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.

By Samuel Greengard

As organizations attempt to navigate the information age, executives are discovering that the biggest obstacle isn't collecting data or finding ways to manage and store it efficiently. It's constructing a framework that allows business and IT leaders to connect all the dots and put all the data to maximum use.

"There is a growing need to analyze many different types of data and to use it to make decisions more quickly and for entirely new types of processes and events," says Vincent Dell'Anno, leader of the big data practice at Accenture.

It's no small task. Across a wide swath of industries and businesses, there's a pressing need to make sense of an increasingly complex and chaotic business environment. Yet, it's crucial to think beyond the vagaries of what big data means and what tools it requires.

Executives must build a strategy and framework that plugs into the "velocity of analysis and the velocity of actions" required in today's world, says Josh Greenbaum, principal at Enterprise Applications Consulting and author of IEEE's "Computing Now" blog.

This new world of business and IT requires radically different thinking, new processes and job skills, and innovative technology platforms to deliver the desired results and return on investment. Big data is also affected by a number of factors, including mobility, cloud computing, social media and a regular stream of other unstructured data.

There's a strong need to build taxonomies and data governance structures for all the data an organization possesses, Greenbaum explains. "It's important to construct a conceptual framework based on the business problems an organization is attempting to solve," he adds.

Framing Data Challenges

Although the term big data has gained widespread acceptance, Greenbaum believes it's not the best way to frame today's data challenges. "Big data is not a clean-cut technology or space," he says. "The term doesn't help businesses build a strategy or assemble the right collection of tools, technologies and assets required to drive business performance."

At the heart of the problem, he says, is that vendor tools and technologies alone cannot address business needs. An enterprise must identify processes, workflows and methodologies that it can use to deliver improved results. In fact, he prefers to call the task "big analysis."

Some disciplines—namely fields such as astronomy, meteorology, oil and gas exploration, and engineering—have long relied on huge data sets to solve problems and build models. But now other organizations are attempting to sift through large and complex data sets in order to glean answers or insights that were once unimaginable.

Along the way, many organizations are looking to build a real-time enterprise that's able to react effectively to changing conditions, events and consumer behavior. They're looking to ratchet up innovation and returns through big data.

Today, sophisticated social listening systems can predict emerging trends and changing tastes in products, services and overall attitudes. In addition, businesses can analyze social conversations to generate leads and marketing strategies.

At the same time, health care providers are turning to sophisticated data analysis systems that gather data from a variety of sources in order to identify patterns. This helps them understand how and when different procedures, therapies and medications work.

To be sure, businesses from a wide range of fields are benefiting from big data initiatives. At Lonza PharmaBiotech, a Swiss global supplier of products and services to the life sciences industry, a growing need to address bottlenecks and latencies in data processing pointed to big data.

The company, headquartered in Basel, Switzerland, wanted to achieve productivity gains through the deployment of self-service business analytics, and enable decision-makers at all levels of the organization to engage in analysis that would create value and deliver cost savings. The firm has approximately 10,000 employees scattered across the United States, Asia and Europe.

In 2011, the company introduced a comprehensive global business analytics program, Adeptia's Enterprise Business Integration Management Suite, to address the challenges in a more sophisticated way. "The goal was to transform Lonza from a company that was rich in data but poor in information," explains Peter Mueller, manager of the business analytics global program. The initiative focused on three primary areas: metrics and operations data, smart information that could guide processes, and insights that could guide major business decisions through predictive analytics.

For example, Lonza now uses big data to understand the effectiveness of an internal Corrective Action Preventative Action (CAPA) process. It involves a highly structured approach that's designed to identify the root cause of process issues and problems related to nonconformity. (The method is required by certain regulatory agencies.)



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Samuel Greengard is a contributing writer for Baseline.

 
 
 
 
 
 

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