Minimize the Impact of Recession data on Predictive Analytics

If a company can predict its future on the basis of past & current data then maybe no one can stop the company from blooming, but suddenly that data got disturb then what the company will do? When big data came into the market business strategy totally changed even medical, sports, education everywhere data played a big role. Business takes it another step. They try to analyze the past and current data and try to establish some kind of connection and make predictions for the future, here comes Predictive analytics.

Models that are blocked from further

analysis; all of them are templates that allow users to turn the historical and

current data into actionable insights that result in great results for a longterm basis. Predictive models that

come in a variety of shapes and sizes:

• Customer Lifetime Value Model: Determine which consumers are

most likely to spend more money on products and services. • Customer Segmentation Model:

Customers are grouped

together based on comparable traits and purchase habits. • Predictive Maintenance Model:

Estimate the likelihood of critical

equipment failing. • Quality Assurance Model: When supplying items or services to consumers, look for and prevent faults to minimise dissatisfaction and additional expenses.

Then Covid-19 hit and it was the first recession which disrupted the Big Data of the country. Everything from consumer behaviour to supply networks has been affected by the Covid-19 epidemic, and the economic consequence is creating even more changes. The discipline of data analytics is grappling with a difficult problem like: how to utilise historical data to forecast future behaviour in the

face of ambiguity. Deloitte, Lendingkart and many more organisations give their concern on this topic.

As per Deloitte India “From customized marketing to supply and demand patterns, finance to customer service,

speculation algorithms built on behavioural patterns and foresight. Historical data has always played a role in early drivers, and these models provide valuable value and robust decision-making tools. However, Covid-19 has affected us in all aspects of life, from how we live, how we work, how we use, how we move, and thus disrupt data patterns significantly.”

The pandemic unpredictability nature poses a big threat in predicting the future and hence the anxiety on the reliable past data in decision making process for present as well as future. From personalized marketing, supply chain, even customer service just one step behind and that is how to read that disrupted data. Data models vastly rely on past data for prediction and modifying that model now poses a main challenge for any organisation. Though many come up with various strategies but is this going to be a game changer the question remains the same. In an article Ledingkart said “They take this challenge as an opportunity to imitate these many aspects and conditions in its underwriting model that will be updated in line with growth and changes in the ecosystem. This will also strengthen their risk assessment capabilities and create stronger early warning systems during those tests.”

So, minimise that gap need some serious work, from deloitte India to many data science expert gives their various opinions in this matter, they are as follow:

o Plan ahead on data: Planning is everything, as COVID-19 may have far more impact than anyone can imagine and can change demographic business. Identify those emerging challenges and prioritize cases by case. For example, when the education system faces a huge challenge in offline class they shift to online and google take this as an opportunity and launch Gmeet for their business.

These abilities for rapid data proliferation and data synchronization give business power critical decision making. o Re-prioritize data initiatives:

Cutting cost is always the main aim for many organizations, now changing data models puts extra burden for the

organization. Better reprioritization of initiative can help. For example, Kantar IMRB adopted a call-based research policy in the current scenario

which is more kind of optimize data structure and cost

effective.

o Increase adoption: The process of understanding the data in action should be faster and

faster. Immediate adoption will be facilitated by investing in agile and construction data solutions. Additionally, agile solutions that will help data scientists focus on faster data

usage, processed data, and develop understanding. o Scenario Planning: Instead of focusing on a single forecast or a basic outcome, businesses should plan multiple opportunities. Advanced analytics can help businesses make better decisions faster, but they have to throw in models, face skewed data, transform and stay rational. o Business Translators: Organizations need not only information scientists and

information engineers, but also “business translators” who can bridge the gap between business and business statistics, which can bring businessfocused thinking. This will help to align the company’s goals, reduce risk, and increase resilience.

In conclusion, with data, a culture of collaboration across Technology, People, Processes, and Equipment can reduce the impact of COVID-19 on a company. Organizations should increase their ability to quickly use and analyze data. To reduce the impact of COVID-19 today, one must predict what will happen as a result of COVID19.

AKSHAY GHOSH MBA 2020 - 2022