38% of organisations use BI for analytics. 85% will do so in 3 years
New research shows 38% of organizations surveyed are practicingadvanced analytics today to drive sales forecasting using new sales forecasting software and business intelligence. Within 3 years 85% say they intend to. The current recession is teaching everyone to understand business, customer better.
Why such a dramatic change? The use of advanced analytics such as exponential smoothing, trending analysis, business intelligence is driving up organisations need to intepret constantly changing business environments (as seen in the ongoing recession and the resultant market turmoil), as well as to discover opportunities for cost reductions and new sales targets (which are key to surviving and thriving in a down economy). Its the logical extension of the lean manufacturing ethos. Read Fill the Gap between Excel & Business intelligence.
Organisations driving these using advanced analytics: query-based analytics (which relies on complex SQL statements to define recent business events) and predictive analytics using sales forecasting software (which uses data mining and statistical methods to anticipate future events). Much of this activity is currently being done in spreadsheets.
There are many applications of advanced analytics, but most of
them involve discovering relationships, reading trends, anticipating the future, and adapting to change. Working with the right data in the right condition is key to achieving these goals.
Discover relationships. Whether advanced analytics is data mining, statistics, artificial intelligence, or complex queries, you will discover and quantify important relationships that you may have been unaware of. These relationships can reveal fraud, define customer segments, group products of affinity, and link field conditions that lead to product failures. The newly
discovered relationships target marketing campaigns more accurately, develop effective merchandizing strategies, and improve product quality.
Sales forecasting software for example will anticipate the future. For example, predictive business models quantify a customer’s proclivity to churn, thereby giving you an opportunity to retain the customer. Predictive financial models can assist with various types of forecasting. Likewise, predictive analytics can quantify future risk for pragmatic applications in actuarial tables or loan approvals.
Understand and adapt to change. On the one hand, advanced analytics can help you understand change in the form of rising costs or new customer behaviours. On the other hand, the discoveries made through analytics can lead to positive changes that help your business adapt to an evolving world.
From a business standpoint, benefits are obvious however it needs specialised analytic tools and analytic databases from a technology standpoint. Organisations new to advanced analytics will need to reach beyong current IT landscape, reporting and data capabilities.
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