1) Business intelligence systems are rapidly evolving beyond traditional backward-looking financial analysis to incorporate new types of data sources and real-time analytics capabilities. 2) Next-generation BI will utilize predictive analytics to inform real-time decision making and complex event processing of vast amounts of data from both internal and external sources like social media. 3) While new BI technologies provide more information, companies must focus on tools that deliver real business value, such as predictive analytics that can help direct strategic investments based on patterns identified in historical data.

1. BI Breaks Free, Revolution Ahead – Part 1
Dears,
One kiss of death for many IT and corporate business houses are a lack of vision. The business does
not plan for the future, but focuses instead on speed to market and cut cost. That is not to say that
cost savings can't make or break a company but more important is to create a vision for the
company beyond the current situation and BI is one significant tool which provides insight about
both current clichés and guardian angel for the future .
Business intelligence (BI) systems are evolving so rapidly as to be almost unrecognizable. Just a few
years ago, BI meant clunky, backward-looking financial analysis systems and interfaces deemed
unfriendly by users at large. Improved computing power is increasing by orders of magnitude the
amount of data that can be crunched, while the advent of new types of media is changing the kinds
of data to be crunched. At the same time, the rise in mobile business computing has begun to alter
how BI applications are being delivered.
CIOs forging their BI strategies soon will have to grapple with business intelligence systems
that are little more than buzzwords today: social network analysis, organizational network
analysis, context-aware computing, even sentiment analysis. Sounds far out? The BI
Software Wing of a US Super Store is developing software that instantaneously analyzes
shoppers' facial expressions (confused, angry or bored, for example) caught on in-store
video cameras, and alerts salespeople in real time to take appropriate action. If industry
experts are correct, next-generation business intelligence systems -- or business intelligence
technology 3.0, as some call it -- will be a heady mix of internally generated business data
correlated in the blink of an eye with real-time intelligence culled from multiple sources, such as
social media, newsfeeds, video and sensor-enabled environments.
Not only will next-generation business intelligence systems solve the latency issues that dog
many classic BI operations, but business intelligence technology 3.0 will use predictive analytics
routinely, conjuring what-if scenarios to inform real-time decision making. Complex event
processing, the detection of patterns lurking in petabytes of data, will no longer be the sole
domain of the global investment banks or the military. Every time a customer asks about a
product, a company will use powerful analytics software to predict how that customer would be
served best at that moment, based on past preferences and the current context. In fact, Gartner
Inc. recently predicted (presumably by using predictive analytics) that by 2016, all enterprises
will be using next-generation analytics to forecast the future.
But these next-generation BI advances don't mean, run out and immediately update and add a
bunch of compute capacity. It means, be ready
Business intelligence systems for business value
CIOs also need to be ready for the explosion in mobile business intelligence applications.
Although Blackberries and other Smartphones have hosted mobile BI apps for years, their small
screen has been a roadblock to using them, he said. The iPad and other tablets overcome this
problem, and his research shows that CIOs should expect enormous growth in mobile BI.At the
moment, however, companies' main challenge in this revolution in business intelligence systems

2. is figuring out which kinds of BI tools will give them more business value, rather than just more
information, analysts and CIOs say.Companies will discover over the next few years where new
business intelligence systems, such as those for predictive analytics, can be applied to provide
value for their industries. "That is when you invest"
The 'uncharted territory' of business intelligence systems: Predictive analytics
The New York City Housing Authority is identifying already where predictive analytics can
improve the agency's mission of providing affordable housing to poor and moderate-income
families. A simple example: As part of a long-term initiative to figure out which conditions
actually improve residents' quality of life, A team is studying the impact of security cameras on
violent crime. The assumption is that cameras prevent crime. However, after running correlations
on a decade's worth of information from multiple sources including police reports, the team
found that cameras deter vandalism but have no effect on violent crime after they've been in
place for two months, unless they're coupled with other factors, including an effective intercom
system and random patrols by police officers. That information will help direct how the authority
invests in security. This is what Predictive Analytics to offers it gather all source of Information
and predicts the future behavior and actions.
Predictive BI analytics, like many an IT term, refers to an amorphous discipline that involves
using statistical and other complex mathematical analyses of data to predict the future. It is not
exactly data mining, the industry experts like to point out, and it's certainly not data mining for
data mining's sake. Predictive BI analytics identifies patterns in the volumes of data generated by
a business and by relevant external sources. Those patterns are used to predict the future so the
business can adjust its strategies accordingly.
Using analytics to shape business strategy is not easy to do, however, and not just because it can
be expensive or because it's a relatively new undertaking for many but the biggest corporations.
Putting predictive analytics to work in the corporation requires change management skills,
finding the right people and the right mind-set.
How do we implement Predictive Analytics that’s a Sequel, Watch this space for the concluding
part of the Article.
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