Traditional business intelligence leaves executives with the same amount of work, but with even more information to sort through. The number of decisions, the unit of work, is not diminished.

Traditional Business Intelligence asks, "What information do you need to make better decisions?" The outcome is hopefully beautiful well designed reports and dashboard that support decisions.  The problem is that you still have to make decisions.

Decisions are work.  Having more information doesn't reduce the amount of work required to make decisions. In fact, it makes decisions more work.  More information does not create less work.

The flaw is thinking that the business decisions are calculations.

People don't care about business intelligence software, they care about what it can do for them.  CAN is built on this idea.  Instead of focusing on business intelligence software, we are focused on providing answers directly to our clients.  We are improving this process by launching the CAN Portal.  The Portal is how we work with our clients.  It will allow you to get better answers faster and more securely.

What are your objectives?

At Contemporary Analysis (CAN), we take a completely new approach to helping companies and organizations get more out of the information they have access to. At our core is the idea that businesses should be working smart and hard.  At CAN, we are different because we always keep the human element, actionable impact, and added value at the forefront of our development process.

I overheard someone the other day telling their friend that there was no way their CEO deserved a million dollars a year.  "What does our CEO even do anyway, " she said?  "I wish I could come in late, play golf all day, and have no responsibilities.  I would do the job for $500,000 and do it better than him..."

I wish I could say I turned and scolded her about how her CEO probably was at a networking event while she was with her family, works most weekends, including holidays, and never shuts off the pressure of running a business, but truth be known, I didn't know if that was the answer.

The visual world is extraordinarily complex.  For example a quick scan of my desk reveals hand-written notes, dry erase markers and USB thumb-drive.  While I recognize these objects rapidly, I experience them at a basic visual perceptual level long before I can label or describe them.  This low level of perception is what is called preattentive processing, or visual processing that occurs without deliberate attention.  Preattentive processing can be used to create dashboards that easily communicate extraordinary amount of information per pixel and need very little effort to understand. Download our eBook, "Dashboards: Take a closer look at your data".

At CAN, as needed we use the visualization software Tableau to create reports and dashboards for our clients.   Also, because Tableau is capable of handling large amounts of data very quickly, we’ve started using it to explore data visually during the data discovery stage of each project.  We use Tableau to check the quality of data, find outliers, and get a sense of the properties of a data set, such as dispersion, central tendency, clustering, etc., before we apply statistical analysis or build predictive models.  A Tableau feature, especially useful for exploring data, are Reference Lines.

This blog post explains a few ways that CAN uses Tableau to explore a data set.

Jefferson joined CAN before we had this blog, our website, our products, or our office at 1209 Harney St.  This video is about how and why he decided to join Contemporary Analysis.  He knew we had potential and decided to become apart of CAN's future and the future of data science.  CAN specializes is predictive analytics.  Predictive analytics involves collecting data about your business and customers, and then applying theory and math to build simple systems to help you work more effectively and efficiently.

CAN is helping one of our clients improve their asset management strategy, by building predictive models to determine when heavy equipment is most likely to fail.

Using mean absolute error, CAN helps our clients that are interested in determining the accuracy of industry forecasts. They want to know if they can trust these industry forecasts, and get recommendations on how to apply them to improve their strategic planning process. This posts is about how CAN accesses the accuracy of industry forecasts, when we don't have access to the original model used to produce the forecast.

My name is Branden Collingsworth. I interned at Contemporary Analysis this summer, and joined the team full-time January 2nd, 2012 as a data scientist.  As a data scientist I use tools from econometrics, statistics, operations research, and data mining to solve our client’s business problems.