While Internet companies may have jumped to the lead on Big Data, many other industries need to follow quickly, even those whose business is not threatened by Internet firms (present and future). The growing number of companies whose customers purchase their products and services over the Internet (and increasingly from mobile devices) will have a distinct competitive advantage if they can incisively analyze customer behavior on their sites and other data – and act on it quickly. Amazon.com Inc.’s ascension to a $61 billion in revenue over 20 years and Netflix’s decimation of Blockbuster Entertainment show how companies with superb Web data and analytics capabilities can elbow aside traditional players that operate too much on intuition.
Yet Amazon, Netflix and other companies are also showing what can happen when a company possesses much deeper insights on customers based on their digital habits: it can get into the product business itself (e.g., Netflix’s “House of Cards” TV series). Companies that don’t use their analytics to see the next great product or service opportunity run the risk of letting analytics-savvy competitors trump them in product innovation.
But bricks-and-mortar companies that don’t compete against internet businesses such as GE’s aircraft engines and turbines divisions believe they have an immense opportunity to use the internet and Big Data (especially unstructured data) to keep improving their products and help customers get more value from them. Companies that sell big-ticket purchases (to consumers or businesses), whose products’ performance must be frequently monitored to ensure they work, have a great opportunity. They can turn data that had been “external” (collected by customers) into “internal”.
By applying Big Data in the right places in the organization, centralizing and nurturing talent, and building bridges to functional managers who need data-driven insights to make superior decisions, companies will greatly raise the odds of keeping up in a world in which digital data-driven decisions become the norm, not the exception.
Big Data Benefits : Where Should Companies Focus?
There is no one-size-fits-all answer to this question. It will largely depend on the industry, a company’s competitive dynamics in that industry, and (most of all) where it needs to improve its performance with customers, among other factors.
Nonetheless, our data on functional activities rated highest in potential benefits provides guidance. Perhaps even more so does a comparison of the ROI leaders vs. ROI laggards on this data (see Exhibit VII-6). Six functional activities that ROI leaders rated much higher than laggards did are in marketing. Of the top 15, all but two were in marketing, sales and service.
Exhibit VII-6: Where ROI Leaders Pan for Gold (and Laggards Don’t as Much)
Q18a-h : 15 Largest Differences Between Leaders and Laggards in Functional
Activities with highest Potential Benefits from Big Data
In marketing, the leaders see the potential benefits of Big Data as being much higher than the laggards see them in marketing to consumers based on their physical location, improving their experience of the company in offline channels, discerning competitors’ moves and monitoring customer and market brand perceptions.
In sales, ROI leaders see much greater potential in using Big Data to size and structure sales territories. And in customer service, leaders see much more potential in using Big Data to monitor customer usage of products to detect manufacturing and design flaws. This is where companies such as GE and Xerox see the greatest potential of Big Data.
Again, where a particular company should focus investments depends on dynamics of its customer and competitive situation. We have found it helps to think about the opportunities in three broad categories:
- Revenue: These investments will most often fall in marketing, sales, service, and R&D/product development.
- Risk reduction: Finance and supply chain operations are full of opportunities to detect risk, from spotting customers whose credit is about to go bad to transportation routes and locations that have become heightened theft risks.
- Operational effectiveness: These investments enable companies to improve the way they engage with customers and make every interaction more effective. This camp includes activities that may not directly turn into more revenue but which are important to keeping customers in tow. Consider investments such as getting to the root of customer service problems, manufacturing process flaws, and supply chain bottlenecks.
Companies that create a portfolio of initiatives in these three categories and then implement them over time will reduce the chances they overlook good opportunities, especially less glamorous ones that don’t directly touch revenue. (Don’t forget that the functions with the highest Big Data ROI were finance and logistics – back-office operations that might be easy for the data scientists to overlook.)
To prove that Big Data has value, companies that are just starting to make serious investments should focus their portfolio on acute pain points. Those that can be solved by analyzing structured and internal data are particularly good to focus on. This is because structured data is typically much easier to gather and process; internal data mostly likely already resides in a company’s information systems. Having data that is easy to manage allows a company to cut its teeth in a more controlled environment. Gaining these skills will help it deal later with more complex types and sources of data.
As the returns on early Big Data initiatives become clear, it becomes easier to get funding for more elaborate Big Data initiatives. (See Exhibit VII-7.) These may require unstructured or semi-structured data, as well as data from outside the company.
Exhibit VII-7: Where to Begin (and End) With Big Data
Over time, as a company’s Big Data initiatives prove their value, it will be far easier to secure funding to address bigger, more costly problems — ones that may very well require new technologies and new people. These initiatives have the opportunity to produce a “bigger bang” – a higher top line or larger cost reduction. But they could also require larger investments.
At the same time, these initiatives can create bigger competitive barriers: data scientists who have built company-proprietary algorithms for crunching data; home-grown processing and analytics tools designed for industry-idiosyncratic data challenges (e.g., in velocity, variety and volume); and possibly data that competitors have not collected. GE sees a $30 trillion opportunity in the industrial markets it serves for companies that can use Big Data and analytics to boost the efficiency of aircraft, power turbines, trains, and other costly industrial equipment. The Internet company whose website improvements have lifted revenue by hundreds of millions of dollars over the last few years provides an excellent example of this. Employing 70 Big Data analysts to recommend changes to the website is, of course, a major expense. However, the return appears to be one of far greater magnitude.
The path over time is for Big Data investments to provide both “bigger bang” and “bigger competitive barriers”. We’ll discuss more about how to provide the latter in the next two sections.
Getting a 360-Degree View on Big Data
Trying to understand what a large company is doing with Big Data depends upon whom you ask in that firm. Corporate IT management is likely to have a broader but more superficial view of what the company is doing with the technology. Managers of marketing, sales, service, and other business functions are likely to have greater knowledge of how Big Data is used in their areas. And managers running analytics groups (apparently a growing breed) have perhaps the best view of how an organization is using Big Data.
To get a better picture of how companies are using Big Data, we designed the study to collect data from IT, business functions, and analytics managers. Nearly one third were IT managers; 62% were from eight business functions (marketing, sales, service, production/manufacturing, logistics, research & development, finance, and human resources). And the remainder (7%) operated in analytics groups. (See Exhibit VIII-1.) In all, 88% either headed one of those functions or reported to the head of it.
We also wanted people in these functions who had intimate knowledge of their company’s Big Data activities. The majority (58%) said they played supporting roles in this endeavor, and 23% played leading roles. The rest (19%) said they had no role but substantial knowledge about what their company was doing with Big Data.
Exhibit VIII-1: Big Data Survey Respondents by Functional Role
Q3 : Respondents by Function?
We also wanted to limit ourselves to large companies. The clear majority of companies – 83% — had revenue of more than $1 billion. In fact, the median revenue was $6.9 billion (while the average revenue was much higher, at $19 billion). Of the 643 companies that reported at least one Big Data initiative, the percent by regions can be seen in Exhibit VIII-2. Nearly half were from North America (that is, the U.S.). One quarter were from Europe, 16% were from Asia-Pacific and 11% were from Latin America.
Exhibit VIII-2: Big Data Survey Respondents by Region of World
Q5-a : Where Companies are Headquartered (Region of World)
The breakout by the nine countries we surveyed can be seen below.
Exhibit VIII-3: Big Data Survey Respondents by Country
Q5-b : Where Companies are Headquartered (Country)
The survey population was tilted toward large companies. The majority had revenue of at least $1 billion in all four regions (see Exhibit VIII-4).
Exhibit VIII-4: Big Data Survey Respondents by Revenue
Q2: 2012 Revenue of Respondents by Region
Most executives surveyed either ran a business function (marketing, sales, service, manufacturing/production, R&D, logistics, finance or HR), the IT function, or an analytics function – or reported to the person who ran one of those functions. (See Exhibit VIII-5).
Exhibit VIII-5: Big Data Survey Respondents by Organizational Level
Q4: Respondents by Organizational Level (Region)
Travel/hospitality/airlines, telecommunications, banking/financial services and high tech companies rated higher on Big Data spending in 2012 than the other sectors that we surveyed (by median spending), as shown in Exhibit III-1.
Travel/hospitality/airlines companies spent a median $25 million/company, as did telecom companies. High tech companies’ median spending per company was $17 million. Banking/ financial services companies’ median spending was $19.3 million per company.
On the opposite end of the spectrum were life sciences companies ($4.7 million) and energy & resources firms ($2.5 million).
Exhibit III-1: Per-Company Spending on Big Data by Global Industry
Q14 : Median Spending Per Company on Big Data in 2012 by Industry (in $ millions)
In this Big Data Study, TCS surveyed 1,217 companies in nine countries in four regions of the world (US, Europe, Asia-Pacific and Latin America) in late December 2012 and January 2013. Of these companies, a little more than half (643) said they had undertaken Big Data initiatives in 2012. We also conducted in-depth interviews with more than a dozen executives across industries about their Big Data initiatives between December 2012 and March 2013. In addition, we interviewed two experts in the fast-evolving technologies of Big Data. This data, as well as our consultants’ growing experience in helping large companies leverage Big Data, provide the basis for the findings in this report.
While our findings are numerous, we believe the following 10 are the most important ones:
The Big Data Study: Key Findings
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. (Read more)
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. (Read more)
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%). (Read more)
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. (Read more)
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. (Read more)
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. (Read more)
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. (Read more)
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. (Read more)
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. (Read more)
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). (Read more)
In the sections that follow, we explore these findings as we discuss how the survey results in this big data study compared across regions of the world, by global industries and by business function. Lastly, we discuss the implications of our research and provide advice for companies that want to get more out of Big Data and need to know where and how to begin.
We base our prescriptions in Section VII of the Big Data Study on two sources: our analysis of what leading companies at the Big Data game (those with the greatest ROI) are doing differently than the rest, and insights from TCS consultants who are helping our clients capitalize on Big Data.