Unleashing the potential of Data Led Enterprise Intelligence, CIO News, ET CIO

The advantages that can be gained from data-driven insight are immeasurable. They promote a more efficient and effective asset management strategy, and, more importantly, they play a key role in assisting clients in achieving their sustainability and net-zero goals. Data-led intelligence is a bit of an Übermensch as it tries to create operational efficiencies, improved customer service, and cost-efficiency. It tries to do all of this in a significant way by
identifying new patterns, and trends, and bringing new ways to amp up the service companies can provide to their customers and the ecosystem. Data has been the beating hearts of businesses for a few decades. Now there is an emphasis on understanding the future, the art of possibilities, and predictability with data.

Going forward with data

“Banks often had lots of data and there were often conversations about banks not doing much about them”, this is what Deepak Sharma, CDO, Kotak Mahindra Bank, had to say. In the last 5 years, businesses have witnessed a transitional and transformational shift. Initially, it was all about building a data infrastructure using data lakes, big data warehouses, or cloud adoption.

The next phase comes in understanding the value that businesses derive from it. “That’s the stage we’re all in where we always used to look at historical data, but it only helps as much because you have to continuously validate the data with the current behavior and trends”, he adds. Experimentation is the key element now.

Organizations have now shifted from a mathematical approach to a more machine-learning-based algorithmic models. Whether it is clicked data, browsing data, or card data, organizations’ primary objective is to create value out of the data.

“For example, giving a real-time credit decision making at the point of consumption is one of the significant ROI-led use cases that financial services industries have been able to build,” Deepak opined. Some of these opportunities have given organizations enough room for experimentation. For banks, taking care of the fraud and risk paradigm has been a challenge. “How do we real-time and take a decision to allow a transaction, take a step-up or a remedial measure is also taken into our data models”, Sharma explained.

Improving operational efficiency

Enterprise manufacturing operations typically capture data from the plant operations, and business systems, that help make fact-based decisions to reduce costs and manage inventory.

“In the overall decisions of business, we have inside latency where you are not able to collect the information fast in an integrated manner. The second is analysis latency where you are not able to analyze this information fast. The third is the decision latency where you are not able to make quick decisions based on the decisions, and the fourth is the action latency where you have decided something but then you don’t implement it fast”, Technology revolves around all these
four verticals, as mentioned by Yogesh Zope, Group CIO & CDO, Bharat Forge.

He further talks about connecting sensors, followed by building interconnected sensors, and then integrating big data, AI, and ML for analysis. Upon completion of the analysis, visualization, and implementation in the form of actions are the next steps. “I think most of the manufacturing companies still haven’t reached the last cyber-physical level but for the first three, even we have deployed various technologies”, he further added.

Applying ideal forms of data-led intelligence

Businesses are becoming more data-led either in terms of taking business decisions, personalizations of offers or looking at targeted stuff for customers. There are a lot of unexplored opportunities that result from data-led intelligence in the manufacturing industry.

There is a saying that ‘Content is the king, but context is the kingdom’. In the context of customer segmentation, persona creation, recommendation engine, digital marketing, and risk-based modeling it is essential to apply the ideal forms of data-led intelligence across these categories.

“The way we have looked at it is in two stages. First, we have started to build the data science capability closer to the business. The second part of it talks about innovation and testing”, Sharma exclaimed. Getting the ideal blend of behavioral consumer insights and structured or unstructured data is the primary necessity to apply ideal forms of data-led intelligence.

It’s also about creating the data-led mindset and culture within the bones of organizations to then further take it forward in achieving the data-first goals. However, one cannot just rely on data all the time. There needs to be the perfect implementation of creativity along with them as well. “Everyone has to look at data as a variable before you look at any creative outcomes,” Sharma said.

Ensuring action in a preventive mode

Companies now realize and understand the importance of connected ecosystems- from customers, and internal plants, to the last mile service implementation, all of it has been impacted ever since the pandemic hit. “Bringing everyone on a common platform and doing a what-if analysis to answer business questions is required”, Zope mentioned.

The advantages that can be gained from data-driven insight are immeasurable. They promote a more efficient and effective business management strategy, and, more importantly, they play a key role in assisting clients in achieving their sustainability and reaching their goals. As a result, organizations may be able to predict any business failure before it occurs, or they may be able to switch to condition-based maintenance, lowering the frequency of site visits.

New Technology Era

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