Gartner recently released its latest Hype Cycle Research Report, an annual maturity assessment of technologies and IT trends. The company’s latest research focused primarily on Big Data and when this technology is expected to reach maturity or “the plateau of productivity,” where mainstream adoption can begin. Unfortunately, Gartner’s estimate of when Big Data would reach this ‘plateau of productivity’ has become more pessimistic since last year – the research firm now estimates that it will take between five and ten years for Big Data to reach that plateau.
The research goes on to outline two reasons why Gartner believes Big Data is heading down the ‘trough of disillusionment.’ Yet, the research also overlooks a key aspect of analytics that contrasts directly with big data – operational data. While Big Data refers to a large, nebulous and immensely complex data set, operational data has a known, contained structure. Such data is populated by normal functioning business activities like sales and marketing efforts, is accessible and is used repetitively, such as each month or quarter to guide business decisions.
Here are the two reasons touched on in the research for why Big Data is far from mainstream adoption and productivity – and counterpoints on why operational data has already arrived.
Misconception #1 – “Tools and techniques are being adopted ahead of learned expertise and any maturity/optimization, which is creating confusion.”
This primary reason, according to Gartner, for why Big Data has not seen mass adoption speaks to a level of expertise required for companies to make sense of the reams of data and information. Many companies simply don’t have the tools or resources to capture, curate, store, visualize analyze and, most importantly, understand the information derived from such large data sets. Small businesses especially would be hard-pressed not to be confused by such immense collections of data.
Operational Data Counterpoint #1 – “There is no expertise or expensive tools required to access operational data.”
The beauty of operational data, coupled with the right out-of-the-box tools – such as InsightSquared! – is that there is no expertise or expensive paid consultant required to leverage operational data. In fact, the accessibility of operational data across all levels of a company – from CEOs and VPs of Sales down to sales reps – make it such a valuable asset.
Operational data can be easily understood because the information touches on the normal day-to-day functioning of the business. For instance, a sales rep looking to improve his or her own sales process and results can dive into the data on the activities that they are performing on a daily basis. Are they seeing that most of their calls to a group of leads produces a low rate of conversion to opportunities? Perhaps they need to focus on more effective lead sources. Are they finding that many of their opportunities are ultimately not interested in buying? Perhaps they need to be qualified more stringently to ensure a cleaner, less-clogged sales pipeline.
This data, with the right tools, can be easily accessed. The reports, when properly presented and visualized, can be easily understood. The insights, when accurately put into action, can be easily leveraged to solve existing business inefficiencies. Such functionality makes operational data a great and accessible asset for any business.
Misconception #2 – “…the inability to spot Big Data opportunities by the business, formulate the right questions and execute on the insights.”
At the core, Gartner’s research suggests that most companies who do have access to Big Data are not using it correctly to produce actual actionable insights. Big Data and analytics for Big Data’s and analytics’ sake is a useless endeavor that is not worth the investment. Most companies have a deer-in-the-headlights look when facing such complex data sets – they simply don’t know what to do with all this information and can become easily overwhelmed, thus preventing them from truly unearthing the potential of having this information at your fingertips.
Operational Data Counterpoint #2 – “Operational data identifies pain points, and then helps solve these issues by looking at the numbers.”
The key to what makes reports from operational data valuable is that the visualizations help identify where your pain points and primary issues are. For example, looking at a sales funnel report that features historical conversion rates at various sales funnel stages can quickly point out any weaknesses in the selling process of the entire team, or of individual reps.
Once that weakness has been identified, sales managers can then take the appropriate steps. This rep does a great job sending reps on through to the closing stage, but ultimately fails to convert opportunities at this final stage into closed-won deals. Now there are actionable and direct points of sales coaching for the manager to work on. They can then dive back into the numbers and data to figure out how to solve the problem. Maybe the rep is not giving these opportunities the attention they require at the end of the buying process. Perhaps there needs to be a better nurturing marketing effort throughout the buying cycle.
Finally, operational data allows you to do just what the name suggests – put this information into operation. Sales managers have to intently study the data and formulate the right questions that probe into their selling process and challenges them to be better. They must then put the insights gleaned from this information into action.
We conclude this counterpoint of Gartner’s stance on Big Data with a quote: “Have no fear of the unknown.” Big Data is a dark and scary place that isn’t worth venturing into. On the other hand, as business learn more about operational data, they are realizing that this could be the informational shining light they need, revealing all they need to know about their business. Don’t waste another minute fearing the unknown of Big Data; step out of the darkness and into the light of operational data.
