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For Innovators, a Drawback to Data Analytics

Matt Palmquist

Matt Palmquist is a freelance business journalist based in Oakland, Calif.

 

Bottom Line: Number crunching can be valuable for firms exploiting their existing resources, but can backfire for companies seeking to branch out with new products or services.

As companies have gained more and more access to information about consumers, competitors, partners, and suppliers, their use of data analytics to inform their decisions has skyrocketed. Reliance on managers’ gut instincts is increasingly a thing of the past. Now, decisions are made by crunching the numbers from such sources as social networks, Internet click-through patterns, search engines, and commercial databases, which, especially when combined, can give firms valuable data about the opinions and behaviors of consumers and other companies.

But despite the hype surrounding the era of big data, its usefulness might not apply equally to all types of firms.

A new study is one of the first to break down the importance of data analytics to companies with different strategic outlooks. In line with previous research on the broader topic of companies’ strategic positioning, the authors divided the firms they studied into two broad camps: those that prize innovation and exploration (which they called innovation-oriented), and those that seek to streamline their production processes and exploit their existing resources (which they called process-oriented).

Number crunching can backfire for companies seeking to branch out and innovate.

In findings that may seem counterintuitive, the authors discovered that innovative, forward-looking firms, in particular, should be wary of attaching too much importance to data analytics, which seem to muddle their research and development activities. Companies that base their business models on incremental improvements to existing products or processes, however, can indeed get a significant boost from exploiting data analytics.

The authors looked at a handful of databases to get a sense of how leading firms use analytics. First, they used data from a 2008 survey of senior human resource managers and chief information officers at more than 300 large publicly traded firms, who provided details on their company’s business practices and use of IT tools. The information in the survey allowed the researchers to classify each firm as either innovation- or process-driven. They then merged this information with a Compustat database, which, since 1987, has tracked many factors integral to the performance of large firms, including their physical assets, sales, and employee payroll. This was used to gauge the company’s success.

Finally, the authors accessed an online catalog of more than 6 million employees through 2007, identifying people who worked at the firms in their study sample. They used keywords in resumes to pinpoint employees with data-oriented backgrounds — including software engineers, systems analysts, programmers, consultants, program managers, financial specialists, and customer service supervisors. Because they had career-long information on all these workers, the researchers could link the importance of data-driven skill sets to each firm’s strategic focus and performance over roughly 20 years. Indeed, previous studies have suggested that firms’ ability to extract value from data analytics depends not only on having the right IT platform and latest technology in place, but also on employing a larger proportion of skilled workers who can process the information.

As a side note, the researchers acknowledge that their data came from before the explosion of social media. But as we’ve noted in the past, engaging in co-creation activities with consumers online is a tricky business, because fans of a brand, especially, are typically wedded to their preexisting notions about the products and services already on offer and are somewhat resistant to change. And the data available from social media is really just an extension of the type of information that was available to firms during the study’s time frame, which encompassed the proliferation of enterprise systems that enabled companies to use data analytics on a large scale for the first time.

Overall, the authors found, the level of number-crunching expertise in the workforce doesn’t seem to correlate with firm performance. But the use of analytics does help firms that have a process-oriented outlook. Analytics are not so helpful, however, for those with an innovative bent. In fact, the researchers found that a higher capacity for data analytics can actually reduce the performance of innovative companies. In short, use of data analytics seems far better suited to companies that are looking to make small incremental changes to their existing businesses than to those seeking to make a big splash with novel products or services.

The authors posit that data skills can be more crucial for firms that focus primarily on process-related decisions because the rationale for those choices can be more readily induced or backed up by hard numbers. After all, a great number of firms have used IT advancements to streamline their internal operations and achieve greater flexibility while cutting costs, resource usage, and turnaround times.

For innovative companies, in contrast, managerial judgments and instincts are still more valuable. Although search engine data and product reviews can provide an endless amount of information, the result is likely a case of too many cooks spoiling the broth. Studies have shown that analyzing consumer preferences at such a granular level tends to lead to evolutionary, rather than revolutionary, innovations, and coming up with breakthrough ideas necessarily involves the type of tacit knowledge or impulsive thinking that can’t be captured in a spreadsheet.

For an example, look no further than Steve Jobs, who famously ruled out the use of marketing research when Apple was developing the original iPad. Until then, most consumers and media analysts were skeptical about the idea of tablet computers, most of which had failed to deliver on their promise of seamlessly combining high performance and mobility. If Apple engineers had placed too much trust in public feedback, which is hugely dependent on the experiences and products people already know as opposed to their imaginings of what might be, managers might have been dissuaded by the existing cynicism. Suffice it to say that the iPad has done pretty well for itself — and other innovative firms should take note to avoid information overload.

Source:How Do Data Skills Affect Firm Productivity: Evidence from Process-Driven vs. Innovation-Driven Practices,” by Lynn Wu and Lorin Hitt (both of the University of Pennsylvania), Wharton School Research Paper No. 86, Feb. 2016

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For Innovators, a Drawback to Data Analytics