns, which can help to improve future optimization of big data.

For example, an electronics retailer has been able to determine through customer segmentation data that a particular customer is male, between the ages of 25 and 34 and has an annual household income of $100,000 to $125,000. That information may help the retailer understand some basic characteristics of this particular customer.

But additional online behavioral information can reveal more actionable insights – including whether the man is married or in a relationship, the company’s web pages he has browsed most recently, etc.

These types of insights can enable the retailer to gain a much deeper understanding of this customer and the types of offers he’s most likely to respond to. If the retailer achieves higher conversion rates with this customer and others by analyzing and acting on the behavioral data, this will result in high returns for the data that’s been obtained and analyzed.

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