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Daniel Raskin

Drinking from the “Data Fire Hose”

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I’m two weeks into my new role at Kinetica and I am drinking from the proverbial “fire hose.” Every day I’m reading about the technology explosion across business analytics, artificial intelligence, and big data platforms. The innovation is amazing, and what excites me more is the market opportunity driving it.

The world is dramatically changing! As more users, devices, and things come online, there is an opportunity to transform how businesses engage with customers—an opportunity to create digital relationships that drive category leadership, improve customer satisfaction, and propel growth. This opportunity is already disrupting entire industries.

This couldn’t be more true than in the realm of banking. Traditional banks are facing new levels of competition from virtual banking upstarts with no need for branches or paperwork. Companies like Simple, Ally Bank, and Atom Bank are embracing the latest in digital tech, artificial intelligence, biometrics, and analytics to challenge the traditional banking behemoths.

Additionally, changes in banking regulations are driving more digital competition. For example, in the UK, the new Open Banking Standard will require that banking data be shared through secure open APIs so that customers can more effectively manage their wealth. In other words, banks have to embrace new digital innovations and compete in a world where there is more data transparency than ever before.

The point of differentiation—whether in banking or any other industry—is no longer simply about who owns the data, but rather the fluidity of the data and how it is used. Those companies that invest in the tooling, operational processes, and product offerings that derive more intelligent insight from data will be best positioned for innovation and category leadership. However, the data challenge has grown in complexity.

There is more data being generated than ever before. The digital exhaust generated by the Internet of Things (IoT) is creating massive volumes of “digital smog.” There’s more data today than humans can process, yet the IoT is still nascent. As a result, we need to reconstitute how we ingest, process, and analyze fast data and operationalize machine learning for deep insight. This requires a converged data platform that serves business owners, data scientists, and technologists. It requires not only innovative technology, but an operational process around machine learning that includes data cleansing, model development, model training, model evaluation, model deployment, and auditing. The end result: a market-leading approach to generating business insight that differentiates…and a next-gen data foundation built for leading customers into the future!

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