Unleash the Potential of Data Led Enterprise Intelligence, CIO News, ET CIO

The benefits that can be gained from data-driven insight are immeasurable. They promote a more efficient and effective asset management strategy and, most importantly, play a key role in helping clients achieve their sustainability and net-zero goals. Data-driven intelligence is a bit of a roadblock as it tries to create operational efficiencies, improved customer service, and cost efficiency. It tries to do all this in a significant way by:
identify new patterns and trends and devise new ways to empower the service companies they can provide to their customers and the ecosystem. Data has been the beating heart of companies for several decades. Now the emphasis is on understanding the future, the art of possibility and predictability with data.

Moving forward with data

Banks often had a lot of data and there were often conversations about banks not doing much about it”, this is what Deepak SharmaCDOKotak Mahindra Bank, had to say. Over the past 5 years, companies have witnessed a transitional and transformational shift. Initially it was about building a data infrastructure using data lakes, big data warehouses or cloud adoption.

The next stage comes in understanding the value companies derive from it. “That’s the stage we’re all at where we’ve always looked at historical data, but it only helps so much because you have to continuously validate the data with current behavior and trends,” he adds. Experiment is now the most important element.

Organizations have now moved from a mathematical approach to a more machine learning-based algorithmic modeling. Whether it’s clicked data, browsing data or map data, the primary goal of organizations is to extract value from the data.

“For example, giving real-time credit decisions at the point of consumption is one of the significant ROI-driven use cases that the financial services firms have been able to build,” Deepak said. Some of these opportunities have given organizations ample room to experiment. Ensuring the fraud and risk paradigm has been a challenge for banks. “How we make a real-time decision to allow a transaction, take a stepping stone, or take a remediation action is also incorporated into our data models,” Sharma explained.

Improving operational efficiency

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

“In general business decisions, we have an internal latency where you can’t quickly collect the information in an integrated way. The second is the latency of analysis, where you cannot quickly analyze this information. The third is the latency of decisions where you are not able to make quick decisions based on the decisions, and the fourth is the latency of actions where you have decided something but then do not implement it quickly.”
four verticals, as mentioned by Yogesh ZopeGroup CIO & CDO, Bharat Forge

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

Applying ideal forms of data-driven intelligence

Businesses are becoming more data-driven, whether in terms of making business decisions, personalizing offers or looking for targeted things for customers. There are many untapped opportunities arising from data-driven 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-driven intelligence in these categories.

“The way we’ve looked at it is in two phases. First, we started building the data science capability closer to the business. The second part is about innovation and testing,” exclaimed Sharma. Obtaining the ideal mix of consumer behavioral insights and structured or unstructured data is the primary necessity to apply ideal forms of data-driven intelligence.

It’s also about creating the data-driven mindset and culture in the bones of organizations and then taking it further in achieving the data-first goals. However, one cannot rely on data all the time. There must also be the perfect implementation of creativity. “Everyone should think of data as a variable before looking at creative outcomes,” Sharma said.

Ensuring action in a preventive mode

Businesses are now realizing and understanding the importance of connected ecosystems — from customers and in-house factories to last mile service implementation, it’s all been impacted since the pandemic hit. “Bringing everyone to a common platform and doing what-if analysis to answer business questions is required,” Zope said.

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

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