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 or just call me at 816-781-2880.

Oracle Analytics Cloud Service is Now Live!

Now that it’s released, we can talk about Oracle Analytics Cloud (#OAC—pronounced “Oak”).  I’ve heard some confusion about what exactly this is and isn’t, so let me try to clear this up a bit.

Basically, OAC is Oracle Business Intelligence Enterprise Edition (#OBIEE) running in the #OracleCloud. In case you thought that was what Oracle Business Intelligence Cloud Service (#BICS) was, here’s how the two differ:


  • Oracle managed
    • Can modify settings
    • Can install your own plugins
    • You determine when you upgrade
    • Can SSH into server
    • Full control over networking including VPN
  • Flexible licensing
    • License by OCPU, unmetered and metered
    • Bring own license of Oracle DBaaS or Exadata Cloud
    • Requires IaaS for network and storage
    • Std $3,000/month/OCPU, EE $6,000/month/OCPU
    • Min 1 OCPU: Std $36,000/year + DB + IaaS
    • Min 1 OCPU: EE $72,000/year + DB + IaaS
  • Includes Essbase
  • Cloud Data Modeler or use BI Admin Tool


  • User managed
    • Cannot modify settings
    • No plugins
    • Oracle updates/patches for you
    • Customer cannot access server
    • Limited network and security options
  • Simplified licensing
    • License by user
    • Requires Schema as a Service
    • No IaaS required
    • $1000/month + $150/month/user
    • Min 10 users, 50GB: $30,000/year
  • No Essbase
  • Cloud Data Modeler




So why OAC? Well, BICS is great for a departmental solution. But if you are in IT and want finer control over your BI instance, you’re going to want OAC. BICS is great to get started, but eventually you’re going to want to graduate to OAC to gain finer control over your environment. Also, Oracle is adding new capabilities to OAC such as access from the new Day By Day app coming out any day now. Also, available with OAC is scenario modeling with Essbase.

Another big difference between BICS and OAC is how Oracle is treating the required database license and infrastructure costs. With BICS, Oracle simplified the pricing model, and required a Schema as a Service license (limited to 50GB, and 300GB/month data transfer). That’s what the extra $1000 per month was for. But for organizations that already have a Database as a Service license or want more flexibility, with OAC you un-bundle the database license, storage costs, and Infrastructure as a Service costs and pay for what you need. Finally, with OAC we now have the option of metered licensing by the hour for situations where you are not going to be running an instance all the month.

So what’s the difference between Standard Edition OAC and Enterprise Edition OAC? Well, Standard Edition is basically an OCPU-licensing of Data Visualization Cloud Service (#DVCS). Standard Edition is great for departments that access spreadsheet data or raw data via connectors. Each user needs to model the data (light ETL work, joins, etc.) for himself. This can be liberating at first since you’re not relying on some centralized IT department to “map” the data, but after a while users will tire of having to redo the mappings each time they want to share information.

With OAC Enterprise Edition, you add in the capabilities of Answers and Dashboards, and the ability to access a Common Enterprise Information Model (CEIM) that you can edit using the regular Oracle BI Admin Tool, including use of Session and Repository variables. These are high-powered tools that you are going to need in an enterprise deployment of Oracle BI.

If you want to go deeper on this, give us a call or shoot me an email. OAC is now available and ready to use. It’s time to get serious about Oracle BI on the cloud!

Tim Vlamis to Present at GLOC 17

Tim Vlamis will be returning to Cleveland, Ohio to present at the Northeast Ohio Oracle Users Group "Great Lakes Oracle Conference" on May 17th and May 18th, 2017.

This year Tim's presentations include:

For more information or to register for GLOC 17 visit

For more information on Vlamis presentations visit our papers page.

Join us in Celebrating 25 Years on May 1st

Vlamis Software Solutions will be hosting an open house on Monday, May 1st from 4:30pm to 7:00pm at our headquarters in Liberty, Missouri. If you are in the Kansas City area and would like to join us in celebrating the milestone we would love for you to join us. If you would like to celebrate remotely please sign our virtual guestbook! You can also tweet @VlamisSoftware using #Vlamis25years

For more information please visit our Anniversary Party page.