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Nickels and dimes

MM: That was one of the things that really came through in your talk, Mike. First of all, you were approaching these applications that you call “diagonal applications,” really almost as a value chain optimization suite. So you’re looking not just at one business, but rather at how to optimize an entire value chain—irrespective of your location in that value chain.

MB:  I guess that’s a way to look at it. There is a collection of these Diagonal BI applications. We’ve tried to package a number of them in modules that can be sold to particular industries.

MM: There was another thing that was remarkable in your presentation. That was the ability for using all of these kinds of hidden charges in the trucking area. There were some terms that you used, but they referred to—basically—”hidden” markups.

MB: Yes. That actually brings me full circle to the point I started to make at the beginning of the interview here. That was about “tools versus solutions.”

When I said Oco is a provider of business intelligence solutions, well—every business intelligence provider will tell you they’re providing solutions. The question is “solutions for whom?”

If you’re a data analyst, then a data analyst tool is a solution to your problem. At Oco we’re trying to provide a solution to business users for a transportation cost minimization problem—as our example here. That application goes to the eyes of the business user—not to the eyes of a data analyst. It’s intended for use directly by the people that are in the trenches who need that information. That’s why I stress that it’s a solution.

MM: The nickels and dimes, to use a metaphor. Right?

MB: Yes. Well, because it really adds up. That’s the problem. This is part of the reason why summarized data cubes and so forth have given way to, customers saying, “I need to be able to drill down to the actual data.”

In a summarized data cube, you would just roll up all the accessorial charges noted above. If instead you can actually see what’s happening at the individual bills-of-lading of the trucks, you can spot many of the problems and identify the carriers charging more than others, and so forth—even though the line-haul charge which is the advertised cost of the shipping, is the same.

MM: I refer to these as “carbon monoxide expense items.” Carbon monoxide constitutes an odorless gas that you can’t see, touch or smell. But you know you have it because you have a headache. And if you’re in a cave, you know the canary dies.

MB: Yes. These are, in some sense, ways for people to slide charges in on you.

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Problem of transportation logistics

MM: Not just trucks, but what’s on the pallet and how many pallets get organized by what truck.

MB: That’s right. And how many stops it takes and so forth.

This brings me back to what we mean by a “Diagonal,” BI application.

To build an application that really helps address the problem of transportation logistics, or the truck shipping of goods, you have to embed a lot of industry understanding and knowledge of trucking into the application. So it requires information specific to the business problem of shipping goods by truck, but it’s not specific to any particular industry.

You don’t really care whether you’re shipping machinery or consumer packaged goods or clothing. These applications cut across industries, but not all industries. Obviously, financial services people aren’t shipping goods around by truck, and for the most part, shipping is just not a part of their primary value proposition. Similarly, higher education is not a truck-oriented industry. But any manufacturing company, whether in the food segment, the clothing segment, the toy segment, the industrial products segment, etc., all have a similar trucking problem to solve.

Another example is any company that makes or sells something that typically has sales margin and profitability issues. The companies really want to understand what products are selling at good profit margins. They want to be assured that the inventory they carry, relative to sales rate, is in balance.

Sales margins and profitability issues cut across industries that have goods to buy and sell—but obviously these aren’t applicable to government or higher education. It’s not like a database system because it doesn’t apply across all industries.

These diagonal types of applications are important because they add high value for their customers. They typically save companies thousands and thousands of dollars all the time, or even millions, for large companies. So they are applications that can command high price points, because they really deliver great savings and a very attractive return.

But also, they’re applications that—because they can be sold across many industries—have a pretty large base of prospective customers—larger than vertical-market applications that are targeting a very narrow perspective. They are very attractive from a business standpoint.

Diagonal applications also work very synergistically with SaaS deployments. That was one of the things that I emphasized in the talk I gave at SaaScon. The reason there are companies like Oco and obviously other new market entrants in this space is because of this synergy.

When you build a system for a particular business problem, transportation logistics, let’s say, then the structure of the database of information that’s needed to support it is not specific to that particular customer. It’s a database that’s designed to support transportation logistics.

As a result, you can get great economy of scale in the deployment of that system by creating a SaaS multi-tenant deployment of that database. All the customers sharing that infrastructure are trying to solve the same kind of transportation and logistics problem against a database of similar structure.

This works a lot better than the ASP models of a decade ago. Back then, custom data warehouses would be designed for each business. If you tried to aggregate those together, you’d get a whole bunch of totally different databases. In some sense, they were too customized. You’re not going to get common behavior by putting them together.

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Emergence of diagonal applications

MM: Would it be fair to characterize that these vertical applications tend to be not necessarily transaction systems, but rather analytic systems?

MB:  I do not believe that is exclusively true. But I do think more of them tend to be like that.

MM: Predominantly true?

MB: For example, in industrial manufacturing… Industrial manufacturers sell—in many cases—equipment used by other people. So they have the service and maintenance applications associated with after-market service of the equipment. That’s not analytical.

Yes, they’re very interested in analyzing why these machines are all failing or what the quality issues are, but they are also scheduling the repair cycles.

MM: Yes. The operational systems, then.

MB: Yes. There are some operational ones. But I would tend to agree with you that a lot of them do have the tendency to be analytic.

That brings me to the issue of what I call Diagonal BI. The term was coined by our CEO Bill Copacino. So, diagonal—if it’s not horizontal and it’s not vertical—what is it? Diagonal is the word we chose to define things that live across some industries, but not across all industries.

These are applications or analytical solutions that are definitely not specific to a particular industry, but they represent common functionality that’s needed across many industries.

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