A short overview of the subject on Forbes.
My Forbes article about how I think the interesting research is a step towards passing the Turing Test.
Almost a month after my last post, there is a change. Differences in focus and goals have resulted in me solely focusing on my own practice. I wish the people on the TIRIAS Research team the best of luck.
Since my last post, I’ve had a number of columns on Forbes. In addition, my brother, Paul Teich, and I have released a TIRIAS Research report on NVIDIA’s new framework for deep learning. As usual, you can link to all of them via my publications page.
As I post my latest Tech Target article, on Tableau showing the subscription model is winning, I should point out that I’m no longer linking articles via blog entry but, rather, adding them to my Published Articles page. I never blogged much, so that makes sense.
As I have thoughts that I don’t think are formal enough to publish, I’ll still use the blog page; but refer to the articles one for publishing
The webinar I’m going to review is worth seeing for one reason: The Covenant Transportation Group case study is a fantastic example of what’s going on in BI today. It’s worth the time. IMO, skip the first and third presenters for the reasons described below.
Machine Learning – Yeah, Right…
I usually like Fern Halper’s TDWI presentations. They’re usually cogent and make sense. This, sadly, was why the word “usually” exists. The title of the presentation was “Machine learning – What’s all the hype about?” and Ms. Halper certainly provided the hype.
She started off with a definition of machine learning: “Field of study that gives computers the ability to learn without being explicitly programmed,” by Arthur Samuel (1959). The problem with the rest of the presentation is that’s still true but the TDWI analyst bought into the BI hype that the definition has changed. She presents it now as complex analytics and “Enabling computers to learn patterns.” No, it’s not – except in our sector as people and companies try to jump on the machine learning bandwagon.
Our computers, at all levels of hardware, networking and software are far faster than even a decade ago. That allows for more complex algorithms to be run. That we can now analyze information much faster doesn’t suddenly make it “machine learning.”
There’s also the problem that seems to exist with many, that of conflating artificial intelligence (AI) with expert systems. AI is simply what we don’t know about intelligence and are trying to learn how to program. When I studied AI in the 80s, robotics and vision were just becoming well known enough to be their own disciplines and left the main lump of AI problems. Natural Language Processing (NLP) was starting to do the same.
Yet another main problem I’ll discuss is another conflation of analytics and learning. Fern Harper listed and mentioned, more than once, the “machine learning algorithms.”
Note that all except neural networks are just algorithms that were defined and programmed long before machine learning. Machine learning is the ability of the software to decide which to use, how to change percentages in decision tree branches and other autonomous decisions that directly change the algorithm. It’s not the algorithm running fast and “finding things.”
Neural networks? They’re not even an algorithm, they’re only a way to process information. Any of the algorithms could run in a neural network architecture. The neural network is software that imitates brains and uses multiple simple nodes. Teaching a neural network any of the algorithms is how they work.
Covenant Transportation Group – A Great Analytics Case Study
So forget what they tried to pitch you about machine learning. The heart of the webinar was a presentation by Chris Orban, VP, Advanced Analytics, Covenant Transportation Group (CTG), and that was worth the price of admission. It was a great example of how modern analytics can solve complex problems.
CTG is a holding company for a number of transportation firms. Logistics is a KPI. There were two main issues discussed, but worthy of mention.
The first example was the basic issue of travel routing. It’s one thing to plan routes based on expected weather. It’s quite another to change plans on the fly when weather or other road conditions (accidents, construction and more) crop up. The ability for modern systems to bring in large volumes of data, including weather, traffic and geospatial information, means CTG is able to improve driver safety and optimize travel time through rapid analysis and communications.
The second example was mentioned in the previous sentence: Driver safety. CTG, and the industry as a hole, has enormous driver turnover. That costs money and increases safety risks. They use algorithms to identify problem indicators that help identify potential driver problems before they occur, allowing both the drivers and companies to take corrective actions. A key point Chris mentions is that communications also helps build the relationships that also help lower driver turnover.
Forget that nothing he mentioned was machine learning. CTG is a great case study about the leading edge of predictive analytics and real-time (real world, real-time, that is) BI.
SAP was the webinar sponsor, so David Judge, VP and Leonardo Evangelist, SAP, wrapped up the webinar. I was hoping he’d address SAP’s machine learning, to see if it’s the real definition of the phrase or only more hype. Unfortunately, we didn’t get that.
SAP has rolled out their Leonardo initiative. It’s a pitch for SAP to lump all the buzzwords under a brand, claiming to link machine learning, data intelligence, block chain, big data, Internet of Things and analytics all under one umbrella. Mr. Judge spent his time pitching that concept and not talking about machine learning.
The CTG case study makes is clear that SAP is supporting some great analytics, so they’re definitely worth looking at. Machine learning? Still a big question mark.
I did follow up with an email to him and I’ll let folks know if I hear anything informative.
It wasn’t my chosen title but the author was trying to make a play on words with Ike, so move past it and read my latest article on Tech Target.
Late last year, I read an article in the Harvard Business Review on disruptive innovation. I thought it needed correction and decided to post my response on LinkedIn.
This week a Brighttalk webinar based on a study driven by Holger Schulze occurred. As he is the founder of the B2B Technology Marketing Community on LinkedIn, it should be no surprise that the topic was a discussion of his recently released report on B2B lead generation. The presentation was a great panel discussion, with Mr. Schulze picking out portions of the report and then the panel providing feedback. The panelists and their firms were:
- Dallas Jessup, BrightTALK.
- DeAnn Poe, DiscoverOrg.
- Ben Swinney, Entrust Datacard.
- Dale Underwood, LeadLifter.
- Sue Yanovitch, IDG Enterprise.
The survey was done via the same LinkedIn group, so it’s a bit self-selected, but the results are still interesting. The top five trends are:
- Increasing the quality of leads is the most important issue.
- The same issue of quality of leads is also listed as the major challenge.
- Lack of resources is the main obstacle.
- Lead generation budgets are starting to increase.
- Despite the hype, mobile lead generation still isn’t big in B2B.
I’m a blend of data and intuition driven, so it’s nice to see what I’d expect backed up by numbers. However, in the stretch to get a list of five, the first two seem redundant.
68% of the respondents mention lead as a priority. The first thing pointed out was by Ben Swinney, who was surprised that “Improve the sales/marketing alignment” was down at number four. Ben was the one customer in a group of vendors and that opinion has a lot of weight.
Fortunately for the vendors, he wasn’t in a void. The rest of the panel kept coming back to the importance of both marketing and sales working closely in order to ensure leads were recognized the same way and had consistent treatment. I agree that is necessary for improving lead quality and if there’s one key point to take from the presentation, it is improving that relationship.
Another intriguing piece of information is the return of the prioritization of conferences, had dropped to third last year and are back up to number one. Sue Yanovitch was happy, as I’m sure all IDG folks are, and pointed out their research shows that tech decision makers value their peers and so sharing information in such forums are valuable. Enterprise sales often need to lead with success stories, because most companies don’t wish to be “bleeding edge.” Conferences are always great forum for getting not only improved understanding of technology but also for people so see how others with similar backgrounds are proceeding. The increase in budgets as the markets continue to recover leads to the return of attendance to such forums.
A key point in the proper handling of leads was brought up by Dale Underwood. His company’s research shows that people have completed 60-70% of their research before there’s a formal lead request put in to a prospective vendor. That implies both better tracking and handling of touch points such as web visits, but also means that sales needs to be better informed about those previous touch points. If not, sales can’t properly prepare for that first call.
That leads into another major point. Too many technology marketing people get as enamored of product as do the founders and developers. As Dallas Jessup rightly pointed out, lead generation techniques should focus on the prospect not on the vendor. What pains are the market trying to solve? That leads to the right calls to action.
To wrap back around to the sale and marketing issue, the final point to mention came during Q&A, with a simple question of whether turning leads into customers is marketing’s or sales’ responsibility. That should never have been a question, but rather how the two achieve it would have been better.
Sue began the reply by pointing out the obvious answer of “both,” though she should have said it proudly rather than saying it was a cop-out. All the other panelists chimed in with strong support that it has to be an integrated effort, the full circle lead tracking must happen.