Big Data Benefits – Which business functions should companies focus on?


Big Data Benefits : Where Should Companies Focus?

Big Data BenefitsThere 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

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

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.

Big Data Study Implications & Recommendations
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