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. Even when licensing information to others, most organizations will add value to it by generating and selling the insights or analysis instead of or alongside the raw information itself. However, evolving from traditional business intelligence or BI represented by enterprise reporting or 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 squandered opportunities to generate measurable economic benefits from monetize their information assets. We'll explore the case for reaching beyond business intelligence and how embracing these ideas can lead to improved economic benefits for your organization. Analytics is a key way to package and deliver information, to make it more usable and valuable to people and to processes. The CIO of a big force systems integrator once told me, "From what I've been able to determine, we have over a 100 distinct internal BI implementations producing some 15,000 reports, most weekly, some monthly or quarterly and that's just in the US." You went on to question the value these implementations and reports generate for the organization, he said, "I have no idea who's using them and if they're using them at all and for what purpose. But the systems are costing me millions. So, I'm considering shutting down some just to see if anyone complains." This story is repeated over and over in varied vernacular by IT executives I speak with, most often by those who have inherited a goggle of data warehouses, data marts, and BI applications. Often, it's followed by a common proclamation, "I'm desperate to get IT out of the report writing business." Their real concern isn't the cost or resources required by analytics. It's the inability to link it to discernable 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 be clamoring to keep them within the IT organization. Basic Analytic Implementations are everywhere in every corner of the organization. They range from personal spreadsheets to financial and production reports to executive dashboards. The sprawl of these applications especially 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 and 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 are these implementations may have been measurable, few organizations have actually measure them other than with poor proxies of values such as user satisfaction. Ultimately, analytics and data warehousing had become a significant IT cost sink. In many instances, only with acknowledged soft and unmeasured benefits. Yet inertia to continue generating hindsight oriented reports and dashboards is a function of having chased after them the past 20 or 30 years. Typical analytic implementations tend to be far removed from actually monetizing data or generating economic benefits, or connecting the dots between them and the top or bottom line value propositions. More advanced analytics as we'll see, generate actionable insights predictions or explicit recommendations that can be connected more readily to economic improvements. Therefore, there is an imperative for organizations to reach for an Advanced Analytics capabilities. However, we find Advanced Analytics initiatives tend to be more challenging and vocational. That is, targeted at particular business problems or an opportunity rather than being enterprise wide. Because information reporting and exploration continues to serve a valid purpose in organizations, especially with the emergence of self-service analytics, it can be helpful to consider BI and Advanced Analytics as distinct entities and initiatives with unique value proposition, staff, and technologies from one another. Whereas analytic implementations are appropriate typically for informing business managers of performance indicators, Advanced Analytics Implementations can provide far-reaching organizational benefits over basic BI. The requisite BI tool or 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 maybe made it a rate slower than the speed of business. Tactical and operational decisions increasingly must be made at a rate faster than that what humans are capable. Despite the continued corporate reliance on spreadsheets, analytics is now a core competency in most organizations. However, most implementations especially enterprise implementations until 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 and behavior at all, or at least limit the reliance on it. Resolving this demands producing analytic results beyond simple summarizations and delivering those results direct to business processes, not necessarily people. Directing analytic output merely at eyeballs continues to be one of the great fallacies and limitations of analytics. However, Advanced Analytics and all its variety of instantiations for example data mining, prediction, artificial intelligence, complex event processing, visualization, and simulation can be not only difficult to implement but also difficult to articulate and coordinate. This is why even the most well-intentioned analytic initiatives can too easily and quickly devolve into pedestrian presentations of lagging performance indicators.
