What is the Business Value of Business Intelligence?

Maybe we should define Business Intelligence (BI). According to CIO magazine,“Business intelligence, or BI, is an umbrella term that refers to a variety of software applications used to analyze an organization’s raw data. BI as a discipline is made up of several related activities, including data mining, online analytical processing, querying and reporting. Companies use BI to improve decision making, cut costs and identify new business opportunities. BI is more than just corporate reporting and more than a set of tools to coax data out of enterprise systems. CIOs use BI to identify inefficient business processes that are ripe for re-engineering. With today’s BI tools, business folks can jump in and start analyzing data themselves, rather than wait for IT to run complex reports. This democratization of information access helps users back up—with hard numbers—business decisions that would otherwise be based only on gut feelings and anecdotes.”

That isn’t bad and pretty much sums up the cavalcade of technology that is now called decision analytics or business intelligence. Notice the definition does not conclude with the purchase and implementation of software applications.

Why would any organization try to implement BI or a BI program? Many companies already have several tactical implementations of smaller data analysis or BI products. Can’t we call it done if we plug in various technologies in the areas where they are needed?

Most Organizations, that have taken this tactical approach don’t realize the value that BI can provide with he reaction time, monitoring and mission-critical results that can be found in successful strategic implementations.  Being able to monitor, measure, and even predict how the enterprise is performing against business goals helps leaders determine whether  the organization is on or off track and more important why- with the ability to change direction as necessary.

What if we compare this situation with other, more mature mission-critical technologies such as email? Would it be efficient, productive and cost effective to have pockets of employees on separate email systems, some of which may not integrate or communicate effectively with the other systems?  How would this impact the productivity of the work force?

In a BI scenario, if the information is siloed across various department how can decision-makers ever get to a consistent view?  They are stuck with manual reconciliation, spreadsheets or data integration efforts to produce enterprise level data to support their decision.

Taking a strategic approach to BI puts executives in a very different place.  The benefits include:

  • Direct access to enterprise-wide information for business users to make critical decisions.  This increases overall company productivity and accuracy.
  • Improvement in collaboration and overall business effectiveness.  This includes better utilization of resources, consistent view of critical business data, reliable corporate metrics, and the measurement of progress against business goals.
  • The ability to reduce total cost of ownership (TCO) for IT and increase the return on investment (ROI) for software and hardware.  This will also increase the amount of time that IT can spend on strategic work rather than busy work.

Arriving at this level of BI maturity will take time, effort and resources.  However, it took years, generations or even a century to arrive at the current state of data silos and technology.

How to move forward?

  • Take a series of smaller steps, with each success building toward the analytics road-map you have in mind.
  • Consider it a journey.  It needs to reflect the needs of the company today and tomorrow and is never “finished.” As business goals, environment and economies change, so should your BI roadmap.

Technologies will come and go, but with the right strategy you can insert the best of breed solutions where they help most and the company will continue to see the benefits of true data intelligence.

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By | 2017-10-23T16:00:15+00:00 February 19th, 2016|Big Data, Project Management|0 Comments