Achieving Analytic Nirvana During Lockdown
The world was hurtling towards destruction with environmental degradation, deforestation, increased carbon footprint and pollution levels, a system reboot was inevitable. Then, the COVID-19 pandemic bought the world to a standstill, forcing everyone to stay indoor and companies to adapt and evolve themselves to accept remote working as a norm. Overnight, business leaders and team managers had to change the way they did their work, decreasing reliance on in-person supervision. It also provided an opportunity for a relook at how they make decisions, moving from synchronous to a more reflective and an asynchronous way backed by data. For businesses, the future holds a more decentralised decision-making culture with more time spent on analysing the data. All this means that the roles and responsibilities of IT & Analytics teams will increase dramatically to support business continuity with the increase in the use of data and analytics. In a general business environment, we find three types of leaders, who will need data training to come up to speed with data-driven decision-making culture. The first type believes that experience and context are paramount and that data cannot capture the essence of the business. The second type of executives are more detail-oriented and do their analysis themselves. They will only reach out to the analytics team if they have more questions. The thirds type includes those who neither use too much data nor they accept that they need training. They work based on reports prepared by others. Analytics platforms must design should keeping in mind that all three types that co-exist in a workspace, are now working remotely. From an analytics evolution perspective, organisations fall in one of the five stages mentioned below. Those in the first two stages, where most organisations come under, see the current situation as a challenge and are trying to cope with it. Those organisations that are under the next three categories see the present scenario as an opportunity. Stage 1 – Remote working for these organisations is not an issue as long as the systems do notwn breakdown. The executives of these organisation have all the information they need to keep the business running remotely. Stage 2 – These organisations responded quickly to the changes and are demanding more data support now. Demand for their quarterly and monthly reports are currently on a monthly and weekly basis. Their data models are updated more frequently, and they expect more responsive support on ad-hoc analysis requests. They need shorter range forecasting now and are trying to replicate all their physical strategies into virtual strategies. Stage 3 – Organisations in this stage have a democratic view of data as the leadership believes that they cannot drive the ship alone and make decision-making decentralised. Executives have their personalised dashboards, and the executives are continuously pushing for more and more data literacy. These organisations are using data to search for newer ways of collaborating continually. Stage 4 – Organisations in this stage have evolved to a point where analytics get closer to human capability. The analytics inputs are provided more proactively to the executives, and the executives can even ask for their data on the go and get instant feedback. Moreover, the information is available at various touchpoints on demand, be it mobile, web, voice etc. Stage 5 – At this stage, organisations use data analytics and models as assistance to meet their business goal.  Business goals are fed into the analytics platform, which suggests strategies and tactics with constant guidance personalised to the extent that individuals can achieve their goal by simply following the guidance. In these organisations, analytics crosses predictive, prescriptive, cognitive and becomes experiential and act as a personal assistant or a co-pilot. The act of combining business analytics with experimental experiential learning helps us get to Experimental Experiential Analytics. Employees untrained in analytics will be able to use data specifically tailored for their need, identified by using AI and Machine Learning. Machine Learning Algorithm trains itself by experiencing the needs of the individual and then nudges the individual with solutions and information continuously until the goals are achieved. The customized nudges drive quick adoption and Experiential analytics can help executives better utilise information and make more data-derived decisions. The focus is on the delivery of personalised discoveries and insights for executives’ that also help driving better adoption. Creating a data-driven culture is a journey that cannot be reached overnight. Adopting an AI-based solution can stimulate behaviours that drive adoption even amongst the non-analytical workforce. During this lockdown phase embracing remote working culture by the non-data savvy workforce can catalyse the path for experiential analytics evolution and adaption to reach the fifth stage or the ‘Analytic Nirvana Stage’. The views/opinions expressed in this article are the author’s own and do not reflect the views/opinions of the organisation where the author is employed.
The world was hurtling towards destruction with environmental degradation, deforestation, increased carbon footprint and pollution levels, a system reboot was inevitable. Then, the COVID-19 pandemic bought the world to a standstill, forcing everyone to stay indoor and companies to adapt and evolve themselves to accept remote working as a norm. Overnight, business leaders and team […]
The world was hurtling towards destruction with environmental degradation, deforestation, increased carbon footprint and pollution levels, a system reboot
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["Replicate", "Go", "machine learning", "AI", "data-driven", "ViT", "analytics", "GAN", "R", "analytics platform"]
["AI", "machine learning", "analytics", "R", "Go", "GAN", "ViT", "analytics platform", "data-driven", "Replicate"]
An experienced Business Head with demonstrated history of entrepreneurial style of working with over 20 years of work experience. He has diverse experience in financial sector that includes Financial Planning &amp; Analysis, Market and Product Risk Analysis, Asset Management, Pension Funds, Equities and Data Modelling &amp; Analytics. His contribution has been in developing software products and recommendation engine for mutual fund analytics being used by lots of mutual fund companies, large distribution houses and banks. He has served in reputed organizations like ICRA Online, CRISIL (research and analysis); worked as Head- Research at Citibank, Head-Products at Standard Chartered AMC, CEO at IDFC Pension Fund; Project team for setting up IDFC bank before taking over the responsibility to set up analytics business unit for IDFC Mutual Fund. In current role he has successfully setup data warehouse by integrating data from various sources to power the MIS and business analytics platform. Vikash is a strong finance professional with an Executive Program in Business Analytics focused in data Analytics and Data Sciences from Indian Institute of Management, Calcutta; MBA – Finance from India Institute of Finance; BSC (Physics Hons); CFP from FPSB India; cleared CT1, CT2, CT3 from Institute of Faculty of Actuaries, UK