Pearson on Excellence: New insights on analytics

Effective use of business analytics—using quantitative methods to derive forward-looking insights from data—is essential for companies that are serious about supply chain excellence. Why? Because with analytics, supply chain managers gain a deeper understanding of what is happening upstream and downstream. As a result, they’re better able to assess the operational impacts of prospective supply chain decisions.

And in fact, more and more companies are using business analytics. According to Accenture research, 45 percent of large North American companies increased spending on analytics in 2011, and 65 percent will do so in 2012. Bloomberg Businessweek Research Services reports that 97 percent of companies now use some form of business analytics. And IDC predicts that the market for analytics software will grow at an annual rate of more than 8 percent through 2015.

But even with increasingly high adoption rates, most organizations aren’t always able to make advanced analytics click—to fully leverage the technology’s ability to turn raw data into usable information and prescient decision-making. Here are five things that may be holding companies back.

The first big barrier is the quantity and competency of human resources. In some cases, companies’ enthusiasm led them to acquire analytics technology before obtaining the capabilities to use the new tools effectively. According to Accenture’s research, more than 60 percent of survey respondents believe their companies have a dearth of analytical talent.

A second problem is that companies are often stymied by technological issues—not installing/acquiring analytics, but maximizing the quality, consistency, accuracy, and accessibility of analytically derived data. More than 40 percent of Accenture’s survey respondents said that they are uncertain about how to integrate analytics-based insights into their business decisions.

A noteworthy exception is Staples, which recently sought to develop a deeper understanding of its shoppers’ online buying habits. By using analytics, Staples discovered that strong Web traffic wasn’t translating to sufficiently high sales, and that the problem was customer drop-offs at cumbersome points in the page-navigation stream. A subsequent site redesign made a huge difference.

A third pressing factor is culture: organizational resistance and discontinuity. For analytics to work, collaboration is clearly essential. But as businesses grow, data becomes more dispersed, silos form, and access and cooperation become more difficult.

As a major bank discovered, these growing pains are largely a culture problem. According to the institution’s director of analytics, staff members frequently ask for data, but they don’t know precisely what they’re looking for. Analytics has helped the bank’s people ask better questions and, subsequently, get more valuable answers. According to the director, “all of this benefits our customers: The customer gets the right offer because of our research, and this directly impacts the bottom line.”

A fourth issue is lack of a strategy. In most organizations, business analytics began as an ad hoc endeavor—focused on one issue or department. Over time, its use may have expanded to other parts of the business. This piecemeal approach makes it difficult to step back and examine analytics’ use in a holistic, forward-thinking manner.

The fifth obstacle is measurement of success. Many companies—about one in four, according to Accenture research—do not know if they actually have achieved a positive return on their analytics investments. The logical conclusion is that these organizations have not developed the necessary success metrics. In some cases, an overall “culture of measurement” may be absent.

Becoming a high performer in analytics
Given these five critical hurdles—talent, technology optimization, culture, strategy, and success measurement—it isn’t hard to see what, in Accenture’s view, defines a high performer in the analytics arena.

Such companies are successful in their use of business analytics as measured by the effective use of analytics in decision making and a positive, measurable ROI for analytics expenditures. The most obvious difference between high performers and non-leaders is that the former have the right talent in place.

High performers also enjoy strong executive support for analytics and a culture that supports fact-based decisions. And although analytics may not be in force across the company, it has spread in a logical and integrated fashion beyond individual functional and department use.

And how are high performers rewarded for their analytics prowess? A short, high-level list could include new opportunities to identify profitable customers, “right price” products, accelerate innovation, optimize supply chains, minimize risk, create differentiated services and (perhaps most important) recognize the key drivers of financial success.