Companies Grapple With Big Data Challenges

By Samuel Greengard  |  Posted 2013-10-29 Email Print this article Print
 
 
 
 
 
 
 
big data challenges

More enterprises seek to leverage the opportunities offered by big data, but constructing a framework that lets them put the data to maximum use is a challenge.

In the past, internal analysts were forced to sift through large volumes of data in an attempt to identify issues and potential preventative actions. However, some measurements and analysis weren't possible.

That's no longer the case. The company now funnels data from dozens of sources through the analytics software. The results are visible instantly from a series of interactive scorecards and dashboards, and managers and others can take immediate action.

"We can see the effectiveness of our quality system—something that wasn't possible in the past," Mueller says. Moreover, employees using the software can run what-if scenarios and understand how different decisions and actions impact results. "We have the data analysis tools to understand quantitatively what would potentially occur if we decide to make a change," he adds.

The Adeptia system extracts and collects millions of records—both structured and unstructured data— from databases, enterprise applications and other sources across the company's facilities, Mueller reports. Employees can view results based on a number of criteria, including how countries or local offices are performing. The system also standardizes reporting and processes so that there's a high level of consistency across the company.

"The data is different, but the scorecards and information assets all look the same," he says.

Brewing Better Data

Green Mountain Coffee in Waterbury, Vt., is another company that's embracing big data. The firm, which has 20 different brands and more than 200 different beverages, has embarked on an initiative that taps into both structured and unstructured data from audio and text analytics to boost intelligence about customer behavior and buying patterns. The system relies on a Calabrio Speech Analytics solution to glean insights from multiple interaction channels and data streams.

"In the past, when customers called into the contact center, there were questions we couldn't answer," recalls Nate Isham, a level three network engineer. "They included how many people were asking for a specific product, which products they [more] questions about, and which products and categories generated the most confusion." Big data and analytics produced insights along with "solid information to make decisions."

As a result, the company is able to produce materials, Web pages and database entries that help representatives do their jobs more effectively. Moreover, "We are able to catch issues quicker and prevent them from becoming bigger issues for our customers," he says.

The company is now plugging the data into everything from manufacturing and operations to marketing. That could drive changes to areas including product packaging to advertising campaigns. The end goal, Isham says, is to "drive action, not only insight. We are attempting to transform data into information and then knowledge. When we reach that point, we're able to take actions that benefit the customer."

Enterprise Applications Consulting's Greenbaum says that organizations must ultimately adopt a mindset that focuses on business results: "The real issue isn't, What's in my data? It's, What analysis do I want to do?" It's not so much about the size of data sets as it as how to connect them in more innovative and creative ways to answer key business questions. In some cases, this might translate to small data sets; in others, vast data pools.

Within this framework, IT must support the line of business. It's critical to break down silos, ensure data integrity and quality, and construct systems that allow data to flow through the enterprise.

Achieving a real-time enterprise is the end goal, Accenture's Dell'Anno points out. "The idea of data moving through the enterprise in a linear way and landing in a data warehouse is changing," he says. "There's a need to identify choke points and assemble a cross-section of data elements from across the enterprise."

In many instances, this requires a fundamental rethinking of data sources and types. "The issue isn't whether data is structured or unstructured," Dell'Anno says. "It's figuring out all the different data sources to tap internally—and from the Internet and beyond—to help you shape context about your clients and customers."

As organizations grapple with continued data growth, the proliferation of clouds and increasingly tight IT budgets, the task of harnessing big data won't get any easier. Determining which technologies to use, including popular open-source tools, can also prove challenging. In the end, he believes that there's a need to re-examine everything from infrastructure and data collection strategies to the types of skill sets the organization seeks from outside and develops through training.

"The goal is to find the right combination of data, thinking and technology to fundamentally change customer relationships and the business," Dell'Anno concludes.



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Samuel Greengard is a contributing writer for Baseline.

 
 
 
 
 
 

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