Altair + Datawatch = interesting opportunities

Nov 6, 2018 | Hot Topics

Yesterday, Altair announced that it wants to acquire/merge with Datawatch, prompting a lot of “who?” and “why?”. Since I hadn’t heard of Datawatch, I did a bit of research — here’s what I learned and why I think this combination is an interesting idea, with lots of potential upside.

First, who’s Datawatch? Datawatch solutions help clients gather, sanitize, process, analyze and visualize data. Datawatch’s 14,000 clients come from financial services, healthcare, retail and other industries — but not, typically, manufacturing. Its main products are Monarch and Swarm, for data prep; Angoss for predictive analytics, and Panopticon for real-time visualization and analysis. Some products are cloud, others on-prem, but the main business model seems to be perpetual sales.

During a conference call with investors, Altair and Datawatch said that Datawatch had revenue of $36 million in 2017, and that sales for the 12 months through June 2018 were around $40 million — in other words, it’s growing right now. But it’s been a lumpy path, with revenue growth over the last five years ranging from -14% to +19%, for a five-year compound annual growth rate (CAGR) of 9%.

Altair CEO Jim Scapa characterized Datawatch as a “data preparation, data science and real-time digital analytics company with a long and strong market presence, a well-established best-in-class products used by customers, including 93 of the Fortune 100.” He went on to say that Datawatch’s “technology is highly relevant and applicable to almost any company in vertical market today. Bringing Datawatch into Altair should result in a powerful offering consistent with our vision to transform product design and decision-making by applying simulation, data science and optimization throughout product life cycles.”

And here’s the vision bit, from Mr. Scapa: “We see a convergence of simulation and machine learning technology to live and historical sensor data as essential to creating better products, marketing them efficiently and optimizing their in-service performance. [With Datawatch,] Altair will be able to provide a broad-solution offering, under a compelling licensing model, to meet all of their digitalization needs.” I’ve bolded a couple of key bits for further examination.

First, the convergence of machine learning and simulation. In a product context, machine learning is about looking for patterns: Knowing what happened in the last 1,000 duty cycles, when will that rotor fail? Reconstructing the last n accidents, why did that frame component buckle? It’s backwards-looking, crunching through mountains of data to figure out what’s relevant and find the lessons buried deep. Simulation takes that baseline and asks, “what if”: what if we changed the design of the rotor? and applies physics to try to arrive at an answer. They’re completely different approaches to the same end-result: better performance, safer cars and so on.

And that’s why the second element is so important: the commercial model. Just as it took decades for manufacturers to build simulation programs alongside design and engineering, it will take time for them to figure out what data they have, what they need and how to use it to meet their business goals. Adding a machine learning capability to Altair’s HyperWorks scheme will make it accessible, low=risk. and sandbox-y so that people can experiment at will.

Mr. Scapa laid out three potential use cases in describing Datawatch. The first is data preparation: “Every company has data all over the place –business, marketing, engine– and often in different places and in many different forms. Datawatch’s foundational technology lets you bring that data in very, very easily. It has a huge number of tools – it is arguably the best data prep technology on the market.” So there is applicability in our world for customer requirements creation, as-used data to inform simulations and similar uses.

Machine learning was Mr. Scapa’s second case. “Angoss is an environment to set-up doing machine learning and we think that’s going to be relevant for anybody who is trying to apply machine learning algorithms. Whether you’re trying to do predictive analytics for predicting failure or we see actually even applying it to some of the core things that
we do, some of the simulation things that we’re doing for crash or optimizing for crash and those sorts of things.”

Finally, he cited “real time data streaming and visualization, which relevant to the IoT data that you’re bringing in. [Some Datawatch] customers have streaming data from marketing and other sources as well. Financial is where they’ve played a lot, but there’s really not much like it in the market and we think it’s just going to be huge with all the data coming from IoT.”

Datawatch CEO Michael Morrison gave a little insight into the competitive environment, telling investors that Alteryx is the biggest competitor in data preparation, that he believes Panopticon has no real competitors. His most important point on the competition, however, was this: “We typically compete against building yourself. And so, when you get into the IoT space and real-time streaming data, and time series data, we’re very confident about how we stand.”

The companies say that there is very little overlap in their customer bases. That gives Altair the opportunity to perhaps package Datawatch’s products with its own HPC and other technologies — and, of course, where relevant, to move what appears to be a predominantly perpetual model to a more repeatable subs revenue stream. That’s going to be a challenge, for sure, since the people Altair sells HyperWorks to are likely not the current, typical Datawatch customer. How that will happen is, for now, TBD.

Here’s the thing: investors don’t like the deal, sending the share price down 20% yesterday (when the markets were flat). And I get that: it’s big in dollar terms, brings unpredictable revenue and profitability, and will likely be a distraction to management. And it’s not SIMSOLID, another physics solver or something else that’s more typically Altair. But here’s the thing: Altair knows how to integrate acquisitions, has done dozens over the years with mostly the same management — I’m not worried about that. The concerns around revenue and profitability are real and valid — but this isn’t so big an add that Altair is betting the farm. Altair generates plenty of cash to cover its debt service and is conservative in how spends money; if anything, I’d worry about under investing in the sales resources to take Datawatch products into long-time HyperWorks CAE customers.

The main concern, then, boils down to “it’s not CAE”. But is that so bad? Altair isn’t all CAE, even today.

Altair has always been in it for the long-term, and that’s what this acquisition is all about. Manufacturers, Altair’s traditional base, hear all the buzz about IoT and data and analytics, and they’re starting to wonder what this means for them. They want a low-risk way to try to use the data in historians and from sensors. Those projects, in sandboxes, may not all succeed but users will eventually hit upon the magic combination of data in and analyses out that yield a business benefit — and then Altair starts making real money from simulation customers applying Datawatch.

But Altair is NOT abandoning traditional CAE. At all. In fact, Mr. Scapa told investors, “I know, from the outside this might look like it’s far afield but it’s really becoming more and more relevant because of this convergence between high performance computing, simulation and data science –ultimately, they will be one. We’re just anticipating that and making decisions in that context. We’re continuing to look at lots of more traditional CAE and simulation. We just acquired SIMSOLID and we’re very active in this space, too.”

Still active in CAE. Check. Adding something new and interesting. Check. Is this a slam-dunk with no business challenges? No. But, overall, I like it.