Datawatch at BBBT: Another contender and another question of message

Yesterday’s presentation to the BBBT was by Datawatch personnel Ben Plummer, CMO, and Jon Pilkington, VP Products. As they readily admit, they’re a company with a long history about which most people in the industry have never heard. They were founded in the 1980s and went public in the 1990s. Their focus is data visualization, but much of their business has been reseller and OEM agreements with companies including SAP, IBM and Tibco.

The core of their past success was with basic presentation of flat file information through their Monarch product. It was only with the acquisition of and initial integration with Panopticon in 2013, providing access to far more unstructured data that they rebranded as data visualization and began to push strongly into the BI space.

The demo was very standard. Everyone wants to show their design interface and how easy it is to build dashboards. Their demonstration was in the middle of the pack. The issue I had was the messaging. It’s no surprise that everyone claiming to be a visualization company needs to show visualization, but if you’re not one of the very flashy companies, your message about building your visualization should be different.

Datawatch’s strengths seem to be two-fold:

  • Access a very wide variety of data sources.
  • Access in motion data.
  • Full service from data access to presentation.

While Ben’s presentation talked about the importance of the Internet of Things and that real-time data is transactional, Jon’s presentation didn’t support those points. Datawatch is another company working to integrate structured and non-structured data and they seem to have a good focus on real-time, those need to be messages throughout their marketing, and that means in the demo.

Back from that tangent to the mainline. The third point is a major key. Major ETL and data warehouse vendors aren’t going away, but for basic BI, it adds costs and time to have to look at both and ETL and a data visualization tool which may not work together as the demoware indicates (A surprise, I know…). The companies who can get the full stream data supply chain from source to visualization can much more quickly and affordably add value for the business managers wanted better BI. I know it’s a fine line in messaging that and still working with vendors who overlap somewhat, but that’s why Coopetition was coined.

They seem to have a good vision but they haven’t worked to create a consistent and differentiated message. That could be because of resources and hopefully that will change. In February of this year Datawatch issued a common stock offering that netted them more cash. Hopefully some of that will be spent to focus on created strong and consistent marketing. That also includes such simple things as changing press releases to be visible from the PR link as html, not just pdfs.


I know you’re getting tired of hearing the following refrain, but here it is again. The issue is that I’ve heard this message before. The market is getting crowded with companies trying to support modern BI that’s a blend of structured and unstructured data. Technologists love to tweak products and think that minor, or even major technical issues that aren’t visibly relevant to the market should sell the product all by themselves. Just throw some key market points on top of them and claim you have no competitors because your technology is so cool.

BI and big data are cool right now and there are a large number of firms attempting to fill a need. Datawatch seems to have the foundations for a good, integrated platform from heterogeneous data access to visual presentation of actionable information. That message needs to quickly become stronger and clearer. This is a race. Being in shape isn’t enough, you have to have the right strategy and tactics to win the race. Datawatch has a chance, will they stumble or end up on the podium?

1 thought on “Datawatch at BBBT: Another contender and another question of message

  1. John

    Thanks for the interesting and thoughtful commentary.

    I agree with several comments. But for me it took time to discover the advantages of Datawatch over other visual data discovery products (Tableau, Qlikview, and Spotfire). My conclusions were realized only after spending time working to solve various large data discovery projects from beginning to end. I can say now that a demo can’t possibly demonstrate the advantages. That’s how I found out. I gave up every time with other solutions only to use Datawatch Desktop to realize results.

    Don’t under estimate the power of data variety in the hands of the functional expert. They can extract meaning from unstructured data from multiple sources and in the process create a self documenting (“portal” “project”) file. This saved project file contains links to the original data sources including the excel like formulas to extract key source data. Keeping the ETL process separate from the final export to a visualization file is an advantage that won’t be fully appreciated at first. Stopping to wait for a separate ETL guy (Informatica, etc) defeats the purpose and creative advantages from self service data discovery.

    Lastly, assuming you have access to the required data the other vendors fall short in large data visualization discovery. In memory or speed of thought is critical. This performance is absent in Tableau and Spotfire. Furthermore, when I’m dealing with millions of records, multiple changing file sources, + 50 fields the layout of filtering functionality becomes crowded, confusing and impossible to quickly understand and discover. The ability to add or remove multiple fields or tables for effective data filtering is critical. Qlikview is fast like Datawatch but the layout for discovery can be challenging and diminishes innovation. I wanted and have used the other products. There were several reasons including their visual appeal but was unable to complete projects that required large complex data sets with uncertain quick discovery analysis. My opinion is based on 20 years of experience with data analysis software for Fortune 300 companies.



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