Self-Service Analytics – Racing to Meet Growing Expectations

by   |   December 5, 2016 5:30 am   |   0 Comments

Peter Guerra

Peter Guerra

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.

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.

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. Business users are no longer limited to a handful of data sets related to their areas of specialty – they can look for insights in all the available data.

New analytic architectures are also making it possible for business users to ask questions of that data in different ways. Instead of testing hypotheses, they can ask open questions of the data to find the kinds of hidden patterns and connections that can be so valuable. They can let the data “speak for itself.” This is critical if business users are to freely explore the data.

These advances are integrated into new self-service tools.  Some feature easy-to-use analytic modules that perform complex workflows behind the scenes. Average business users can ask questions in plain English – natural language processing does the work for them.

With traditional self-service analytics, business users who don’t know how to code cannot do much more than generate relatively simple reports. For example, a dashboard might show a sales analyst that his company generates $100 per transaction. It would be difficult for the analyst to explore how the company might raise that to $150 – this typically would call for a heavy, manual data analysis effort through an intermediary.

But new self-service analytics are giving average business users access to advanced analytical approaches – allowing them to complex questions and get answers back within seconds. For example, the sales analyst might plug in various sales levers – such as the impact of free shipping – and then use machine-learning predictive analytics to find the optimal strategy.  What makes these advanced analytics so effective is that they’re not limited to a small number of relational databases – their queries can cover vast amounts of data, both structured and unstructured.

New self-service analytics are transforming how data scientists work as well. Using pre-built modules, data scientists can conduct higher-order types of analysis much faster than ever before. This speed makes it possible for business users, working in teams with data scientists, to more freely explore the data. They can look for nuances, ask any number of follow-up questions, and pursue hunches wherever they might lead.

Where Self-Service Analytics Are Headed

This is just the beginning. Future self-service analytics will use artificial intelligence to make another quantum leap – they won’t just tell us what we think we need  to know, but what they think we need to know. They’ll tell us the kinds of questions we should be asking, and they’ll show us the data where we might find the answers.

Advances in self-service analytics are rapidly closing the gap between the business user and the data scientist. In the future, we might all be data scientists.

 

Peter Guerra is Vice President leading Booz Allen Hamilton’s Data Science commercial team. He has 15 years of experience in creating big data and data science solutions for government and commercial clients. His specialty is in highly available, large-scale distributed systems, data science, and advanced analytics to solve our clients’ hardest problems. Follow him on Twitter @petrguerra.

 

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