There are at least two aspects to the question of industries investing in Big Data:
- Which industries should consider making greater-than-average investments in Big Data and analytics?
- Which other industries have smaller numbers of early adopters of Big Data and thus have many companies that need a wake-up call?
To address the first question, we revisit the survey results on median industry spending on Big Data (Exhibit VII-1).
Exhibit VII-1: Which Industries Spend the Most on Big Data?
Q14 : Median Spending Per Company on Big Data in 2012 by Industry (in $ millions)
Four industries — telecom, travel-related, high tech, and banking/financial services – told us they spend much more than the median on Big Data. So what do these industries have in common? For one, they have high numbers of customer interactions (especially online). In addition, according to our data, three of them generate higher than average percentages of revenue from Internet orders. (See Exhibit VII-2.)
Exhibit VII-2: By Industry, % of Companies With Revenue From Internet Orders
Q2-a: Percentage of 2012 Revenue from Customers who order Products/Services over the Internet by Industry
While it may be easy to believe that if your company is not an internet company that Big Data is far less important to you, it’s not the case. The reason is that the way customers are interacting with companies online today go far beyond ordering merchandise from their websites. The points of digital interaction abound: social media sites such as Facebook, Twitter, and Pinterest; mobile applications that consumers use on their smart phones; sensors on machines that report their vital signs back to the manufacturer; and many more.
If you simply look at the early adopters of Big Data – internet companies that continually tinkered with their websites and mined their web viewer data to sell customers more of what they want – you’re likely to miss the next wave of Big Data adoption. These adopters, exemplified by companies such as General Electric, will use it to pinpoint fraud, predict machine failures, and make a wide range of other product and process improvements in their bricks-and-mortar businesses.
Now let’s look at the second question we posed: In which industries are a small minority of companies starting to pull ahead, thus industries where the majority need a wake-up call? Through our survey, we found a small minority of companies with projected ROIs on Big Data of more than 50% in four sectors:
- Consumer goods (in only 9% of these companies did functional managers report ROI >50%)
- Utilities (15%)
- Insurance (17%)
- Media and entertainment (19%)
That’s a sign that the clear majority of companies in these industries have a long way to go to catch up to the leaders.
Another way to shed light on which industries may have some of the biggest opportunities with Big Data is to understand which may be betting the “most bang from the buck” – the sectors with the highest projected returns on Big Data1 despite the lowest investments (i.e., Big Data spending as a percent of revenue). Exhibit VII-3 shows these to be energy & resources, life sciences, travel-related, banking, insurance and heavy manufacturing.
Exhibit VII-3: Who’s Getting the Biggest Bang from the Buck – and Should Strive for More?
Plotted on a 2×2 chart, the data looks like this:
Exhibit VII-4: Plotting the Industries by ROI and Spending on Big Data
Spending on Big Data (as Percentage of Mean Industry Revenue)
Opinions may vary on which industries need to take the lead in Big Data. But one fact is undeniable for all 12 industries that we surveyed: The leaders spent more than three times what the laggards spent on Big Data in 2012. The median spending for leaders was $24 million; for laggards, it was $7 million. (See Exhibit VII-5.) With Big Data, there appears to be a certain level of foundational investments necessary to play the game at a high level.
Exhibit VII-5: Leaders Far Outspend Laggards on Big Data
Q14 : Median Annual Expenses on Big Data in 2012 Leaders vs. Laggards (in US $millions)
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Big Data Study: Implications and Recommendations
Big Data Study Implications & Recommendations
- In this calculation, ROI is enterprise ROI, not functional ROI, as reported by the IT or analytics managers who answered the survey on behalf of the entire company, not a single business function. [↩]