Descriptive, predictive, and prescriptive analytics will play a critical role for Indian businesses looking to gain a competitive advantage, write Saurabh Kumar Sahu & Narendra Mulani from Accenture Analytics
Companies in India are increasingly realizing that analytics plays a critical role towards staying competitive in the digital age. Analytics is no longer limited to retail companies and start-ups focused on customer experience; it is powering transformation in manufacturing, life sciences, automotive, and even oil and gas, among other sectors.
Descriptive, predictive, and prescriptive analytics implemented at scale is helping businesses move from data to decisions faster, and address a range of issues—from market share and pricing to employee engagement—to deliver measurable outcomes.
However, many established companies, including those using analytics solutions, are still to realize its full potential. For instance, in these companies, business intelligence is often unaligned to business strategy as data tends to stay siloed in different parts of the enterprise and is used for different goals. Or, in other instances, the investment in analytics solutions is not accompanied by the necessary organizational changes and the enterprise fails to achieve the intended business outcome.
Digital natives, on the other hand, are unencumbered by legacy infrastructure, and are better equipped to realize the value of analytics. To compete, established enterprises need to embed analytics across the entire value chain and become “intelligent" businesses or enterprises. The march of technology is already pushing the manufacturing sector in this direction. As manufacturing changes radically with the use of analytics, sensors and intelligent automation, connected intelligent factories are looking at achieving unprecedented levels of operating efficiencies and discovering new sources of revenue through smart products and services.
Disrupting traditional processes with analytics insights
Industry 4.0—where connected machines share data and automatically take decisions to execute tasks—is already a reality in advanced economies. Lufthansa Technik, the Lufthansa Group subsidiary that provides maintenance, repair and overhaul services for aircraft, engines and components, can predict precisely when components for aircraft should be replaced through a platform called Condition Analytics. Remote control monitoring and data analytics are also helping companies such as Claas, an agriculture equipment maker, to automatically transmit information from its harvesters to farmers or grain experts so that these can be operated through a smartphone app.
Before addressing this challenge, it is important for companies to understand two things about data. First, it is not a static entity and flows like a river throughout the organization. And second, analytics is not about randomly looking for useful patterns. Companies must begin with identifying an urgent business challenge to solve—such as optimizing costs, improving the bottom line or increasing productivity—and then work backwards.
After identifying the targeted business outcome, companies should assess their talent, tools and investments in analytics before it undertakes the analytics transformation journey. The DELTA model—a framework that measures maturity—could prove useful to assess the current capabilities and the route map to move along the five stages of the model. The journey from Stage 1 (where there is little management commitment to analytics) to Stage 5, the highest level in which analytics is a strategic competitive advantage with C-level accountability, can take time.
Analytics operating models in the new
To create a new operating model that supports an analytics-led transformation, many elements need to come together—from identifying enterprise-wide priorities (as opposed to “value islands"), to defining the analytics value proposition across functions, to the relevant metrics for measuring the outcomes. A strong leadership is needed to drive this strategic change as well as to acquire and retain scarce talent. Appointing a chief data and analytics officer to steer the organization’s analytics journey could set the right tone. The leadership must also mould a cultural shift that will enable everyone in the enterprise to work toward the same business goals with analytics .This could also involve collaboration with vendors, research organizations, start-ups, and other players in the digital ecosystem.
Accenture’s Analytics Operating Model Benchmarking study reveals that analytics leaders tend to work with a center of excellence (CoE) for analytics, instead of working with a centralized or a distributed model with islands of excellence in different business functions. An “analytics academy" also helps raise business acumen of analytics resources and analytics acumen of business resources.
To retain the right analytics skills, companies could deploy “pod" teams of data scientists, analytics modellers, visualization experts, data engineers, business analysts, and business domain experts. As speed and scalability are of the essence, leaders should ensure that their governance structures quickly empower and drive programs, and use the key metrics that can improve business performance.
Last but not the least, to optimize investment in analytics, companies first need to identify the priority capabilities, prove the value achieved in days and weeks (not months), and then identify a path to scale.