Business intelligence provides companies with valuable historical information, keeping many organizations competitive during tough times. Purchasing departments, for example, use business intelligence (BI) to monitor, choose and negotiate with suppliers. Customer service departments use it to identify problems. Airlines use BI to monitor the status and performance of their fleets and personnel.
Though BI provides many advantages, it has limited ability to predict, forecast, and make inferences on unknown facts and relationships – for instance, predicting customer behavior, the probability of fraud, or suggesting the next best offer during an online transaction. For these reasons, most companies are enhancing their BI practices to include predictive analytics and data mining. This combines the best of strategic reporting and basic forecasting with additional operational intelligence and decision-making functions. By developing the capability to move from insight to action, leading businesses are combining historical and predictive analysis to determine what immediate actions to take.
A foundation for advanced analytics
In today’s economic environment, “good enough” is no longer enough. For example, in fraud analysis, knowing what happened yesterday and stopping the same thing from happening tomorrow is only step one. With advanced analytics, organizations can now identify fraud before they write a check, refund money or settle a claim.
This seems simple enough, but even companies with sound enterprise data management practices, processes and infrastructures that were built for traditional BI reporting cannot always handle the complex requirements and unpredictable workloads of operational analytics. The good news is that much of the work that establishes enterprise-class business intelligence – creating consistent data, rigorous systems governance, and sophisticated data integration and data quality – can serve as a sound foundation for advanced analytics.
However, the question often raised is how to use existing platforms and maintain predictable performance and governance without limiting the ability of analysts to explore, transform and develop data and models in an ad hoc fashion.
Advanced BI and need
– Applying advanced analytical techniques to data to solve problems Applying advanced analytical techniques to data to solve problems that were previously difficult (if not altogether impossible) to solve
• The propensity of customers to choose one action over another, or to predict who among current customers will no longer be customers six
months from now, or to predict which prospects are most likely to become customers
• Advanced analytics are more encompassing than data mining
• This empowers enterprises to optimize their efforts on the most-likely-to-be-profitable customers.
Advanced analytics impact
– Advanced analytics can provide a compelling even significant Advanced analytics can provide a compelling, even significant, advantage
– Does a better job of attracting the right customers, and positioning the right products to those customers. Increase customer base with the focused attention
– Cost savings: Identify the appropriate supply, no wasted warehousing etc
– Customer Retention: Identifies potential churn and allows for preventive action
– Merges data and analysis for insight in the present and foresight into the future into the future
– Helps in customer management strategy, developing acquisitions modeling, discern fraud,
– Application of Advanced Analytics vary from enterprise to enterprise, based on specific needs and goals.
Advanced Analytics Future
• The role that advanced analytics will play in industries as well as in individual enterprises in the next 3 to 5 years
– Less need for specialized software for trend analysis– Analyze on Less need for specialized software for trend analysis Analyze on the data within DW
– Help us move toward true real-time decision making in terms of how we work with customers
– Analysis of Unstructured Data will result in improved medical applications, scientific applications, or even military applications
• for example non-structured data such as CAT scan images
• How can an enterprise best face challenges and turn them into opportunities?
– Requires application of specialized technology, gather and then organize Requires application of specialized technology, gather and then organize data, and then take a fresh look at the best way to run the enterprise.
– External challenges are even more plentiful
• increased competitiveness
• increased business regulations
• increased customer fluctuations, etc
– An opportunity to bring together business and analytic technology for sophisticated insight and forecasting for sophisticated insight and forecasting
– These challenges represent an opportunity for an enterprise to differentiate itself from the competition.
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