Every month, the business world becomes more and more adept at working with large quantities of data. Companies are collecting information more than ever - about their clients, their customers and the economic factors around them - and they're still exploring the full potential of what they can do with it.
That brings many questions, ranging from how data applies to fields like education and commerce to how the IT community can improve its analytics. Slowly, the answers to these questions are beginning to trickle out.
Here's a recap of the month's biggest stories in big data.
Data transforming Colorado education
Analytics are slowly making their way into public education in America. School superintendents are slowly coming to realize that they can improve their institutions by collecting more data and acting on it. According to Information Week, Colorado school IT czar Daniel Domagala is one leader of this movement. He's worked to gather and analyze information about 860,000 students, 2,000 schools and 178 districts.
"Information doesn't tell the whole story, but we've been able to get a glimpse on what's working or not working," Domagala told the news source. "We probably have a ways to go to where we can definitively say here are things that are proven to work and here are things the data is not supporting."
Business schools are in the data business
Speaking of schools, it's a two-way street - data can help education, and education can help data. Newswise recently reported that universities and their business schools are creating new fields of study to help their students contribute to the big data revolution. The University of Iowa is one prime example - Jeffrey Ohlmann, associate professor of management sciences, is working on a new major called Business Analytics and Information Systems (BAIS).
"In a sense, the BAIS major combines topics from computer science, industrial engineering, mathematics, and statistics and teaches them through the prism of business problem solving," Ohlmann said.
Working to improve mobile commerce
Companies have already used big data to sharpen their strategies for selling merchandise online. Improving sales via tablets and smartphones is the next frontier. According to CIO, this has become a primary area of focus because it's essential for selling to younger consumers. The news source noted that per Accenture Interactive survey data, 72 percent of people between ages 20 and 40 use mobile devices for comparison shopping while in retail stores.
Mobile retailers will look to capitalize on this trend, but they also need to avoid being too invasive. If they pry too far into consumers' spending habits, they risk alienating potential customers.
Brick and mortar matter too
Data-driven sales shouldn't be limited to high-tech purchases. The Silicon Valley Business Journal noted this past month that retailers can also use real-time analytics to improve in-person shopping revenues. eBay is working on an application that uses Bluetooth technology to identify customers and compile "profiles" based on their spending histories. David Geisinger, eBay's head of retail business strategy, told the news source that the new technology will be available soon.
"We've modernized the little black book," Geisinger said.
With this new mechanism, stores can improve their merchandising, sales tactics and customer service approaches.
Quality still a struggle
While organizations of all kinds have the potential to accomplish more with data, they still have a number of questions to answer about data quality first. According to Information Management, CIOs need to be more careful.
"In the world of marketing data science, IT may not be completely relieved of its data quality duties, but the game has changed," said Michele Goetz, an analyst at Forrester Research. "Lack of or reduced IT data quality services are not always a barrier to big data when in context of relevant quality customer insight. Yet, IT still needs to support and certify data quality in the access and integration of data."
Before proceeding with analytical initiatives, tech leaders need to check their data clusters for accuracy, completeness and sample size. Only then can they achieve the best results.
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