How technology advancements contribute to the democratization of data

How technology advancements contribute to the democratization of data

How technology advancements contribute to the democratization of data

In the past, the application of data and analytics to inform decision making was only available to the largest companies with the biggest IT budgets, lots of spare compute cycles and a few high-caliber data scientists on the payroll. But data is everywhere these days, and when you couple data with the analytics necessary to actually do something with it—and that something has real-world implications—then you have a decision-making aide that people can appreciate and actually use.

With the help of today’s powerful chip sets, cloud architectures and advanced analytics processing engines, what started out as information overload is being transformed into just the right amount of knowledge. And that knowledge is applied to just the right problems at just the right time to have a significant impact on business outcomes. That’s what the democratization of analytics is all about: once-big-budget capabilities becoming available to a wider range of companies and applying them to new use cases.

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Somnath Banerjee, CTO at LodgIQ, described in a recent interview with The New Builders podcast how LodgIQ wants to provide the hospitality industry with access to analytics so that hotel managers can better set pricing to maximize occupancy rates and profitability. The hospitality industry historically is not the most analytics-oriented industry. Factors such as time of year, local events, competitor specials, cancellations, occupancy, weather, conference bookings and, more recently, new entries into the marketplace such as Airbnb and other nontraditional lodging providers, all affect the rate a hotel operator can charge. 

“Currently, there is a lot of manual review of this data, and there’s not as much of a mathematical approach as we want,” says Banerjee. “We want to collect this data, put it in a big data repository and use modern, scientific, statistical and machine-learning techniques to accurately predict the forecast and determine the pricing recommendations.”

With the advent of open source analytics processing engines—Apache Spark—and databases—MongoDB—and infrastructure, LodgIQ is able to provide analytics to any hotel, from large chains to intimate boutiques.

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“Whether you’re a small hotel or a global hotel chain or casino chain, size doesn’t matter,” says Banerjee.

 



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