Unless you're licensing information directly or trading it for goods and services, it's more likely you're monetizing it indirectly via some form of analytics or digitalization. Even with the latter, most licensed or bartered information has some degree of analytics applied to it before it's shared. And even when licensing information, most organizations will add value to it by generating and selling the insights or the analysis instead of or alongside the raw information itself. However, evolving from traditional business intelligence or BI, represented by enterprise reporting and end user query tools, has been slow to materialize in many organizations. Not only have lagging organizations lost out on the opportunity to understand their businesses and markets better, but they've squandered opportunities to generate measurable economic benefits from their information assets. In this lesson, we're going to explore the case for reaching beyond business intelligence, and how embracing these ideas can lead to improved economic benefits for your organization. Analytics, or whatever you want to call it, business analytics, data analytics, or business intelligence, is a key way to package and deliver information to make it more usable, and valuable, and consumable to people and processes. The problem is that over the years there's been quite a degree of analytics sprawl throughout organizations. The CIO of a Big 4 Systems Integrator said, from where he's been standing and able to determine, they have over 100 distinct internal BI implementations producing some 15,000 reports, mostly weekly, some monthly, or some quarterly. And he said, that's just in the US. He went on to question the value of these implementations and reports, what value they generate for the organization. He said, "I have no idea who is using them, and if they're using them at all, for what purpose." But he said the systems are costing them millions of dollars, so he's considering just shutting them down just to see who complains. This story is repeated over and over by IT executives I speak with, most often by those who have inherited a gaggle of data warehouses, and data marts, and BI applications. Often this is followed by a common proclamation. "I'm desperate to get IT out of the report writing business!" Their real concern is that the cost or resources required by BI, it's the inability to link it to discernible economic gains. I'm certain that if IT executives could attribute top or bottom-line value propositions, or any key organizational metrics, for that matter, to these implementations, then they would have be clamoring to keep them inside the IT organization. Basic BI implementations are everywhere in every corner of every organization. They range from personal spreadsheets, to financial and production reports, to executive dashboards. The sprawl of these applications, particularly as a result of commonplace data warehouse implementations, over the past 20 years, no doubt has improved enterprise transparency and influenced improvements in productivity, customer partner relationships and compliance. But, slicing and dicing, and reporting on information in most cases, has fallen woefully short of producing measurable economic benefits. Where these implementations may have been measurable, few organizations have actually measured them other than with poor proxies of value, such as user satisfaction. Ultimately, BI and data warehousing have become a significant IT cost sink. In many instances, only with acknowledged soft and unmeasured business benefits. Yet the inertia to continue generating hindsight oriented reports and dashboards is a function of having chased after them the past 20 or 30 years. Typical BI implementations, particularly those that focus on hindsight oriented data, tend to be far removed from actually monetizing data, or connecting the dots between them and top and bottom line value propositions. More advanced analytic capabilities which generate actionable insights, predictions, or explicit recommendations can be connected more readily to economic improvements. Therefore, there is an imperative for organizations to reach for advanced analytic capabilities. For good or bad, we find advanced analytic initiatives tend to be more challenging and more vocational. That is, they're targeted at a particular business problem or opportunity, rather than the enterprise at large. And because information reporting and exploration continue to serve a valid purpose in organizations, especially with the emergence of self-service business intelligence, it can be helpful to consider BI and advanced analytics, as distinct entities and initiatives with unique value propositions themselves, with unique staff and technologies as well. While BI implementations are appropriate typically for informing business managers of performance indicators, advanced analytics implementations can provide far-reaching organizational benefits, over basic BI. The usual BI tool platform features of data aggregation, summarization, selection, slicing, drilling, and charting, are tuned for presenting information interesting to users, but not necessarily information important to optimizing business processes. Today, only strategic decisions, and not even all of them, may be made at a rate slower than the speed of business. Tactical and operational decisions increasingly have to be made at a rate faster than that what humans are capable. Despite continued corporate reliance on spreadsheets, analytics is now a core competency in most organizations. However, most implementations, particularly enterprise implementations, entail basic decision support solutions for business managers and executives. In addition, much information is still left to interpretation. Pockets of analytics result in information clashes between departments, and many users choose not to rely on analytic output to guide their decisions or behavior, or at least they limit their reliance on it. Resolving this demands producing analytic results beyond simple summarizations, and delivering those results directly to the business processes, not necessarily people. Directing analytic output merely at eyeballs continues to be one of the great fallacies and limitations of BI. However, advanced analytics and all its variety of instantiations can not only be difficult to implement but also difficult to articulate and coordinate. This is why even the most well-intentioned analytic initiatives can all too easily and quickly devolve into just pedestrian presentations of lagging performance indicators. In the next lesson, we'll look at different levels and styles of analytics with a particular focus on advanced analytics.
