I’d been thinking of writing a column on p-values, since the claim that data “scientists” can provide valuable predictive analytics is a regular feature of the business intelligence (BI) industry. However, my heavy statistics are years in my past. Luckily, there’s a great Vox article on p-values and how some scientists are openly stating that P<.05 isn’t stringent enough.
It’s a great introduction. Check it out.
This is more of a marketing flavored post as the recent presentation seemed to miss its own point. The title implied it was about fast decision making, but Fern Halper, TDWI Research Director for Advanced Analytics, gave a rather generic presentation about the importance of operationalizing analytics.
Fern gave a nice presentation about operationalizing analytics, but it was not significantly different than her last few. In addition, some of the survey issues discussed were clearly not well thought out. For instance, Ms. Halper listed the expected growth of predictive analytics and web/mobile analytics as if they belonged in the same discussion. The fact that web and mobile are methods of display doesn’t overlap with whether they are used to display descriptive or prescriptive analytics. The growth of those display methods also don’t move away from the use of dashboards in CRM and ERP applications, as was implied, since those applications will migrate views to the new display methods.
The best thing mentioned by both Fern Halper and the SAP presenters was the fact that there were multiple references to that need for multiple data sources. Seeing the continued refocusing of many firms on wide data rather than big data is a good thing for the industry. Big data is more of a technical issue while wide data more directly addresses complex business environments.
Now I’m hoping for more people to begin to refer to loosely structured data rather than unstructured data. Linguists, I’m sure, are constantly amused at hearing languages referred to as unstructured.
The case study was by Raj Rathee, Director, Product Management, SAP. It was an interesting project at Lufthansa, where real-time analytics were used to track flight paths and suggest alternative routes based on weather and other issues. The business key is that costs were displayed for alternate routes, helping the decision makers integrate cost and other issues as situations occur. However, that was really the only discussion of fast decision making with analytics.
The final marketing note is that the Q&A was canned but the answers didn’t always sync up. For instance, the moderator asked one question of Fern, she had a good answer, but there was no slide in the pack about her response, just the canned SAP slide referenced by Ashish Sahu, Director, Product Marketing, SAP, after Ms. Halper spoke.
I think the problem was that the presenters didn’t focus down on a tight enough message and tried to dump too much information into the presentation. The message got lost.
Usually I’m more impressed with the TDWI half of a sponsored webinar than by the corporate presentation. Today, that wasn’t the case. The subject was supposed to be about predictive analytics, but the usually clear and focused Fern Halper, TDWI Research Director for Advanced Analytics, wasn’t at her best.
Let’s start with her definition of predictive analytics: “A statistical or data mining solution consisting of algorithms and techniques that can be used on both structured and unstructured data to determine outcomes.” Data mining uses statistical analysis so I’m not quite sure why that needs to be mentioned. However, the bigger problem is at the other end of the definition. Predictive analysis can’t determine outcomes but it can suggest likely outcomes. The word “determine” is much to forceful to honestly describe prediction.
Ms. Halper’s presentation also, disappointingly compared to her usual focus, was primarily off topic. It dealt with the basics of current business intelligence. There was useful information, such as her referring to Dave Stodder’s numbers showing that only 31% of surveyed folks say their businesses have BI accessible to more than half their employees. The industry is growing, but slowly.
Then, when first turning to predictive analytics, Fern showed results of a survey question about who would be building predictive analytics. As she also mentioned it was a survey of people already doing it, there’s no surprise that business analysts and statisticians, the people doing it now, were the folks they felt would continue to do it. However, as the BI vendors including better analytics and other UI tools, it’s clear that predictive analytics will slowly move into the hands of the business knowledge worker just as other types of reporting have.
The key point of interest in her section of the presentation was the same I’ve been hearing from more and more vendors in recent months: The final admission that, yes, there are two different categories of folks using BI. There are the technical folks creating the links to sources, complex algorithms and reports and such, and there are the consumers, the business people who might build simple reports and tweak others but whose primary goal is to be able to make better business decisions.
This is where we turn to David Clement, Product Marketing Manager, BI & Predictive Analytics, IBM, the second presenter.
One of the first things out of the gate was that IBM doesn’t talk about predictive analytics but about forward looking business intelligence. While the first thought might be that we really don’t need yet another term, another way to build a new acronym, the phrase has some interesting meaning. It’s no surprise that a new industry where most companies are run by techies focused on technology, the analytics are the focus. However, why do analytics? This isn’t new. Companies don’t look at historic data for purely nostalgic reasons. Managers have always tried to make predictions based on history in order to better future performance. IBM’s turn of phrase puts the emphasis on forward looking, not how that forward look is aided.
The middle of his presentation was the typical dog and pony show with canned videos to show SPSS and IBM Cognos working together to provide forecasting. As with most demos, I didn’t really care.
What was interesting was the case study they discussed, apparel designer Elie Tahari. It’s a case study that should be studied by any retail company looking at predictive analytics as a 30% reduction of logistics costs is an eye catcher. What wasn’t clear is if that amount was from a starting point of zero BI or just adding predictive analytics on top of existing information.
What is clear is that IBM, a dinosaur in the eyes of most people in Silicon Valley and Boston, understands that businesses want BI and predictive analytics not because it’s cool or complex or anything else they often discuss – it’s to solve real business problems. That’s the message and IBM gets it. Folks tend to forget just how many years dinosaurs roamed the earth. While the younger BI companies are moving faster in technology, getting the ears of business people and building a solution that’s useful to them matters.
Fern Halper did a nice review of the basics about BI, but I think the TDWI view of predictive analytics is too much industry group think. It’s still aligned with technology as the focus, not the needs of business. IBM is pushing a message that matters to business, showing that it’s the business results that drive technology.
Businesses have been doing predictive analysis for a long time, as long as there’s been business. The advent of predictive analytics is just a continuance of the march of software to increase access to business information and improve the ability for business management to make timely and accurate decisions in the market place. The sooner the BI industry realize this and start focusing less on just how cool data scientists are and more on how cool it is for business to improve performance, the faster adoption of the technology will pick up.