IBM Partners with Datawatch to Simplify Data Preparation

Mike Vizard
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7 Characteristics of a Data-Driven Organization

One of the more frustrating aspects of analytics is the amount of time it takes to put data in a format that makes it useful. By some estimates, manually making data accessible to an analytics application can consume as much as 80 percent of an analyst’s time. Given the salary analysts command, the cost of prepping data can be considerable.

IBM today announced a partnership with Datawatch under which it will resell Datawatch Monarch, a self-service tool that enables end users to automate much of the data preparation work associated with running an analytics application. In this instance, IBM intends to provide access to Datawatch Monarch to end users making use of the IBM Cognos and IBM Watson Analytics services delivered via the cloud.

Datawatch Monarch makes it possible for an end user to automatically have all the data in a file turned into rows and columns that can be easily consumed by an analytics application. It also makes it possible to join dissimilar data, all of which can be reused across the organization.

IBMDatawatch

Robin Grosset, chief analytics officer for IBM Watson Anaytics, says data preparation has emerged as a major bottleneck as organizations attempt to derive more value out of the data they collect. Datawatch Monarch is designed to enable end users to easily export data on their own into the IBM Cognos and IBM Watson Analytics services to generate a result without any intervention from the IT department, while at the same time putting that data in a format that can be easily reused by others.

Grosset notes that with the rise of “citizen analysts,” there is now a class of end users that want to take charge of their own analytics applications. In fact, the days when end users relied on IT departments to generate schemas and structure queries are coming to a close. End users want to be able to dynamically interrogate data in ways they see fit without having to wait days or weeks for internal IT organizations to format data and then run SQL queries against it. Naturally, the framework for enabling that interaction still needs to be set up by an IT department. But after that initial effort, the next most important analytics task for the IT department is to simply get out of the way of the end users.




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