Disruption is an interesting thing. On one hand it can open new doors where there were not any and on the other it can pose some serious problems for those who are not ready to adapt. Data analytics is a disruptive technology if you consider the how widely and with what brutal speed it has spread across different industries. A lot of enterprises banked everything upon this. Many of them have reaped the benefits, some have had to close shop altogether. We will look at some such stories and try to figure out what went right and what did not.
Why analytics initiatives seem to lack results
66 percent of Indian manufacturing firms have ranked big data and predictive analytics as their top priority for investment in the next couple of years. Be it supply chain management, logistics, or market research, leaders and CIOs are counting on data analytics. What are they getting out of it?
Manufacturers today have applications such as UptimeAI that can predict and mitigate equipment issues in a matter of hours using historical data. It focuses on creating pinpointed alerts rather than a bunch of false alarms. Tools like these, industrial internet of things, and smart equipment have made life easier for a lot of manufacturing houses.
But, yes there is a but. While 63 percent of firms are ready to invest on the hardware to implement this sort of technology only 9 percent have the provisions to make a thorough integration. The problem with most organizations using data analytics is that their implementation is not all pervasive. They optimize the manufacturing unit but fail to do the same with the logistics unit. Some have an incredibly efficient market analysis team but hardly have any optimization in the supply chain. These discrepancies do more harm than good. They keep pouring money into initiatives but do not see results due to lack of thoroughness.
All industries are similarly plagued by these issues.
Industries that are really reaping the benefits
While every industry has its own possibilities of reaping benefits from analytics implementation some just have a sort of innate advantage.
Take the healthcare industry for instance. The healthcare units do not need to worry about data generation and acquisition. It comes from the patients and the healthcare professionals. They can use the patient feedback to optimize services, they can use billing data to understand rise and fall of profits in correspondence with other factors. They can save thousands of hours by using AI tools to prescribe medicines that withstand the patients’ allergies or other conditions. It just could not get any better for the healthcare industry.
Financial institutes are using analytics in a massive scale to prevent loan defaults, targeting customer bases, and to optimize digital security. It has been a win win for them all along the way.
Political parties have used data analytics to great avails. It is a brilliant tool to understand trends and to tap into the pulse of the crowd. Analytics has helped parties optimize everything from their rally routes to slogans to reach and influence the highest number of people. It has made a real difference in the recent elections in both India and the USA.
Learning from failed analyses
It is a well known saying in the world of data science that the best way to predict the future is to learn from the failures in predictive analytics. A lot of companies, in their pursuit for a predictive end forget to focus on the analysis. The key is to focus on the problems you need to solve as well as the problems you find along the way. Predictions should be a by product not an objective. There will be failed predictions. Some predictions will work for a third of the implementing corporation’s branches and back fire for the two other thirds. The analysts have to learn what went wrong.
Strategic best practices
There are few distinct factors that account for most of the analytics failures.
- Reaction instead of response
- Strategy before data
- Quality of data
- Lack of data centric decision making.
- Wrongful allocation of analytics.
Removing these issues can mitigate a lot of disasters regarding analytics.
Decision makers need to respond to the findings of the business analysts and the data scientists. They need to focus on data before placing a strategy instead of creating a strategy and then finding data to support it.
Companies need to focus on the quality of data. And analysts should be more driven towards finding the right areas to apply data analytics. The field is ripe and a lot of businesses need help. This is a very opportune time for aspirants who undergo business analytics course in Delhi. It is time for their careers to take off.