1.About half of the firms surveyed are using Big Data, and many of them projected big returns for 2012. 53% of the 1,217 firms surveyed had undertaken Big Data initiatives in 2012, and of those 643 companies, 43% predicted a return on investment (ROI) of more than 25%. About a quarter (24%) either had a negative return or didn’t know what the return was.

2.There’s a polarity in spending on Big Data, with a minority of companies spending massive amounts and a larger number spending very little.Some 15% of the companies with Big Data initiatives spent at least $100 million per company on them last year, and 7% invested at least $500 million. In contrast, nearly one-quarter (24%) spent less than $2.5 million apiece. This has resulted in a big spread between median ($10 million) and mean spending per company ($88 million). Industries spending the most are telecommunications, travel-related, high tech, and banking; life sciences, retail, and energy/resources companies spend the least.

3.Investments are geared toward generating and maintaining revenue. 55% of the spending goes to four business functions that generate and maintain revenue: sales (15.2%), marketing (15.0%), customer service (13.3%) and R&D/new product development (11.3%). Less than half that amount (24%) goes to three non-revenue-producing functions: IT (11.1%), finance (7.7%), and HR (5.0%).

4.The business functions expecting the greatest ROI on Big Data are not the ones you may think. Although sales and marketing garner the largest shares (a combined 30.2%) of the Big Data budget, the logistics and finance functions (which together get only 14.4% of the budget) expected much greater ROI on their Big Data investments. Furthermore, when asked to rate 75 activities in eight business functions on their potential to benefit from Big Data, companies around the world ranked just as many logistics activities as they did sales activities in the top 25.

5.The biggest challenges to getting business value from Big Data are as much cultural as they are technological. When asked to rate a list of 16 challenges, companies placed an organizational challenge at the very top: getting business units to share information across organizational silos. A close second was a technological issue: dealing with what has become known as the three “V’s” of Big Data: data volume, velocity and variety. The third challenge was determining which data to use for different business decisions.

6.Nearly half the data (49%) is unstructured or semi-structured, while 51% is structured. The heavy use of the former is remarkable given that just a few years ago it was nearly zero. On another dimension of comparison, about 70% of the data is from internal sources rather than external. However, using external and unstructured data has outsized impacts. Companies that expect much bigger ROI on Big Data use more external and unstructured data than do companies expecting lower or no ROI.

7.The companies with the biggest projected 2012 returns on Big Data saw those returns coming from places that the laggards don’t value as much. To use a gold miner’s analogy, the leaders pan for gold in different places – most of all in marketing, sales and service. The two activities where leaders see much greater potential than laggards are: improving customers’ offline experience and marketing to consumers based on their physical location. ROI leaders also see much greater potential than do laggards in using Big Data to size and structure sales territories. And in customer service, leaders envision greater potential benefits in monitoring product usage to detect manufacturing and design problems.

8.Companies that do more business on the Internet spend more on Big Data and project greater ROI. Companies that generate more than 75% of their revenue over the Internet spend about six times more on Big Data than do companies whose Internet business is 25% or less of total revenue. These Internet-centric companies also projected an ROI on Big Data (88%) that was nearly three times that of the less Internet-centric companies. Furthermore, the depth of the behavioral data that Internet-centric companies gather on their online customers gives them proprietary insights for developing superior new products and services, as companies such as Procter & Gamble Co. and Netflix Inc. have found.

9.Monitoring how customers use their products to detect product and design flaws is seen as a critical application for Big Data, especially by heavy manufacturing companies such as General Electric Co. whose customers depend on their products.

10.Organizing a core unit of Big Data analysts in a separate function appears to be important to success. Companies that expected the highest ROI on Big Data in 2012 are more likely to have a separate department of professionals who process and analyze Big Data than are companies expecting the least ROI (or no ROI).

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