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.
Data democratization in action
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.
“Whether you’re a small hotel or a global hotel chain or casino chain, size doesn’t matter,” says Banerjee. “You will still have access to the same sophisticated techniques such as deep learning, or reinforcement learning, which, until recently, were only available in the labs of Google or Facebook.”
Other examples of analytics at work are everywhere. Everyday business users can interact with dynamic data sets in real time to ask and answer questions. These questions are about the impact on sales, shipments, clinical outcomes, customer service, network outages, next-best offers, website traffic, highway congestion—the list goes on and on—that different combinations of variables have. Essentially, if you can think of a use case, very likely an algorithm is available to solve it.
And while using analytics all the way down to the granular level of an individual customer or user is relatively new, it’s not unheard of. Still, only the most sophisticated and forward-looking companies are investing in infrastructure and personnel to move this type of knowledge out of the executive suite and into the hands of front-line employees. And is the decisions of these employees that can have an immediate impact on the bottom line.
Access to data and highly dynamic business decisions
As the use cases for analytics increase, so too does the range of users who have access to it.
According to a 2016 Analytics magazine article, “Historically…useful business data is just confined to IT folks and a handful of senior executives who need to make decisions based on that data…. By freeing themselves from data silos and the traditional practice of data collection, storage and access, agile businesses can not only improve their dynamic decision making, but they can also expedite enterprise data integration and decentralization.”
And, as more companies such as LodgIQ broaden access to analytics, this type of decision support and market insights is expected to become just another part of the standard manager’s toolset—regardless of what industry they are in.
“Foundationally, I believe these techniques, in the next 10–15 years, will enable business to unlock a world where systems continuously learn from data, self-adjust, and tune themselves for optimal performance,” says Banerjee.
Listen to "All the Data That's Fit to Analyze,” a podcast with Banerjee, to learn more about the democratization of data. And for more stories from The New Builders podcast, find us on SoundCloud, IBM developerWorks TV at The New Builders and coming soon on iTunes. Please send your thoughts, feedback and guest ideas in the comments.
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