Find Your Big Data 'Question Space'Find Your Big Data 'Question Space'
Ask the right questions, and your data will deliver the right answers, says one analytics pro.
February 3, 2014
10 Biggest Tech Disappointments Of 2013
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Getting big data to deliver usable insights is an elusive goal for many companies. In fact, nearly half aren't achieving the level of value or return on investment (ROI) they had expected from their big-data platforms, according to a recent study by research firm Wikibon.
So what's a business leader to do? According to Wayne Applebaum, vice president of analytics and data science for Avalon Consulting, LLC, a Plano, Texas-based firm that advises more than a hundred Global 2000 enterprises on data management issues, the key is to find your "question space." Ask smart, enterprise-focused questions that enable big data to have a positive impact on business and marketing decisions.
This approach might seem obvious, of course, but implementing it can be tricky. A business, for instance, might know which questions to ask, but not where to find the answers, Applebaum told InformationWeek in a phone interview.
"I worked with a bread manufacturer who said, 'If I can reduce my fresh bread returns by 1%, it'll save me $10 million dollars annually,' " said Applebaum, a 30-year data analytics veteran. "And I had a wire manufacturer who said, 'If you can give me the information that I need on scrap, I'll deliver $50 million dollars to the bottom line.' "
[Read about last year's biggest analytics trends: Top 10 Big Data Stories Of 2013.]
He added: "The idea of a question space is, how do I define all the related questions for my analytics so that I can take the information I'm getting throughout my enterprise?"
Enterprise analytics today often consists of a lot of small projects, he said. Organizations will spend money on big-data projects focusing on, say, sentiment analysis or opinion mining, which extracts subjective information such as consumer opinions from structured and unstructured data sources.
"A typical big-data question that (Avalon client) wanted to answer was, 'What's the sentiment analysis on my new cellphone?' That is an interesting big-data question where they can monitor websites" to find answers, Applebaum said.
But a deeper analysis revealed a larger set of questions. "If we look a little further, what they really want to know was, how does (consumer sentiment) affect my marketing and sales projections?" Applebaum recalled.
If the sentiment analysis reveals good news, a business might decide to adjust its marketing and sales projections accordingly.
"Other than patting myself on the back, I now have to incorporate that (news) into my production planning, and my production planning has to check (the company's) inventory to see if they can handle it," said Applebaum. "And depending on what happens then, they may have to contact their vendors to see if they can supply the necessary inventory."
He continued: "What we started out with was a simple question of sentiment analysis on the consumer side of the business, which in turn generated a number of other questions that rippled through the enterprise, and ended up on the supply chain side."
It's important to take an enterprise-wide view: How will this information benefit the entire organization? This approach helps a company derive useful information from its big-data analytics, added Applebaum.
"If you spend a little bit of time -- and that's the key, it doesn't take much time to sit down, to brainstorm -- you (can) figure out what your question space looks like," he said.
Applebaum added: "Figuring out your question space is really the best way to jumpstart your analytics program. It helps you look at what you need to do going forward."
You can use distributed databases without putting your company's crown jewels at risk. Here's how. Also in the Data Scatter issue of InformationWeek: A wild-card team member with a different skill set can help provide an outside perspective that might turn big data into business innovation. (Free registration required.)
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