If you were to believe the marketing machine employed by IT bigwigs, data is the new oil. Some regard data as the cure to all your business problems, others regard it as an asset over which the next World War might occur, and then there are some that see stars and dollars when they hear about the next big analytics application being commissioned. Let’s face it: Data analytics is often oversold to the extent that the lines separating myth from reality have been blurred. And the damage is two-pronged. Vendors oversell it, and authoritarian executives who fail to justify their costly analytics application choices via real business benefits go for overkill in belittling and denigrating data analytics.
Let’s bust some myths that anybody associated with data-driven business processes (means everybody) may believe in — but shouldn’t.
Every marketing message you hear from a data analytics software vendor sounds similar to the final call for a cruise leaving on a voyage to bring home treasures from a distant land. Is everyone really on-board?
Every SMB and enterprise possesses data. However, they may not have “analytics-ready” data. Successful data analytics engines run on the fuel of Big Data (volume, variety, and velocity). And making an enterprise’s Big Data ready for analytics is time-consuming and expensive. Even if this checkbox is ticked, there are several other important prerequisites to be met and decisions to be taken. So, if you consider the milestone as “getting positive ROI on data analytics expenditure” as being on-board, it’s very different from considering a business that has “purchased and implemented” an analytics solution as being on-board.
So, understand your business’ key data streams, data acquisition and processing methods, your data volume and variety, and your analytics goals and success indicators before commissioning an analytics implementation.
Technically speaking, any extrapolation is a shot at predicting the future. Any technology can only fine-tune extrapolations to become better at making realistic guesses about future events. With data analytics too, it boils down to the sophistication of the predictive analysis algorithms, the merits of your enterprise’s data governance processes, your data scientists’ capabilities, and your business managers’ intuition to be able to fine-tune the results of predictive analytics. So, the next time someone tries to package a data analytics solution as some sort of a crystal ball, remember that the power of accurate predictions is not an inherent feature of these applications, but is one of the benefits you could draw, based on several other success factors.
As weird as it sounds, even the best of analytics applications can’t be called unbiased. That’s because the core algorithms that help these applications deliver insightful analytics results are coded by humans, and hence, are always prone to biases. Also, the kind of “training data” that are used to implement an algorithm passes on its characteristics to the algorithm. Though most of these biases are benign, there’s always the risk of severe biases too, which can only be unearthed by careful evaluations of the analytical insights delivered by the application.
We’ve tried to unveil some of the questionable marketing communication tactics employed by analytics vendors in the above section. Now, let’s debunk some myths spread by naysayers and skeptics.
The misconception among SMBs, in particular, is that too many of them feel that Big Data analytics applications cost a lot. But in truth, these applications are not limited for use only by enterprises that own a lot of free cash and have large internal IT teams.
As long as you understand your internal data processes and have clear analytics goals, you can find open source analytics applications that will help you do pilot tests to prove business-use cases for analytics. Also, you’ll be able to find cloud-based analytics vendors that will help you with dedicated implementations to achieve process improvements, revenue growth, and risk management.
Because of the inherent cost benefits associated with them, companies can quickly achieve positive ROIs on their analytics expenses.
For many large enterprises, data analytics success has been directly tied to their Big Data capabilities. Though Big Data is a tremendous enabler of analytics success, it’s not a prerequisite. Particularly when businesses have laser-focused use cases in mind, analytics applications can work well without requiring large data volumes. In the same vein, businesses need to realize the importance of simplicity of the analytics reporting, for end users to adopt the application. Too many reports, too many formats, and too many variables - that’s a recipe for trouble with your analytics projects.
Data scientists are among the most sought-after professionals in these times. That’s because they can create tremendous leverage for businesses, helping them bring out massive insights from existing data. However, a wrong practice prevalent among enterprises is that they expect their data scientists to be involved at every step of the data analytics process.
Finding data, cleaning data, and transforming data, although important, are not steps where data scientists can add scalable value. Instead, give data scientists the platform to add value in terms of strong and smart algorithms that help the business users make better decisions. Thus, even a small team of data scientists can help an enterprise get quick success out of analytics applications.
Apart from all these myths, there are certain practices that can impede the progress of data analytics for your enterprise. Among these, the riskiest one is to isolate data analytics into a support function. Today, considering how important data has become for every business processes, it’s pretty obvious that analytics needs to be a cross-functional, integrated, and approachable unit for the business.
Data has to do a lot for your enterprise before you can call it oil. Stay away from myths, and you’ll strike oil sooner than otherwise.
Photo credit: Pexels
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