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Realising the importance of data, Indian enterprises are leveraging it to enhance customer experience, employee productivity and business growth. According to Forrester, insights-driven companies will earn $1.8 trillion by 2021.
This journey of maximising data starts with the building of a data lake.
Insights drive productivity, cost efficiency, newer opportunities
“A massive data lake aggregates data from all our systems and third party sources,” says Bharat Krishnamurthy, CTO, Exide Life Insurance.
Krishnamurthy powered data with a machine learning model to predict the documents required from customers to process an insurance.
This seamless experience extended to his field agents who could process the documents without any lag. In Exide Life Insurance’s case, the machine learning model also helped predict the persistency of customers in paying premiums for the next year.
Healthcare has been equally geared up to implement an insight -driven culture to arrive at decision making faster and reducing the cost of patient care.
Santosh Rathi, VP, Columbia Asia saved Rs 7 crore last year with optimization of medical assets based on the data generated by the healthcare chain.
Relying on the dataset — consisting of a particular equipment’s business utilization from the platform — the total cost of ownership and the total cost to maintain the equipment which rigorously monitored month-on-month. This helps Rathi to arrive at a decision whether to keep, shift or discard a particular medical equipment, thereby driving cost optimization for business.
“Now I can see a trend where a particular manufacturer gives me that kind of cost vis-a-vis clinical operation, doctor’s ease of use, and the reliability of the doctor with respect to the equipment,” Rathi says.
Insights derived from structured data also helps the manufacturing sector to come up with newer initiatives, explains Beena Nayar, Head-IT, Forbes Marshall.
“Several years of data has been captured through IoT enabled sensors and different technologies. We are in the process of building a data lake and analyzing it. We have built one level of analytics, now we focus on the next to enhance it,” she says.
Building predictive analytics can help with monitoring the parameters of flagship assets of the company and bring in corrections real time for efficiency.
Data-driven journey is a bumpy ride
Though the benefits to be derived as enormous, Krishnamurthy lists some of the practical challenges that enterprises encounter in their journey to leverage data.
Data consolidation is the first hurdle. “One of the challenges is to have a single view of the data. The challenge is also to ensure that every system across the organization represents data in a uniform way,” he explains.
Krishnamurthy points out that it is important to have a common data dictionary across the organization so that every department be it finance, sales, analytics, or marketing refers to a particular terminology with the exact same definition.
Another major challenge in the whole exercise is ensuring security and access control around this data. “It is a continuously changing ecosystem with new sources of data coming in all the time, new partnerships being made, the third party data sources contributing to the database,” he says.
Technology leaders believe that the data consolidation and providing uniform view is a change management exercise in itself. It is therefore essential to build a suitable environment for such experiments to thrive.
“In order for the culture of the larger organization to change, it is imperative that data is democratized and made available to everyone in a form which makes sense to the individual and meets their specific requirements,” iterates Vishal Bhasin, SVP-Technology, Viacom18 Media.
To foster the process further, Bhasin set up detailed workshops with different stakeholders to understand the exact requirements from executives, business owners and analysts, operations team, and data scientists.
“After diligently understanding the ask, we curate the data models and generate customized dashboards for different user groups,” he says.
In addition to descriptive analytics, the data engineering pipeline and unified analytics layer also supports predictive analytics ensuing a data-driven, decision-making culture.
Though the idea of transforming to a data-driven model seems enticing to the enterprises, a lot of IT leaders deal with the availability of relevant skill sets. The requirement can be narrowed to data science, data engineering and a sound understanding of the business, says Krishnamurthy. This sets the base for artificial intelligence and machine learning implementation in the organization.
Data science entails having core ML or AI skills and understanding the models and algorithms. An edge above others would, however, lie in understanding the underlying data.
Data engineering involves filling data from across the organization into a form that can be used by machine learning algorithms. Understand business well enough is critical to guide data scientists into defining the problem, concludes Krishnamurthy.