Beringer Technology Group

Building high-value prediction algorithms using machine learning is easy; however, the key challenge for businesses today is to build the prediction model that suits their business objectives. With static prediction models that are adverse to manipulation, businesses usually rely on guesswork to make decisions. With Innodatatics, gut feeling in business decision making is a thing of the past. In the burgeoning big data analytics market, Innodatatics stands out with their ability to deliver a unique combination of analytics and subject matter expertise in diverse industries to provide well-rounded solutions to clients. “We have domain experts who are also statisticians and have great acumen toward IT that makes us a go-to partner for data analytics,” says Bharani Kumar, Director of Innodatatics.

Bharani informs that businesses today are looking for solutions that allow them to forecast market conditions accurately and complete mundane operations automatically. Catering to these requirements, Innodatatics continuously monitors market developments—that the client operates in—and ensures their prediction model is aligned with the market dynamics. Backed by the domain knowledge, the Innodatatics continuously monitors the market developments and alerts clients to update their prediction model by taking into consideration the most recent data.

Innodatatics doesn’t just stop at just understanding the challenges faced by the client but also fully comprehends the impediments experienced by the industry and the geography that the client belongs to. Conducting a thorough market research, including regulatory developments, Innodatatics records those business problems. They have a sophisticated research lab where a team of researchers—Ph.Ds—leverage various open source tools to extract deep insights on the prevalent challenges and provides clients leading-edge technology solutions to address those challenges. In case a client doesn’t have relevant data, Innodatatics takes the initiative of capturing the data on their behalf. To illustrate, if a client is suspecting fraudulent transactions but doesn’t have enough data to prove it, Innodatatics leverages their analytics expertise to build prediction model by taking into account the location and timing of the suspicious transactions. By comparing the distance between locations of transactions vs. the time lag between those transactions, they allow clients to quickly identify whether a given transaction is fraudulent or not.