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The Need for Speed: Data Analysis for Real-Time Decision Making

While there has been intense publicity focused on the sheer volume of data swamping corporate networks, companies seeking to successfully exploit big data must also be able to quickly and easily adjust their analysis efforts, according to a new study.

1movement 150x150 The Need for Speed: Data Analysis for Real Time Decision MakingThe best performing companies are more likely than laggards to allow users to interact with data and to customize their analytics environments to meet their own needs, notes IT research firm Aberdeen Group.

Leading companies report that they find the information they need to inform decisions 85% of the time; that’s a stark contrast to less successful companies that report they only find the right information 59% of the time.

Moreover, it takes leading companies an average of five days to build new data dashboards, while less successful companies need more than 55 days, according to Aberdeen.

The top three pressures driving the need for agile analytics – according to the 103 survey respondents – are:

  • Increasing/changing demand for management information (51%)
  • Growing volumes of source data/number of data sources (39%)
  • Users increasingly need to make decisions anytime, anywhere (34%)

Both leaders and followers agree on the most important strategy for data analysis – to enable end users to be more self-sufficient leaders and automate the collection and assembly of data.

“Static reports, charts and dashboards can tell a manager what happened,” the report notes. “Often, though, they cannot explain why something happened. That is because the ‘why’ is often hidden in detailed information that is not visible at the summary level.”

Aberdeen provides these recommendations, gleaned from the top-performing companies:

Provide independence for users: Top-performing companies are 49% more likely than less successful companies to provide drill-down capabilities and almost 2.5 times more likely than less successful companies to enable users to fully interact with and manipulate data.

“Providing this type of solution means that analytics users have a better chance of finding the right information at the right time,” according to the report.

Simplify the landscape: Leaders are twice as likely as followers to use a single integrated toolset for all analytics, and they are more likely than followers to use tools with built-in data connectors and allow content to be developed without programming or coding.

In addition to providing flexible options for users, companies increasingly must be able to quickly glean insight from data analysis or face falling behind their rivals, notes Narendra Mulani, managing director of Accenture Analytics

The need to ask for and act on data more quickly is due in part to heightened customer and business expectations,” Mulani says. “With ubiquitous access to data via smartphones and tablets, businesses no longer have an excuse not to make informed decisions in real time. Mobile technology also enables workers to track insights about customers, products, work orders and more from anywhere.”

The ability to harness the speed of data is critical for a multitude of industries.

In retail, for example, companies have only seconds to engage customers on their sites before they click away. In this short time, they must be able to link a variety of data including purchase history, social networking comments and details from customer support calls to generate customized offers to promote purchases.

Wal-Mart is working on a feature to add to its mobile app that would use data analysis to generate a suggested shopping list for customers based on their previous purchases.

Wal-Mart’s app already has a geofencing feature that “senses” when a user is in a Wal-Mart store in the US. The app then prompts the user to switch to “Store Mode,” which is a setting that allows the customer to scan QE codes for prices and discounts.

The company is also working on another feature for its mobile app that would use big data to give a shopper useful information on demand.

For instance, if an app user were to be in the toy aisle, he could use a voice feature to tell the app he’s looking for a toy under $60. The app would then generate a list of the best-selling toys in that particular store that meets the customers budget requirements.

“Our goal is to create shopping tools that become second nature to the customer, providing assistance with every part of the retail experience from pre-store planning to in-store shopping and decision making to checking out, said Gibu Thomas, global head of Wal-Mart’s mobile division.

In-memory computing is ideal for the speed needed for the most effective data analysis, notes Accenture’s Mulani.

“In fact, the increasing adoption rate of in-memory technology in corporate data centers – which Gartner predicts will grow threefold by 2015 as memory costs fall – represents an inflection point for business enterprise applications,” Mulani says. “For the first time, it’s possible to unify transactional and analytical processing, and business leaders can ask their databases specific, ad hoc questions and receive immediate answers.”

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