Eight Levels of Analytics

Just after writing yesterday about “six degrees of separation,” I discovered this article, “Eight levels of analytics,” and so I was probably destined to write this post on 11/11. Today I also saw “‘Bessemer’s 10 Laws of Being SaaS-y’ Updated for Cloud Computing,” from Bessemer Venture Partners, and “Seven SaaS Revenue Streams” from Sixteen Ventures, both of which contain numerical titles and very interesting recommendations and analyses of SaaS revenue streams by venture capitalists. I even began this post at 11:11 and finished it at 12:22 for a total time of 1:11!

If there is a pattern in my numerology today, business intelligence (BI) software could help discover and understand it, and predict what will happen over given timeframes. The ability of BI to find patterns, answers, and forecasts for complex business problems and data sets is high on the list of CIOs today. According to this article by Birst Founder and CEO Brad Peters, companies want to increase their adoption of BI beyond executives to front-line decision-makers, and software-as-a-service (SaaS) BI is making this possible due to low cost, quick deployment, and return on investment (ROI).

Although the article is basic for BI cognoscenti, “Eight levels of analytics” by Jennifer Kavur of Computerworld Canada is based on a presentation by Jim Davis, senior vice-president and chief marketing officer of SAS Institute Inc., a leading on-premises BI software vendor. The article provides a good, quick, and simple overview of BI for those who just read that their company is planning a large rollout of SaaS BI to improve decision-making and business performance.

Below are the eight levels of analytics, according to Jim Davis. The first four are the most commonly used and they analyze past data. The second four look at the future and are the hottest areas in BI today because businesses now want more help in modeling the future.

1. Standard reports answer questions like “What happened?” and “When did it happen?”

2. Ad hoc reports answer questions like, “How many? How often? Where?”

3. Query drill-downs answer questions like, “Where exactly is the problem?” and “How do I find the answers?”

4. Alerts answer questions like, “When should I react?” and “What actions are needed now?”

5. Statistical analysis answers the questions, “Why is this happening?” and “What opportunities am I missing?”

6. Forecasting answers questions like, “What if these trends continue? How much is needed? When will it be needed?”

7. Predictive modeling tells users what will happen next and how it will affect the business.

8. Optimization answers the questions, “How do we do things better?” and “What is the best decision for a complex problem?” This includes areas such as price optimization, markdown optimization and size optimization. This isn’t just about cost-cutting and can be the difference between success and failure for an organization.

SaaS BI companies to watch as the SaaS BI market continues to grow include Birst for reporting and analytics, eiVia for forecasting and predictive modeling, Mimiran for price optimization, and mashmatrix Dashboard for enterprise data mashups, dashboards, and visualization.

It will be interesting to look back at this post on 11/11/11 and see how high the SaaS BI market has grown since 11/11/09. We can use predictive modeling to predict the SaaS BI market growth now and set an alert to remind us to run that report at 11:11 on 11/11/11.

Do you predict that your organization will have SaaS BI implemented for more decision-makers by 11:11 on 11/11/11?