Operationalizing a technology or process (or both) means efficiently getting it in the hands of end-users who will realize value and benefits in their roles and by doing so extend the value and benefits to their organization.
This blog focuses on operationalizing analytics for decision support for humans, which as you’d expect accounts for most business decisions. TDWI Research reveals in a recently published best practices report that 75% of an organization’s decisions supported by analytics are made by humans. The entire report that includes a thorough examination of operationalizing analytics and the interrelated topics discussed in this blog, can be downloaded and read using this link: “Operationalizing and Embedding Analytics for Action.”
Analytics, simply put, is a category of information processing methods that derives value from data. Analytics is necessary to operate on data that is too complex and voluminous for manual methods. Specific types of analytics perform vastly different functions that generate different outputs that include: insightful details, predictions, recommendations, optimized choices, outliers (anomalies), patterns and trends. While the output of analytics may be interesting, the value and benefits are only realized when specific actions are taken. That means the recipients of the output from analytics must be able to consume it, comprehend it, and effectively use it to make decisions and take action. Often the shorter the time to action the better.
Until recently analytics has been confined to IT and data science professionals, impeding organizations from maximizing the benefit of the value in their data and from their investments in analytics. The recently published best practices report published by TDWI Research also cites the necessity, value, recognition and trend of making analytics and its output available to a wide group of people within an organization. Among the survey results in that report is an increasing awareness and willingness by organizations to operationalize analytics with 88% of their survey respondents claiming they have analytics in production or development that could be considered to be operationalized.
Another impediment is from delays between the availability of insights for decision support and when its actually needed diminish the value of the insights, or worse, allow other undesirable and potentially preventable consequences to occur. The most common reason for delays is due to inherently slow manual processes required to gather the necessary data, prepare it, have specific personnel run analytics programs, review and otherwise process the output, then convey the results to the decision makers. Each of these steps can take hours or days; even weeks in extreme cases. Timeliness is therefore also an important characteristic of a well operationalized solution. When appropriate actions are taken faster, gains can be maximized and adverse consequences can be averted or minimized.
The good news is that modern technologies make it possible to put actionable insights from analytics into the hands of end-users with few or none of the delays just discussed. That is, operationalized analytics can result in a very short or zero time-to-insights.
Making results available in a timely manner can be achieved by making analytics available on a self-service basis and/or making the output continuously available and readily consumable. One example of making the output continuously available and consumable is displaying intuitive visualizations of analytics output on a monitor wall in an operations control room. For some organizations, it is very commonly necessary to receive and act on insights and output from analytics both inside and outside of a control room. Delivering analytics output to people at their desk, on the factory floor, in the field and wherever they are is typically accomplished using browser-based applications, mobile devices, and ubiquitous communications networks (e.g., WiFi, 4G LTE, etc.).
Another best practice for operationalizing analytics is to embed analytics into existing business processes and the visualized output of the operational applications used to facilitate specific business processes. Analytics processing can be hidden in the background such that what end-users receive is seamlessly integrated into the screens and dashboards they’re accustomed to using. When this type of visual blending is not possible in the native application, situational intelligence, with its ability to create composite views, can be used to include the output of other applications combined with analytics into a single app window. This latter approach creates a broad and relevant context for decision-makers, enhancing their ability to act quickly and appropriately with confidence.
For the reason just mentioned, situational intelligence is in fact a powerful and highly effective way to operationalize analytics because this type of enterprise application lends itself to relatively easily operationalizing analytics with intuitive user interfaces and at-a-glance presentation of information and results from analytics. Tightly integrating visualizations with data and analytics results, especially with browser-based apps, makes insights readily consumable and actionable to anyone anywhere. As a result, organizations from small start-ups to large global enterprises empower workers and correspondingly improve their business results and successes with widespread use of analytics.
As technology marches forward, processing power and analytics-specific frameworks such as Spark enable complex analytics processing software and jobs to be completed fast, even instantaneously in some cases. The ever-present Internet and browser-based user interfaces make analytics with richly visualized results available to anyone, anywhere, on large screens as well as on handheld mobile devices, truly putting analytics into the hands of a wide population of end-users. The benefits provided by situational intelligence are accelerating the ability to effectively operationalize analytics.
The age of operationalized analytics catalyzed by situational intelligence that delivers timely and readily consumable actionable insights to anyone anywhere is here. Fasten your seat belt, the pace of taking action driven by analytics is accelerating.
Another option for operationalizing analytics is automation, which is when systems automatically make decisions and initiate actions via direct machine-to-machine communications. In these cases humans are not in the decision making loop. Automation is an important topic that will be addressed in future blogs.