In IT, the bigger the hype, the greater the misconceptions, and data analytics is no exception. Analytics, one of the hottest facets of information technology today, can result in significant business gains, but misperceptions can get in the way of a smooth and timely delivery of analytical capabilities that might benefit business users and ultimately customers.
As organizations create or expand their analytics strategies, here are a dozen myths they might want to keep in mind.
Myth 1: Data analytics requires a major investment
These days it seems as if every technology endeavor must pass through a filter of financial soundness. “How much will it cost?” is one of the first questions IT and business managers get when they propose launching a project or deploying a new tool.
Some assume that data analytics is by nature a costly undertaking and therefore limited to organizations with big budgets or lots of internal resources. But not all data analytics efforts require a major investment, says Deep Varma, vice president of engineering at Trulia, a provider of mobile and online real estate services.
“Nowadays there are so many open source as well as other tools available in the marketplace that can help you start to show the value of data analytics,” Varma says. “You need to have a good understanding of your internal data storage and what problem you’re trying to solve. Cloud is also making it easy to try analytics to solve a business problem.”
Modern analytics “are based on cloud systems and big data architecture, which by definition are quite less expensive than traditional data warehouse systems,” adds Beatriz Sanz Saiz, global leader of analytics at advisory firm EY.
“Also, data and analytics are typically applied to achieve three outcomes: improve process efficiency, revenue growth, and proactive risk management,” Saiz says. “So overall, the application of data and analytics drive significant [cost] benefits to any company.”
Myth 2: You need big data to perform analytics
For many, the concepts of big data and analytics go hand in hand. The thinking is that organizations need to gather enormous volumes of data before performing analytics in order to generate business insights, improve decision making, etc.
Certainly the benefits of big data analytics have been well established, and companies with the resources can indeed gain significant competitive advantage by leveraging their data stores as part of analytics efforts. But the idea that big data is a must for analytics is not true.
“Oftentimes people try to capture as much data as they can get; they hear ‘big data’ and get excited,” says Tim Johnson, executive director of business intelligence at staffing company Allegis Global Solutions. “The misconception is that the more data the better, and that machines will sort it all out.”
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