Too often, business analysts, data scientists, and similar power users share their analytic epiphanies with a very short list of peers and management. Those insights should be shared across departments if the enterprise is to get the fullest value from big data.
For example, analytics may show the root cause of a new form of customer churn. That’s when you develop and regularly quantify churn metrics to spot the trend if it returns. As another example, let’s say you’re tapping a new source of big data and, for the first time, you are able to reliably calculate customer sentiment. If you want executives to easily understand and track that sentiment, a business analyst or technical user should develop a series of metrics representing various aspects of sentiment that roll up into key performance indicators (KPIs).
Turning an ad hoc analytic insight into a repeatable BI consumable is called operationalization. This usually results in a polished deliverable, such as a dashboard, sandbox, metric, and so on (see Figure 1). Operationalization has distinct benefits.
Operationalizing the outcome of discovery analytics expresses the analytic epiphany in practical terms that many users can understand. Once that happens, the data and its meaning are democratized as they are shared across an enterprise. Democratized insights help the organization gain full business value from its investments in BI and related practices. Likewise, democratized insights (as opposed to those that are unique to a department or other group) keep diverse teams from contradicting each other with competing data sets or arguing over whose data set is best. And finally, operationalization takes a one-time analytic insight and turns it into a metric that can be tracked over time.
Ideally, popular operationalized BI deliverables should be on a platform that enables personalization. Such a platform helps the many diverse managers consuming a deliverable to tailor it to their own unique needs, in a self-service fashion. After all, a C-level executive needs a different view than a line-of-business manager or marketing director does. Democratizing big data this way spawns a social net of executive users who then learn from each other, sometimes in a viral fashion.
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