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

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.

My recent IoT article has been bundled with another by David Loshin, another analyst, by Tech Target. See it here.

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.

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.

A recent Dataversity webinar was titled “Data Model is Data Governance.” All the right words were there but they were in the wrong order. The presenters were Robert S. Seiner, KIK Consulting, and David Hay, Essential Strategies. While Robert tried to push the title, David more accurately addressed the question “Is data modeling data governance?” Both he and I agree that the simple answer is no yet they overlap in important ways.

Data modeling is understand the data, and Mr. Hay described it in the context of business by referring to the Zachman Framwork, with the management’s overview of corporate information fitting Zachman’s first row then things diving deeper into technology as you move down the rows.

What I found interesting in a webinar with that title is the following definition slide:

When those are your definitions, it doesn’t make sense to talk about them as being the same. David Hay regularly pointed out the same thing during the presentation and I thought his points were very useful to people considering the issue. The slide very clearly and correctly points out the different but overlapping nature of the relationship between modeling, stewardship and governance.

Towards the end of the presentation, another comparison came up that I’ve previously discussed. The topic was whether or not data modeling is an art or a science. One flaw was that David Hay seemed to be implying that the only art was in the presentation of the models to management – physical art. His belief seemed to be that the modeling was pure science. I disagree as understanding data means understanding meaning, different people often mean different things when using terms and see different things from the same information, so art is needed to mediate solutions.

My biggest annoyance with the discussion was the word that should have been discussed never made an appearance. For the same reasons I’ve argued that programmers aren’t software engineers, modeling is neither one nor the other: It is a craft. It is a blend of the two worlds of art and science. I don’t know why people today seem upset to admit to modeling and most software work as a craft, the word doesn’t denigrate the work but describes it very well.

The final point made that I thought was great was in response to a question about when to start on logical and physical models, whether that should happen before you’ve defined your business models or they need to wait. Robert Seiner accurately used the old cliché, starting the other models before you understand your business model, the thing that drives business, is to follow the failed logic of “Ready! Fire! Aim!”

Given the title, I must also say that governance was given short shrift, basically mentioned only in definition, but I didn’t really mind. While the webinar was misnamed, it was a great conversation about the relationship between data modeling and business. The conversation between the two was worth the listen.

My latest article published in Search Data Management.

http://searchdatamanagement.techtarget.com/feature/Database-schemas-still-needed-despite-Hadoop-and-NoSQL-pretensions

Trifacta is another business intelligence company to enter the horse race (yes, I know that reference is spelled differently…). They are focused on providing an early look at data coming out of Hadoop, to create some initial form and and intelligences for business use.

Last Friday, Trifacta was the presenting company at the BBBT. Their representatives were Adam Wilson, CEO, Michael Hiskey, Interim Marketing Lead, and Wei Zheng, VP Products. The presenters were there to discuss the company’s position in data wrangling. While some folks had problems with the term, as Michael Hiskey pointed out, it as term that they didn’t invent. Me? I think it makes more sense than another phrase our industry uses, data lake; but that’s another topic.

Simply put, Trifacta is working to more easily provide a view into Hadoop data by using intelligence to better understand and suggest field breaks, layouts and formats, to help users clean and refine the data in order for it to become useable information for analysis.

Michael and the others talk about self-service data preparation, implying end, business user involvement. The problem is that they’re messaging far ahead of the product. They, as lots of other companies are also doing, try too hard to imply an ease of use that isn’t there. Their users are analysts, IT or business. The product is important and useful, but it’s important to be clear about to whom it is useful. (Read more about self-service issues).

The Demo

While Michael Mr. Hiskey and Mr. Wilson gave the introduction, the meat was in Ms. Zheng’s presentation. As a guy who has spent years in product marketing, I have a bit of a love hate relationship with product management. Have had some great ones and very poor ones – and I’m sure the views of me also spread that spectrum. I’ll openly say that Wei Zheng is the most impressive example of a VP of Products I’ve heard in a long time. She not only knows the products, she was very clear about understanding the market and working to bridge that to development. How could any product marketer not be impressed? Her demo was a great mix between product and discussions about both current usage and future strategy.

One of the keys to the product Wei Zheng pointed out is that the work Trifata is doing does not include moving the data. It doesn’t update the data, it works by managing metadata that describes both data and transformations. Yes, I said the word. Transformations. Think of Trifacta as simplified ETL for Hadoop, but with a focus on the E & T.

The Trifacta platform reads the Hadoop data, sampling from the full source, and uses analysis to suggest field breaks. Wei used a csv file for her demo, so I can’t speak to what mileage you’ll experience with Hadoop data, but the logic seems clear. As someone who fifteen years ago worked for a company that was analyzing row data without delimiters to find fields, I know it’s possible to get close through automation. If you’re interesting, you should definitely talk with them and have them show you their platform working with your data.

The product then displays a lot of detail about the overall data and the fields. It’s very useful information but, again, it’s going to be far more useful to a data analyst than to a business user.

Trifacta also has some basic data cleansing functions, such as setting groups for slightly different variations of the same customer company name and then changing them to something consistent. Remember, this is done in the metadata; the original data remains the same. You can review the data and the cleaned data will show, but the original remains until you formally export to a clean data file.

Finally, as the demonstration clearly shows, they aren’t trying to become a BI visualization firm. They are focused on understanding, organizing and cleaning the data before analysis can be done. They partner with visualization vendors for the end-user analytics.

Summary

Trifacta has a nifty little product for better understanding, cleaning and providing Hadoop data. Analysts should love it. The problem is that, unlike what their presentation implies, Hadoop does not equal big data. They have nothing that helps link Hadoop into the wider enterprise data market. They are a very useful tool for Hadoop, but unless they quickly move past that, other vendors are already looking at how to make sense of the full enterprise data world. They seem to have a great start in a product and, from my limited exposure to three people, a very good team. If you need help leveraging your Hadoop data, talk with them.