BI Breaks Free, Revolution Ahead
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
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 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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DC*
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