Again on a flight to Europe this week, I struck up a
discussion with a fellow traveler who is out the oil and Gas industry while
sitting in Abu Dhabi on a layover. It was around the transformation of
decisions support from reports, to dashboards, and now for a need for more real
time decision support. This discussion
aligns with the Information Driven concepts and the transformation from reports
to predictive and decision dashboards.
Information Driven Companies are looking for more than what has happened they driving to understand what will happen?
It is crucial to note Excel, BI and EMI tools provide extremely solid basis for analysis in the past and now, and in a focused area, but as decisions become more predictive the way to sure up the prediction is to start looking across significantly bigger data to see common patterns. EMI provides dashboards and alerts based upon basic rules and KPIs this is still required.
Source ARC
The above diagram shows the evolution of Enterprise Intelligence and Business Intelligence from the understanding of today to a more predictive requirement, this is from ARC. When I talk with customers, I use the Operational Excellence Journey diagram below to describe the evolution to real time, agile decisions. It is vital that companies accept that achieving operational excellence is a journey not a one off project as it evolves as the company learns and tunes.
As you move to right the
analysis and analytics start looking for patterns, and relationships across
data sources, and linking causes to a set of conditions. BI and EMI remain indispensable
tools of information driven companies, but they do have limitations. For
example, BI involves the IT and runs in batches of set time breaks while
information driven companies are requiring broad access to analytical
information and they need it continuously in real time. Finally to be able to adapt
quickly to market place changes, information driven companies need to look
forward, predicting what will happen next. Traditionally BI/ EMI systems have
not incorporated predictive analytics tools to apply pattern matching rule and
algorithms to historical data.
These requirements combine with the new technologies that are
now coming common place and transforming the capabilities of large data
analysis. Four overarching trends are transforming the industry: e.g.| Data,
predictive analytics, self-service/ embedded analytics and cloud based analytics.
Advance techniques such as data mining, predictive analytics, statistical analysis,
data visualization, text analytics/ natural language processing can all be
applied with e.g.| Data to discover new patterns and relationships opening new
understanding and potentially operational advantage.. This significant trend,
reinforced by the fact that modern predictive analytics tools do not
necessarily require advanced skills, and thus overcome many of restrictions of
traditional predictive tools. Many of us are evolving technologies and tools,
that will analytics and simulation module as part of a supervisory/ operational
experience (similar to alarming). Enabling small forward-looking models to run off
existing systems and history to allow a
forward look based on the situation today. Combine this with users now getting
information for decisions via advanced analytics tools on top of traditional
data sources that they can use themselves(self-service) and immediate value.
Next week I will expand on some of key transformations in the Intelligence BI
worlds that apply in operations.
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