Self-Service Analytics – Racing to Meet Growing Expectations

Self-Service Analytics – Racing to Meet Growing Expectations

Self-Service Analytics – Racing to Meet Growing Expectations
In a speech at this year’s Consumer Electronics Show, Ford CEO Mark Fields declared, “We’re not only a car-manufacturing company, we are a technology company.” And, he added, “As our vehicles become part of the Internet of Things, and as consumers give permission to us to collect that data, we’ll also become an information company.”

As entirely new economies spring up and old business models are broken, many companies, like Ford, are redefining themselves, moving their traditional goods and services.

But companies are telling us they can’t do this unless they find ways to fully leverage the oceans of data now available to them.  It’s no longer enough to use data to create efficiencies – companies say they need to find game-changing insights. If they are to redefine themselves, they say, they need to find those insights in every corner of the business – Finance, HR, Marketing, Research and Development, Supply-Chain Logistics…the list goes on.

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The challenge executives’ face today is that there isn’t enough data science talent to go around to analyze all available that data. And so companies are finding they need to “democratize” their data and analytics – putting them directly into the hands of every business user. This business imperative is one of the driving forces behind today’s rapid growth in of self-service analytics.

At the same time, the desire for self-service analytics is coming from the business users themselves. In our on-demand, technology-infused culture, many employees have come to expect instant access to the information they want.  They’re no longer content with having to go through intermediaries – in a time-consuming, cumbersome process – to ask data questions such as, “How do I improve my on-time delivery rate?” or “How do I optimize factory through-put?”   Employees are becoming much more comfortable with using new self-service technologies – and they want them in the workplace, to help them do their day-to-day jobs.

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Broad changes in organizational culture feeding into this trend. For example, we’re seeing an increased collaboration among the people who own the data. They’re more willing to break down silos and share information with other departments and business units. At the same time, business intelligence teams and other analysts are more willing to use open-source tools, and to get answers from data science – rather than relying exclusively on traditional methods. These changes are removing key roadblocks to the spread of self-service analytics. With data now flowing more freely throughout organizations, more people are getting a chance to use it.

Data Science Steps In to Meet the Need

As all these factors set the stage for the new generation of self-service analytics, advances in data science are filling the need. New data storage and management approaches are now making it possible for organizations to fully integrate their entire repositories of structured and unstructured data – and to include any number of outside data sources.

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