Can BI be recession proof

Seems 2008 was a good year for business intelligence companies.  Some of you may not want to read that others are prospering, while many struggle (or at least worry about the future) but business intelligence could be a beacon of light for IT companies.

Here is a small sampling of growth numbers for traditional on-premise vendors that I found.  The article also said the business intelligence market is larger than Forrester's estimation of $8.5 billion.

• SAP Business Objects posts double digit growth.
  
• IBM Cognos reported 12% revenue growth for first nine months.
  
• Microstrategy growth at 8%.
  
• SAS Institute growth at 5%.
  

For many companies, these could look like great numbers in these economic times!  I hope you are apart of this growth in some shape or form.

Then I thought about the numbers (briefly).  Typically B2B sales take several months, maybe 6 - 10 months or longer to complete.  Meaning these growth numbers are from sales initiated in 2007 or early 2008.  Really not when the recession was causing havoc.  So the true test to the resilience of business intelligence will be the 2009 numbers.

"Is anyone looking to buy in 2009?", is the real test.

While growth numbers may or may not be interesting.  What could be very interesting would be to compare SaaS BI company growth with traditional on-premise.  Get the real numbers out there.  Of course, the size of revenues may be apples vs oranges but the percent growth would be interesting, yes?

Here are the 60 fastest growing companies, which SaaS vendors comprise much of this growth.  The link is near the end of the post, if you're not interested in healthcare on the internet.  Unfortunately I haven't found comparable numbers I could use (send some along if you know of any - we could do quick collaborative analysis).

As an aside, I am reading more and more about business intelligence being an add-on to ERP packages.  The first link above mentions the BI market of $8.5 billion doesn't include BI tools packaged with ERP, HR, and customer analytics applications.

I think it's inevitable that on-premise BI's future will be an attachment for ERPs.  Where that leaves enterprise-wide BI, I'm not sure.  Perhaps the value of enterprise-wide BI will be for large organizations with deep pockets to pay for the on-going costs.

If you're looking for emerging bright light technologies, check out:

• predictive analytics
  
• business activity monitoring (or complex event processing)
  
• text analytics
  
• column-based databases
  

Enjoy!

Don't live in the past, predict the future

If Jethro Tull is "living in the past".  And hindsight is 20/20.  Then you're reading this blog in the moment to learn how to predict the future.  From a business intelligence perspective, that is.

Thanks to Accutate for providing this short article on Predictive Analytics.

"Predictive analysis can be an extremely useful tool for many different types of businesses. In fact, where there is any type of data warehousing there should be implementation of a business intelligence program that includes predictive analysis. However, in order to learn how your business can profit from this facet of business intelligence, you are going to have to understand exactly how and why predictive analysis works.  

The main idea of predictive analysis is to use current and past data to predict future events. The goal of the statistical techniques used in predictive analysis is to determine market patterns, identify risks, and predict potential opportunities for growth. In addition, data relationships can be reordered to determine the most plausible outcome of possible solutions and patterns can be recognized that might have the power to alter the outcome of a probable event.  

One of the most important aspects to reliable predictive analysis is data quality. The information provided by predictive analysis can only be as effective as the abundance and accuracy of data available. Data quality is absolutely necessary to the process of predictive analysis. In order to attain accurate business intelligence, companies must maintain quality data.  Predictive analysis requires both past and current data about many different things including customers, businesses, products, and the economy. All of this information is used to draw relationships and patterns between sets of data. If the data is accurate and well maintained, then the business intelligence produced will be high quality as well.   

In the past, predictive analysis was mainly used for newly emerging technologies. However, in recent years these practices have quickly started to become common for mainstream businesses. There are a few differences between the ways that these techniques are currently used and how they were used in the past. One of the main reasons for these differences is why companies use predictive analysis. In the past, these techniques were used for long-term analysis of market and consumer trends. However, in recent years, the mainstream implementation of predictive analysis techniques has tended to focus more on immediate, tactical uses. Because of the “real-time” nature of this business intelligence, more and more companies are using predictive analysis as standard in making predictions about particular industry markets and consumer trends.  

Some of the industries that have started utilizing these business intelligence techniques include telecom, insurance, pharmaceutical, and financial industries. All of the companies in these different business sectors have been able to use predictive analysis to make the right decisions to move their businesses in a positive direction. These processes can help with economic predictions as well as predicting the behavior of businesses and consumers. This type of information, made available in an efficient manner by business intelligence, is understandably invaluable. It can turn a simple prediction into intelligence that is more precise than even the most educated guesses. Predictive analysis with appropriate attention paid to data quality has made it easier than ever for businesses to make accurate market and consumer predictions and thus smarter decisions for the growth of their company."