The metamorphosis of business intelligence

Business intelligence (BI) is a technology that is being increasingly drawn upon to deliver ground-breaking solutions to corporate decision-makers from shop floor to boardroom.

Today, thanks to a variety of enhancements, upgrades and new-generation developments, BI is available to all in the big corporate environment. And it`s making its presence felt, for the first time, in the small to medium enterprise (SME) space as well.

This is because BI has changed - not only in terms of pricing, but also in terms of performance and focus.

The most significant change to BI has come with the addition of predictive analytics to its platform.

Predictive analytics, as a technology, represents a breakthrough for the IT industry. More specifically, it has presented BI users with a quantum leap forward in terms of decision-making ability.

Previously centred on delivering historical analysis, BI is now able to use this data to present a more accurate, holistic, business-wide view of the future.

One of the most positive implications for a BI - predictive analytics marriage - called BI-Analytics - is that the two technologies can be applied and integrated into business processes at every customer touch point, including face-to-face interaction, telephone, mail, email, or the Web.

Together they can provide the user with:

* Sets for segmentation of business data.

* Statistical process control that allows organisations to monitor and improve processes within their organisation, from tracking the number of rejected parts on an assembly line to the average processing time in a call centre.

* Real-time alerts to impending market changes and trend swings.

* Collaboration options for goal-setting and performance management across the enterprise - or a group of collaborating enterprises.

Significantly, BI analytics, as a technology, is being marketed by developers to software vendors who need to add functionality to their applications to attract defined market niches.

They are offering users a number of key advantages. For example, thanks to BI analytics, companies can now benefit from enterprise application integration (EAI) tools, as well as ETL (extract, transform and load) tools to improve data integrity and develop models that facilitate more accurate forecasting business analysis.

These developments have met the challenges associated with the handling of extremely large data volumes, increasingly complex transformations and the synchronisation of data between disparate applications.

The advantages of these developments, particularly in areas such as time, effort and cost savings, are even more evident when BI analytics is integrated with powerful enterprise applications, such as ERP (enterprise resource planning), CRM (customer relationship management) and others.

Unfortunately, integrating predictive analytics technologies into BI applications also has its disadvantages. The proposed cost and productivity gains are, in many cases, based on the vendors` belief that the applications will be used and delivered without any modifications.

This is rarely the case.

The reality is that customisations are a necessity in this arena. Also, as analytical modules move beyond BI and become an integral part of enterprise-wide applications, vendors are increasingly using analytical processes to trigger transaction events.

Known as closed-loop analytics, this method of "turning information into action" is becoming part of the applications development process, either as a bridge between modules or an initiator of transactions.

The down side is that this process is an inhibiting factor that limits the flexibility of BI analytical structures and activities.

Nevertheless, despite these drawbacks, BI analytics as a new-age technology has tremendous potential. The drawbacks do not, and will not, not diminish the impact and combined power of BI and predictive analytics on critical enterprise applications and key business function areas.

Currently a key challenge for businesses is found in the area of deployment. In order to truly benefit from BI analytics, companies must ensure they have sufficient skills, knowledge and competency to complete the task.

What does his competence in BI analytics entail?

In a nutshell, it consists of three important factors: (1) the ability to effectively use the analytical tools integrated into the application; (2) the effective use of large volumes of data in the analytical environment; and (3) the ability to adapt the tools and data streams to answer new, unanticipated questions.

Corporate data no longer needs to he harnessed in centralised repositories for the BI-predictive analytics partnership to blossom within an organisation.

Together, they can source and collate data from any number of sites and, when teamed with ERP and CRM technologies, put key performance indicators (KPIs) at the immediate disposal of decision-makers - allowing them to make the business goal of proactive and predictive business management a reality.