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Business analytics trends yield tools with potential -- if you're ready

Learn about key business analytics trends and get advice on evaluating and deploying new types of analytic technologies, including in-memory analytics and in-database analytics.

Advanced business analytics technologies such as in-memory analytics, in-database analytics and complex event processing (CEP) tools enable organizations to analyze increasingly massive amounts of data from a diverse number of sources, and to do it faster and more efficiently than ever before.

But does that mean you should spend the required time and resources to implement them? The technologies are mature enough to consider for enterprise adoption, according to industry analysts. The biggest question might be whether your organization is ready for them.

In fact, the first thing any business needs to consider when thinking about buying advanced analytics software is whether it’s sufficiently prepared to deploy and use tools that are more complex than mainstream business intelligence (BI) products, said Forrester Research Inc. analyst Boris Evelson.

“The first advice is really, ‘Are you ready?’ You need to learn to walk before you can learn how to fly,” Evelson said.

There a lot of things that have to be done – and done correctly – just to support basic BI and analytic technologies, he noted. Data governance policies and processes need to be put in place. Basic data management issues also have to be taken care of. For example, BI data must be cleansed and properly integrated to ensure that key corporate information is accurate and accessible.

Only when those steps have been taken should an organization look at aligning itself with the latest business analytics trends, technologies and techniques, Evelson cautioned. “Otherwise,” he said, “it’s like the proverb: Garbage in, garbage out.”

But Evelson and other analysts said there’s no reason to avoid emerging analytic technologies once you’re in a position to succeed with them. Until recently, that might not have been the case. For example, in-memory analytics, in which queries and calculations are run against data stored in a computer’s memory instead of requiring information to be pulled from disk drives, has been around for years – but it was limited by 32-bit architectures and high memory costs. Now, thanks to 64-bit architectures and reductions in memory prices, the technology finally appears to be hitting its stride.

As a result, all of the major BI vendors are committed to providing some kind of in-memory analytics capabilities, if they don’t already, according to Rick Sherman, founder of Stow, Mass.-based consulting firm Athena IT Solutions. “It’s a proven technology,” he said.

Business analytics trends at work: friendlier tools, faster performance

The increasing allure of in-memory analytics is being aided and abetted by several factors, industry watchers said. For one thing, many business users have grown frustrated with having to go to the IT department every time they need to create a report. At the same time, organizations are looking for tools that are more flexible and more intuitive to use, as part of so-called pervasive BI efforts aimed at broadening the adoption of BI and analytics software within companies.

“We’re seeing a trend toward technologies that are easier to use for people who aren’t necessarily very technical or capable of writing their own reports but still want to do their own analysis,” said Rita Sallam, an analyst at Gartner Inc. The biggest reason in-memory analytics has taken off, she added, is that vendors have combined the technology with user interface tools that are highly interactive and simple to grasp.

The first advice is really, ‘Are you ready?’ You need to learn to walk before you can learn how to fly.

Forrester Research analyst Boris Evelson

Also working to the benefit of in-memory analytics are growing demands from business users for faster data analysis performance, Sallam said.

Sherman agreed, saying that the amount of time it takes to do more complex analytics is a constant source of frustration for end users. Looking at data through the lens of various metrics “is where analytics gets slowed down considerably,” he said, while pointing to in-memory analytics as a potential way to reduce processing times.

However, giving business users more analytical flexibility and freedom via in-memory analytics also carries with it some risks, Evelson warned.

“It’s a double-edged sword,” he said. “On one hand, it’s great to empower your end users, and it frees up the IT department. On the other hand, you start losing control. So, all of a sudden, end users that maybe don’t have the proper training start creating their own reports. How do you know they’re creating the right calculations?”

End-user training programs at organizations that are deploying in-memory analytics tools should include sections designed to give workers a good understanding of corporate data and data models, so they won’t go astray in using the information and produce faulty findings, Evelson said.

Setting the stage for scaling up advanced business analytics

In-database analytics, in which analytical processing is done directly within a data warehouse, is also being supported by more vendors and seeing broader enterprise adoption. Forrester analyst James Kobielus said in-database analytics tools have the potential to help organizations scale up their data mining activities and other advanced analytics efforts.

For example, as data mining models become increasingly complex, analytic applications have to pull together more data, on a more continuous basis, and from a greater number of data sources than in the past, according to Kobielus. “You need some heavy-hitting horsepower to do that in an efficient way,” he said, adding that in-database analytics can help speed up the process.

CEP software, which is designed to enable organizations to monitor and react to business events in near real time, is another example of how business analytics trends are evolving.

While CEP technology typically is used to look for patterns and trends in large amounts of financial or supply-chain data, Kobielus said new uses are emerging – for example, monitoring Twitter and other social media networks for mentions of a company or a product.

Kobielus described CEP, in-database analytics and in-memory analytics as “hot technologies” that have the ability to provide significant benefits to companies ready to handle them. “This is not bleeding edge,” he said. “This is really happening.”

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