Creative Deployment of Business Competitiveness With Advanced Analytics

If analytics was doing what it’s supposed to do, we wouldn’t have to deal with text spams, we would have lovely customer service all the time, and we would always be advertised the products and services that we need, at exactly the right time. Also, businesses would always make informed decisions and be able to count on predictable outcomes every single time, rendering risk management completely irrelevant.

This Extremely Idealistic Utopia was one of the issues that came up during an Enterprise IT News roundtable titled, “Advanced Analytics: Creative Deployment for Business Competitiveness” not long ago.

Analytics of some form have been around for as long as humans were able to observe events and trends. In all that time, the business IT world had broadly categorised analytics into being descriptive, predictive and prescriptive.

The last type is interesting because as opposed to just trying to see patterns from data, and forecasting what will probably happen next, prescriptive analytics aim to help businesses make better decisions.

Needless to say, gazing into the future is an elusive capability. It would be akin to owning a crystal ball or a genie in a bottle that could provide a solution to your every problem.

This is what drives the business of analytics. This is what drives global companies like IBM, to firmly plant the analytics discipline as one of four main foundations underlying everything that they do. Analytics drives revenue for analytics vendor, SAS, which saw exponential growth to US$2.7 billion from US$138,000 just over three decades ago; 24% of that went back into R&D in 2011. Decision management company FICO’s analytical tool in 1956, the credit scoring system, made credit possible and into what it is today; their tools and services also serve two-thirds of the top 100 banks around the world. NetApp, a unified storage company with solutions that powers all of Yahoo’s storage needs, is drawn into analytics as well, because of the social media function Yahoo plays at a global level and all the unstructured or Big Data it has to deal with.

From the above, one might think analytics is pervasive, widely-used and ready for the next level. But, a consensus during the roundtable said otherwise.

Pervasive? Uh-uh.

A tiny fishing village in Japan was able to allocate fishing licenses more precisely and indirectly increase their yield by observing ocean tide currents, its timing and fish migration patterns. This is one example of how analytics brought benefit to a village’s economy, because it was applied creatively in a very unlikely scenario to very unlikely events. (Fish migration patterns? Their breeding habits are next, perhaps?)

The time for pervasive and prescriptive analytics is now. Big Data’s explosive growth, which sees it already comprising of 80% of all data in the world, ensures this. But somehow, analytics’ pervasiveness doesn’t seem to be happening fast enough, and as long as this is so, our roundtable speakers felt that its creative deployment will be limited.

Charles Manuel, IBM software group’s business analytics executive, for ASEAN said, “Analytics is usually done by a few super users in a business; they provide analytic outputs on a scheduled basis.”

This suggests that analytical tools are present, but whether by choice or otherwise, they are not optimised. Manuel shared that only when a business sees analytics as an asset and implements an analytics quotient culture across the organisation, can it begin to be used creatively. Another massive challenge to overcome is the lack of skills to optimise tools. Sarabjeet Singh, director of professional services at SAS Malaysia explained, “The skills to use it so it’s meaningful and drives growth, is not there.”

The remaining speakers weighed in with their opinions, FICO’s country manager Dinesh Pereira saying that emphasis shouldn’t just be on analytical knowledge but also knowledge in the domain it is practiced in. NetApp Malaysia’s country manager Andy Khoo echoed this, adding that decisions should be based on analytic results from a team of people led by a business leader, rather than by any one single individual.

Pereira agreed with, “We practice knowledge transfer to teach a select group of employees, how to optimise analytics in their organisation.” Overall, it raised the point that the discipline shouldn’t just be a curriculum in institutes of higher learning, but an ongoing education.

Doing the right thing, right

Perhaps SAS’s idea of high-performing visual analytics is the answer to making analytics a habit and from thereon, an organisational culture. Sarabjeet said, “We’re talking about terabytes of data being processed to answer queries in minutes instead of days.” That’s high-powered problem solving that could get pretty addictive; just look at search giant, Google.

Even then, the creative deployment of analytics is not so simple as that. Khoo opined that even after all the analysis done by systems, as much as 60% of decision makers regularly override the data and go with their gut feel instead.

“Whether we human beings make the decision or let a machine make the decision, it’s all about ego. We can’t get away from ego. And nobody likes being sold to,” he added.

At the end of the day, hard results from analytics should empower business users, not ‘overpower’ them to the point that experience and knowledge is left out of the decision-making process.

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