Bhavana Chamoli Looks at How Data Modeling is Used in Different Industries

Analytics data

The Information Age has ushered in transformative advancements in telecommunications and computing. This includes advancements even a few decades ago may have seemed more along the lines of science fiction, such as the ability for ordinary people on different sides of the world to have real-time video conferences or immediate access to an astonishing amount of information on just about any topic. However, it has also given rise to an enormous and incomprehensible volume of data. Fortunately, that is where data modeling comes into the picture.

In essence, data modeling is about creating accurate representations of data objects within a database, including their associations and relationships. Once this representation has been achieved — and the process can be quite complex and time consuming —insights are leveraged in different ways, such as exploring and explaining high-level structures and concepts. Physical data models, which are widely used in Agile software engineering environments, can help design the internal schema of a database, including the relationships between columns and tables.

Bhavana Chamoli, who is currently a developer, investment research and trading at MIO Partners in New York, believes that data modeling is a powerful tool. She explains that given its capacity for helping turn raw information into actionable intelligence, data modeling is widely used in several industries, including healthcare, finance, and retail.



In the healthcare space, data modeling is being used to simplify, streamline, and standardize the transmission of data from various sources, ultimately so that researchers can access more reliable and reproducible information. The use of data modeling can help improve clinical effectiveness, treatment outcomes, and generate deeper and more reliable insights into the risks and benefits of medical care and methods, adds Bhavana Chamoli.



In the finance space, data modeling uses linear, non-linear and density models of financial data to conduct robust performance evaluation, support risk management initiatives, and forecast future trends. Financial data modeling is one of the most exciting and dynamic applications of advanced data modeling, particularly when it applies to fintech analytics such as value-at-risk estimation and portfolio estimation, says Bhavana Chamoli.


In the retail space, data modeling is used to help predict purchase intent and drive sales and revenues, optimize pricing, streamline back-office operations, and optimize customer experience. Bhavana Chamoli says that data modeling in retail has gone from being a competitive advantage to a fundamental necessity. Retailers, whether they use online channels, offline channels or both cannot afford to rely on flawed or incomplete data when it comes to choosing product lines, guiding customers, and ultimately building loyal, profitable customer communities.


At the same time, data modeling is undergoing some key changes and developments due to factors such as the growth of machine learning models, AI, data lakes, the cloud, blockchain, and non-relational/NoSQL data stores. There is also an enhanced focus on security; particularly with respect to handling sensitive data and how it may be breached by external bad actors and internal rogue users.

Final Thoughts from Bhavana Chamoli

While data modeling is an established and proven process that is widely used by enterprises and governments around the world, there is still so much potential in terms of what can be gleaned and revealed. “The future looks very bright for data modeling and I’m excited to be a part of this evolution,” says Bhavana Chamoli.