Business Intelligence

Outliers, Self-Importance, and Shared Understanding

I recently attended another overview of machine learning and analytics, but this time it was at my business school’s reunion and delivered by one of the most prominent thought leaders in the world of analytics. Nothing he said was wrong and I agreed with much of the sage advice he had to offer. Also, there is only so much that can be communicated in a 45 minutes session, but I was somewhat disappointed. This session was designed for executive level business leaders not working directly in the field of analytics. Here were the two big pieces of advice:  

  • Don’t lose the outliers and engage in too much aggregation. That’s where all the good stuff is (with the outliers). 

  • Place analytics within the heart of the business of the organization. It’s the most important thing. 

Fine and good. It’d be hard for me to argue against the potential usefulness of true outliers. However, I do think that the usefulness of outliers is highly dependent on the situation. It’s midway down my “most important” list. You might say it’s a bit of an outlier when too much aggregation rubs out the most meaningful insights.  

I also agree with placing analytics at the center of the business. However, I freely recognize that this is what every functional business discipline advises; finance professors recommend that financial analysis be placed at the center of all business decisions and practices, human resource professors advise that there is nothing more important than world-class personnel practices, business operations professors argue that more money flows through operations than any other part of the business and that operational effectiveness largely determines the strength and future of the corporation, etc. It’s not wrong to advise that analytics be placed at the center of everything, it’s just a teeny bit myopic.  

Here’s a seldom heard perspective I’d love to hear from an analytics thought leader addressing executives. In fact, I’d likely put this at the top of my personal “most important” list.  

The hardest work of analytics is fostering a shared understanding of organizational position, performance, and priorities.  

It’s a group thing, not an individual thing. Designing analytic dashboards is more like putting together a great restaurant with a brilliant kitchen combined with a strong staff and fantastic atmosphere than it is like writing a novel or painting a picture. Restaurants need a team of people working together and are enjoyed most always by people dining in groups, not alone. Great restaurants are about shared experiences, not solitary impressions. When executives share an understanding of evidence, coherent decision making can take place. At that point, the analytics system is supporting the organization, not driving differences in perspective. But great dashboards don’t happen by accident and they don’t arise naturally from a single great artist working alone. It’s a shared thing, not an individual thing.  

Tip on using New Waterfall Viz in DVD 3.0

In preparing for my presentation on What's New in DVD 3.0 Webinar (tomorrow!), I found that the new waterfall visualization was geared towards balance measures (like a checking account balance) instead of a flow or performance measure such as profit.  One of the key differences is that for a flow measurement the value can easily be negative.  In any business, you may lose money in a given period, but you hope to make up for it in prior or future periods.  In addition, flow analysis can be great for showing sources of gains and losses.  A waterfall graph typically shows this very nicely.

But the waterfall graph in DVD 3.0 is designed for balance information.

In order to use the waterfall graph, you can convert from the flow measure (such as profit) to a balance by using the RSUM function in OBIEE.  Put simply, RSUM(profit) gives you the running sum of profit for the year, adding up each month.  It accumulates the profit which is exactly what you need in order to use the waterfall graph.

So, on the left you can see a waterfall graph of an inventory balance.  No special formulas needed.  On the right, you can see a waterfall graph of profit.  In this case, we actually use the cumulative profit measure so we can properly visualize the periods where we lost money.  We can easily see that we ended the year with a total of $9346.

Tim Vlamis in a 2 Minute Tech Tip - Data Visualization: Just Say No to the Default Sort Order

Tim Vlamis was asked by Bob Rhubart of Oracle Technology Network to share a 2 Minute Tech Tip during his recent trip to the Great Lakes Oracle Conference in Cleveland, OH.

Tim's tip focuses on the importance of applying your own sort order when visualizing data.

See Tim's speedy (33 seconds!) Tech Tip below or read more from Bob Rhubart!

For Organizations New to Predictive Analytics, Bet on a Process and Not on a Project

Advanced Analytics Projects are Atypical

Successful projects using data mining algorithms often have returns that are well above normal business Returns on Investment. 200%, 500% or even 1000%+ ROIs are not unheard of. So why isn’t there a rush to do more Proof of Concept projects? Having designed and pitched dozens of these, I have concluded that it’s largely because of corporate politics. I’m not using the term “corporate politics” in a pejorative sense, but rather as a fact of life that results from normal business structures and organizational dynamics. So perhaps the question is not “how do we fight better to get funding for new data mining projects?”, but rather it’s “how do we lower the political risk associated with Proof of Concept projects using data mining algorithms and predictive analytic techniques?” We recently did a program with a client which did exactly that.

 

Place a Lot of Bets

Think like a venture capitalist. Place a large number of small bets rather than one or two large bets. We recently led a predictive analytics summit workshop at a client’s headquarters where representatives of different functional areas of the corporation were invited to bring their own data for a day-and-a-half of intense data discovery using advanced data visualization and predictive analytics tools (Oracle Data Visualization Desktop and OBIEE 12c). There were roughly 40 participants from groups as diverse as sales, service, finance, operations, and marketing. Everyone sat at round tables organized by function in one large room (a table for sales, a table for finance, etc.). The participants were largely comprised of the business intelligence system’s “power users”. A few had engineering or technical backgrounds that gave them a leg up in statistics and analytics, but most were just smart people interested in their business and data analysis. We also had plenty of help on hand including representatives from the IT staff who could solve potential data connection or data structure issues, a couple data scientists who knew statistics cold, some experts in the software interface who could help with “where do I click?” questions, and some support staff who made sure that all the conference logistics were handled.

 

Amazing Returns

The results were incredible. One “discovered” insight provided the >1000% ROI that qualifies as a “home run”. The workshop participant made an appointment with his VP to share the insight and its visualization for the next business day (why wait?). In totality, more than 30 projects were identified as potentially significant areas for future work and development. Was it all sunshine and unicorns? Not at all. Some people struggled to get their data sets fully prepped for predictive analytic processes. Others changed their minds during the middle of working on something and never really finished an analysis. But there was no stigma attached to not hitting the ball out of the park. Everyone who was there contributed to the success of the overall workshop. Perhaps one of the most significant outcomes was the cross-functional collaboration between the different teams. A few hours into the workshop after walking around and observing everyone and helping some people, we identified some interesting and promising results. We asked people to present their initial findings and visualizations to the entire room in the context of their business problem. It got people thinking broadly about the business and how their work might intersect with others’ work. It also showed different visualization and analytic techniques. And it provided a bit of a break from discovery work without losing momentum in the workshop.

 

The Secret to Success is in the Preparation

Everyone who does data mining or predictive analytics knows that half the battle and more than half the work is in preparing the data. Likewise, there is a lot of work in preparing a successful workshop and setting everyone up for success. We did several things and leveraged several of our internal processes we typically use on consulting engagements. All participants were required to write up their business cases in advance (we provided them with an outline of questions). Participants were required to submit their individual data sets in advance based on explicit directions. Finally, all participants were asked to review introductory articles and videos on predictive analytics in advance so a common foundation of terms and concepts could be leveraged in the actual hands-on work. There were also “preworkshop” meetings where we reviewed the process and answered questions and made sure that people were making progress on their business cases and data sets. Using one of the data sets, we developed a training exercise customized to their data with a step-by-step data discovery process so that participants were able to see theory applied in practice.

 

The Best Way to Get Started is to… Start

Want to get traction and help predictive analytics get going your business? Consider sponsoring a multi-day workshop for a sizeable (but not enormous) group of power users with several different business cases. You may not know precisely which ones will be the winners going in and the proctors may have to accept some degree of discomfort in not knowing the details of the “live” data sets, but it’s basically what venture capitalists do. They spread their bets, enable and assist where they can, and somehow, they manage to do pretty well.

If you want to talk about your business and how a workshop might help, send me a note to tvlamis@vlamis.com or just call me at 816-781-2880.

Oracle BI SampleApp v607 and Resources Webinar, August 16, 2016, Noon Central

Join Dan Vlamis, Oracle ACE Director, as he demonstrates Oracle Business Intelligence using the latest release V607 from the Oracle BI SampleApp team. SampleApp is known in the Oracle BI consulting community as THE go to platform for showcasing what Oracle BI is capable of. Release V607 of SampleApp runs on version 12.2.1.1 (first 12c patch release) of Oracle Business Intelligence Enterprise Edition. Expect a fast-paced presentation with a live demo using a brand new SampleApp image. 

The webcast will also include other demonstration and learning platforms Oracle is rolling out to users. This platform is geared towards Oracle BI (and Data Visualization Desktop) users, allowing them to learn new techniques, and even to download additional visualizations into the Oracle BI platform.

Register now for this popular webinar! Tuesday, August 16, 2016 at noon Central