Published Editorial

The analytics challenge

  • Share

The Financial Express

by

Samiron Ghoshal
Partner & National Leader—IT Advisory Services
EY

Contributed by:

Manoj Jha
Associate Director-IT advisory services
EY

Analytics is the discovery and communication of meaningful patterns in data. As a topic analytics has traversed from being discussed at the sidelines of industry and technology conferences to the top of the corporate agenda. With the extant promise of delivering performance improvements not seen since the redesign of core processes in the 1990s, these tools are likely to change the competitive landscape in quite a few industries in the years to come.

Time was when a visit to the neighborhood general practitioner elicited physical examinations, about with the stethoscope followed by embarrassing questions about bowel movements ending with a red colored medicine bottle. The doctor of today will suggest a myriad of blood, urine and other pathological tests for diagnosis before he even hazards a guess on your ailment.

Much in the same way management and decision making seems to be transforming from gut-feel and experience in the trenches approach to being influenced by hard numbers, analyses and trends thrown up by the analytics teams. For instance, marketing has evolved from a creative process into a highly data-driven process. Marketing organizations use analytics to determine the outcomes of campaigns or efforts and to guide decisions for investment and consumer targeting. Demographic studies, customer segmentation, conjoint analysis and other techniques allow marketers to use large amounts of consumer purchase, survey and panel data to understand and communicate marketing strategy.

Most organizations have addressed the data and analytics challenge by spending money on the IT infrastructure—hardware, software and consulting, in a half-hearted attempt to catch up in the race. Yet most are struggling to exploit these resources and are far from the analytics nirvana that was promised.
Broadly, there are three broad questions that an enterprise needs to have answers to they embark on the journey: what data to use—internal and external, how will they handle analytics—internal or external capabilities or a mix of both and how will they use the insights from the data to transform themselves.

The first question focuses on what is the data they should or can use. Really determining which data to source, how to source it and how to get it together in an integrated form is first part of solving the puzzle. For instance an agrochemicals company may want to incorporate weather, climate and irrigation data to their internal sales data to see meaningful patterns that emerge.

The challenge for most Indian companies in this regard is while there are substantial amounts of data from their transaction, billing and other OLTP systems not a lot of it is clean and usable. Since data is the bedrock of the success in this area, this requires the highest amount of attention. Data quality, data cleansing and governance need to be put in place by most organizations. Again most Indian companies are far behind their global peers in this regard.

The second challenge is the analytics challenge. This is a highly mathematical, analytical modelling exercise which requires the right skills, the right IT infrastructure and governance to get it right. Building the right team of people who understand the business context as well as the statistical and mathematical nuances and the IT tools to run these was and continues to be a challenging task.

Questions about whether to centralize or decentralize analytics is a key decision. Centralization balances and builds skills in a center of excellence providing data geeks the appropriate environment to work and feed off of each others skills. Analytics skills in the respective business units however brings them closer to the end users who understand the context in which the data is being used. As in life, so in analytics there is no correct answer—both of these opposing forces need to be balanced and seen in the organizational context before being baked into the overall plan.

The third challenge is how to ensure the insights are translated into business decisions. As with any new fad there are and will be challenges in how the insights get used. Most business units in enterprises are used to taking decisions in a certain way and need to be change managed into accepting analytics.

A large Indian conglomerate has gone about answering the analytics challenge by appointing a chief data officer whose charter is to work with each of the business units to create a framework and a plan of how analytics could possibly add to the decision making at each level. His next charter was to consolidate the requirements and design a hub-spoke model with data scientists and cutting edge expertise at the center of excellence and data analysts embedded within the business units. Finally to work with the CIO to build and select the IT infrastructure to support this initiative. Regardless to say this exercise has taken considerable time and expense to set up, but has the right thought behind it and will definitely begin to show results to differentiate it from its competition in years to come.

In contrast is the case of a hydrocarbons major who has had a fragmented approach to the whole data issue and in spite of spending many millions is no closer to seeing value from it. Constant pushback from business unit heads who have not been party to the overall analytics plan and have not been sold the value of this piece in the overall decision making is stalling the projects. Requests for changes in the overall structure and requirements continue to pour in even as the design is being finalized.