How firms can ride the analytics wave
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Riding the analytics wave, organizations are now leaving comfortable shores and beginning their journeys into unknown seas. Exciting as these voyages are, they remain, however, fraught with the dangers of over promising and under-delivering—the key problem being not the technical teams but the business teams within an organization.
Consider the central government’s historic demonetisation exercise, undertaken on 8 November, 2016. Regardless of how you perceive the move, it does make for an interesting case study in analytics. A cashless, digital India would most definitely be more transparent, more efficient and more honest. Such an exercise throws up great opportunities for learning and there are enough nuggets about data analytics in this exercise that business leaders can learn from.
As demonetisation kicked in, billions of rupees gradually found their way into new and old bank accounts. This, though hailed as a failure by detractors, actually was a small victory—because now there is a digital trail for lost and hoarded paper money. This resulted in close monitoring—with the government, banks, technologists and data scientists working 24x7—and, subsequently, in the Central Board of Direct Taxes (CBDT) reporting that it had identified close to 2.7 million suspicious income-tax returns. Identifying 2.7 million returns—which make up for less than 1% of all returns filed—requires large-scale crunching of data, considering the technology environment the tax authorities operate in is filled with legacy and interoperability issues. This is an apt example of deriving insights from the application of data analytics—and it is just the beginning of a long journey given that the current infrastructure of the tax department is simply not built to handle the new sea of information.
As digital continues to proliferate and more people and organizations come into the tax net, the backlog will certainly increase. There will arise a need for a massive analytics exercise which crawls though the large amount of data available. With technical and data science expertise, this exercise will generate only insight, but not value. To be able to derive value from the insights received from data, the answer is transformation, which in today’s age is a necessity for any organization.
The tax department in this case would need more assessors to begin with, new channels of communication and interaction with the assessees and new ways of evaluation. This might need volunteers as well. It may perhaps also require doing away with ‘deadlines’ for filing taxes once a year and make it a continuous process. Additionally, the department would also need to change its disputes and resolution mechanism for speedy redress and faster turnaround times.
The gap which we see is not typical of the government alone. It is common in many organizations across the world. For a successful analytics voyage, organizations need technical experts (technologists and data scientists) as much as they need organization-wide “consumers” who can bring business value by consuming data and acting upon insights derived from the data.
A simple way to assess and ensure that an organization rightly aligns their energy and effort on both these dimensions is to use a ‘technical agility and business agility framework’.
In such a framework, every analytics idea or use case is quickly run against a set of pre-qualifying criteria comprising five elements each in terms of technical agility and business agility. The elements on the technical side are data availability, data sufficiency, data quality, architectural building blocks and technical capability. And on business agility, the key elements include business value, information velocity, data culture, trustworthiness and data consumption readiness.
There are instances where the lack of balance between technical agility and business agility leads to the undermining of analytics treasure troves. For example, gaining from insights into suspected cases of fraud, customer offers/discounts and identification of employees who may potentially leave an organization requires the business teams to ‘adjust to the new level of information’. This may require more capacity or different skill sets to deal with the new information.
Consider the case of a technology product company with more than 200,000 resellers globally, which implemented an analytics programme to identify the infringement of its contracts and incentives (in respect of the dealers). The number of potential infringements was so large that they had to deploy a self-assessment process along with an issue-tracking system to make sure the identified cases were dealt with and the sales and support teams were retrained to be able to educate the reseller community. Additionally, their sales incentive teams, too, had to be revamped so as to amplify and optimize efforts in the light of better information.
Analytics has the ability to overcome the ‘fat smoker problem’, which is simple yet profound. It assumes that every fat person who smokes is aware that what he or she is doing is wrong; that they should slim down and give up smoking—yet they can’t. If the ‘fat smoker decisions’ (when we know data knows better) are left to analytics, it would make substantial gains for an organization.
Finally, it doesn’t matter what kind of an organization you belong to—big or small, global or local, fast or slow, profitable or unprofitable. Riding the data analytics wave is perhaps inevitable and is the new reality.
Abhijit Varma is partner, data and analytics, at KPMG in India.