Adding Variety To Your BI

Over the last year, a couple of our consultants and customers have mentioned using a product called Monarch from a company named Datawatch. They have been using it primarily to extract data from pdf files to include in QlikView applications. After looking into the technology a little more, we learned that Datawatch has a suite of products that can automate accessing information from a vast array of non-traditional (from a BI perspective) data sources.

Datawatch has been around for 30+ years and is publically traded (DWCH). Their stock has risen sharply in the last year.

We wanted to learn more, so we took a trip to Datawatch’s HQ in Boston to hear about Datawatch directly from company execs. We met with their management team, who is heavy with Cognos/IBM alumni, so it makes sense that their focus is turning towards BI to complement their traditional back-end tool/utility focus.

From a technical perspective, we are impressed. Datawatch software can access structured (rows and columns) data, which is nothing that any ETL tool can’t do, but we were very impressed with its ability to access semi-structured data such as PDF, XLS, XML, XBRL, EDI, VSAM, etc. We have not seen a tool yet that can access and manage the array of data formats as well as Datawatch. The automation component works in a similar way to the high-performance file-based ETL tools like Ab Initio.

From a marketing perspective, they are focusing on “Big Data.” Their angle on Big Data is variety (as opposed to the other “V’s”: volume, velocity, or veracity). They are going to market in 2013 with the phrase, “Add some variety to your BI.”

Any long-time BI practitioner or data warehouse traditionalist will tell you that getting access to data in the systems where it originates (or as close to it as possible) is the goal. You then extract the data, clean the data, transform the data, stage the data, etc. so that it can be available for your BI applications. However, those same people will tell you that sometimes that is not possible or feasible, and sometimes the “original data” is not in simple rows and columns easily accessible by traditional ETL tools. This is where Datawatch can help.

Our meeting concluded with an agreement to explore opportunities to work together, especially where we have mutual customers. We will also execute some joint events in the near future. Stay tuned!

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