The growth of digital technologies has enabled companies to collect massive amounts of data — and as a result, most are now seeking more powerful techniques to make sense of that data. Big data analytics is therefore emerging as a critical business capability.
A recent Accenture study found that while companies tend to have high expectations for big data analytics in their supply chain, many have had difficulty adopting it. In fact, 97 per cent of executives report having an understanding of how big data analytics can benefit their supply chain, but only 17 per cent report having already implemented analytics in one or more supply chain functions. But as increasing understanding leads to action, big data analytics is moving from hype to reality. Our research revealed that three out of 10 executives surveyed have an initiative underway to implement analytics in the next six to 12 months, and 37 per cent are having serious talks about the role that analytics could play in their supply chain.
The research also uncovered some commonalities among companies that generated a higher return from their investment in big data analytics. Three key practices distinguished these supply chain leaders from the others.
- Leaders made developing a robust big data analytics enterprise-wide strategy a higher priority.
While some companies applied a process-focused strategy to their implementation of big data analytics, companies more frequently realised stronger results when they applied an enterprise-wide strategy. For instance, 61 per cent of those who had an enterprise-wide strategy said big data analytics helped them shorten their order-to-delivery cycle times while only 14 per cent of those using a process-focused strategy saw similar results.
Before applying an enterprise-wide strategy, Accenture suggests that companies get a clear view of what will help them drive value and create differentiation in the market. Then they can use those insights to build a road map that can help them achieve their goals with big data analytics.
- Leaders embedded big data analytics into operations to improve decision making.
The operationalisation of those plans is important, and Accenture has found that embedding analytics into day-to-day operations can generate significant benefits – more so than when it is applied on an ad hoc basis. Respondents who embedded their big data analytics more frequently said they’d been able to shorten their order-to-delivery times, increase their supply chain efficiency by at least 10 per cent and lower their cost to serve.
There are many technologies, devices and vendors to consider which means that making the right decision can be a challenge. But the decisions can be informed by the goals the company aims to attain as big data analytics is operationalized.
- Leaders hired talent with a mix of deep analytics skills and knowledge of their business and industry.
As with any new capability, skills are critical. Our research found that companies with a team of data scientists tended to get stronger results than traditional database personnel. For example, 50 per cent of respondents employing a team of data scientists saw improvement in running demand driven operations while only 9 per cent of those rely on traditional database personnel saw similar results. The key is finding people with strong mathematics, statistics and econometrics skills who can create analytical models that are also rooted in an understanding of the business.
Companies that take advantage of the results that big data analytics can yield have the opportunity to realise tremendous results which can boost a business’ bottom line as well as its operating performance. But as we’ve seen, creating a competitive edge with big data analytics requires a thoughtful approach that combines the technology with the front-end business strategy to help companies draw on the potentially transformative characteristics of big data analytics to their advantage.
☛ Rob Woodstock is a managing director in Accenture’s Strategy practice.