Picture of Wisdom

Pictures convey a thousand words. But sometimes, the words may be in another language. For example, there is an article about knowledge management that describes the progression of data to information, knowledge, and wisdom that I liked. Here’s the URL: Data, Information, Knowledge, and Wisdom

Now, I figured that these concepts could be useful in describing the value of a business intelligence system to a group of business users (in this case, Medical, Clinical, and Regulatory folks.) After all, they know what data is, and they have systems that store information about each of their respective domains. They even shared some information in a few reports. But I was trying to convince them that we could use the data and information they had and go even further. I was hoping to build out some predictive analysis and pattern recognition dashboards to take their current level of Business Intelligence from “dimwit” to “genius” level.

But the picture in the article that caught my attention was not going to convey anything to this group. Here’s my version of that picture:



This picture was theoretical concept only. I needed to tie it to some systems architecture. So I imposed some familiar terms and concepts to lock the idea down. I ended up with a flow that was familiar, yet was able to convey the need to continue along the spectrum and give me some talking points about my vision for this new BI system. Here is my new, improved picture.


(can't read the picture? click on it for full size.)

Well, it worked. I was able to discuss possibilities in both visionary and pragmatic terms, pointing to points of the picture and describing algorithms, graphics, alerts and all the other cool BI stuff. It was much better than trying to do so pointing to a box that said “wisdom”. So, thanks to the guys at Systems Thinking for get me thinking about the value chain of BI in something other than servers, clients, and code.

Data Warehousing Best Practices

Data warehousing is not a new technology by any means. But it is one of those more intimidating subjects to many folks that don't have the experience or exposure to them. I've spent the past several years working on various data warehouses and data marts in the Life Science's industry, and have a developed a few tips and techniques to avoid some of the most common problems. Lets face it, Data Warehouses have a higher than normal rate of cost overruns and even project failure rates than other systems. This is largely due to the fact that they are complicated projects with several focused and specialized teams that aren't always communicating well. So I've put together a list of these "best practices" and my company, Intrasphere, published them in a white paper. Which makes it a prime candidate for sharing here.

Now, if you are a data warehouse guru, and you are looking for bleeding edge approaches that will shake the earth as you read, don't bother. This white paper is written for project managers and IT managers that may not have tons of data warehousing experience, but are looking for some pragmatic and practical advice they can both understand and use.

Data Warehouse Best Practices

As always, I appreciate your feedback and comments.