Merriam Webster’s online dictionary states the first known use of “analytics” was in 1590 and was defined as “the method of logical analysis,” whereas “analysis” was first used in 1581 and was defined as “the separation of a whole into its component parts.”

About 430 year later, analytics is now defined as whatever you want it to be including visualization, machine learning, calculations in Excel, business intelligence, dashboards, KPIs, predictive analytics and/or other terms. The pressure to gain insight from data is so pervasive that “analytics” has become a throw-away term in marketing materials for all types of software. Analytics is in there, trust us!

In fact, the only term more abused than analytics is “actionable insights,” the hopeful outcome of an investment in analytics. Thus, “analytics for actionable insights” has been the consistent industry promise for how to improve any of 100 different outcomes in production or business results over the last decade.

There is, however, a growing focus on advanced analytics as a new and distinct set of capabilities with the potential to disrupt the overused “analytics everywhere” language. “Advanced” may seem like an odd adjective to couple with “analytics”, but it’s the term of choice of industry analyst Gartner Group and management consulting firm McKinsey & Company. Their definitions of the term are:

  • McKinsey & Company: Advanced analytics refers to the application of statistics and other mathematical tools to business data in order to assess and improve practices…
    • Using data visualizations (distributions, deviations clustering)
    • Identify core determinants (of performance, correlations)
    • Using significance testing (statistics)
    • Using artificial neural networks (impact and optimal ranges of parameters)
  • Gartner: Advanced Analytics is the autonomous or semi-autonomous examination of data or content using sophisticated techniques and tools, typically beyond those of traditional business intelligence (BI), to discover deeper insights, make predictions, or generate recommendations. Advanced analytic techniques include those such as data/text mining, machine learning, pattern matching, forecasting, visualization, semantic analysis, sentiment analysis, network and cluster analysis, multivariate statistics, graph analysis, simulation, complex event processing, neural networks.

And if you haven’t read any of the work by McKinsey on advanced analytics, for example “Buried Treasure: advanced analytics in process industries,” then we strongly recommend you do so. McKinsey & Company have been publishing the most expansive insights on analytics as they relate to various manufacturing vertical including oil & gas, chemicals, power generation, etc., and one can learn a lot by following their opinions on the subject.

With these definitions as context, perhaps a better adjective for analytics would be “accelerated” because what Gartner and McKinsey are talking about is the acceleration of analytics through innovation—combining computer science and expertise with analytics to improve potential insights.

Certainly, a core aspect of “analytics” is accessibility by subject matter experts, process engineers, and other employees with expertise regarding the assets and processes of a plant or an operation. Therefore, putting these employees and their expertise together with analytics and the current innovations of data science should lead to improved outcomes.

We certainly should expect this based on our user experiences as consumers. Google accelerates finding things on the web, Alexa accelerates our interaction with Amazon and other services, Uber with transportation and the list goes on. In each of these cases, computer science innovations are being delivered to customers as an accessible end user experience. Slack, mobile phones and other tools provide the same experience for (most) of our workplaces. Isn’t it time that this acceleration through innovation was delivered to end users in analytics and production experiences?

This is what’s next for analytics: advanced analytics in the hands of engineers and other experts to accelerate finding insights, and to find new types of insights, in the data pertaining to their business and production processes.

Header Image: Seeq puts advanced analytics in the hands of engineers and other experts, with no assistance required from data scientists.

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