Unlike many other enterprise data processing solutions, analytics is viewed as a mysterious black box. Historically some analytics and models were so complex that only experienced IT professionals could execute analytics jobs on costly large computing platforms. IT personnel, database administrators, software developers, and other skilled personnel were necessary to interpret the output of analytics to arrive at the proverbial “answer.” In such cases the time-to-answer could be hours, days and even weeks.
The air of mystery especially envelopes analytics that derive likely outcomes (a/k/a “predictive analytics”). Predictive results are actually likely outcomes derived by processing large volumes of data using specific mathematical and statistical methods. Nevertheless some say colloquially that predictive analytics can predict the future. Statements such as this add to the mysticism about analytics.
Today’s vocabulary of analytics increases the aura of mysticism: Hadoop, big data, artificial intelligence, machine learning, data science, stochastic optimization, etc.
Because of this mysticism and the seeming ability to predict the future, people also have a notion that analytics is an elite category of software available only to large enterprises with large budgets, extensive IT infrastructures and dedicated teams.
The good news is that specialized terminology, rarified skill sets and expensive machinery no longer confine analytics to elite glass houses. As the simplicity of analytics becomes more commonplace, the aura of mysticism evaporates. The image of an esoteric technology for an elite few fades, giving way to adoption by a broad range of workers and end-users.
Powerful yet affordable commodity hardware and other technological advances make it possible for organizations regardless of size, budget and personnel to obtain and run analytics, consume and act on the results, and realize the many benefits. Software advances such as distributed high performance computing platforms and alternatives to traditional relational databases, to name a few, bring analytics within the grasp of any organization. Advances in data visualization remove the need to post-process analytics results into readily consumable and actionable answers, whether the answers are recommendations, predictions or other forms of insight. Ubiquitous communications, modern browsers and applications that take advantage of HTML5 greatly simplify the ability to deploy software solutions of any type and complexity, including analytics.
This new demystification and accessibility comes just in time. For organization to benefit from their data, personnel must be able to receive, comprehend and act on alerts, recommendations, predictions and all other insights generated by analytics. Without this operationalization of analytics, data threatens to flood the organization without providing any value.
As analytics become more accessible, their use and results will be embedded in many applications, which in turn hides complexity and helps make analytics ubiquitous. I eagerly look forward to many new and innovative uses of analytics, and the resulting business and societal benefits.