Ataccama acquires data visualization startup Tellstory

Data management platform Ataccama today announced it has acquired data visualization provider Tellstory. Terms of the deal were not disclosed.

The data visualization tools will make it simpler for end users to surface insights from data residing in the Ataccama ONE Gen2 platform, Ataccama CEO Michal Klaus said.

Tellstory will be made available as a multi-tenant tool, with a free version provided to Ataccama ONE Gen2 GA customers next month. Tellstory will be more tightly integrated within Ataccama’s data management platform later this year, Klaus added.

The Ataccama ONE Gen2 platform is an expert system that has been optimized to manage large volumes of disparate data, Klaus said. It enables organizations to create data profiles, track data quality monitoring, set up data previews to better understand the relationship between different datasets, and enforce the policies that determine which end users are allowed to access what data. The visualization tools from Tellstory will make it simpler for end users to navigate the Ataccama Data Catalog as they invoke existing analytics tools provided in the platform, Klaus explained.

Despite interest in digital business transformation, one of the biggest issues any organization faces today is they simply don’t know what data they have. Unfortunately, most organizations don’t always appreciate the scope of the data management challenges they face until after an initiative runs into obstacles. “One of the reasons we get called in is because there was a project that failed,” Klaus said.

Longer-term, Klaus said the goal is to enable end users to navigate datasets using natural query languages and speech interfaces. For now, the priority is to simply make it easier for developers to navigate the data they have, he said.

The challenge organizations face, however, is not just the volume of data that needs to be organized and analyzed. The number of types of data that organizations collect keeps expanding to include, for example, unstructured data in the form of video. Organizations need to able to apply metatags to data as it is rapidly created if they expect to employ that data to make better business decisions.

Arguably, the whole point of investing in a digital business transformation initiative is to enable business decisions based on fact rather than intuition. The challenge is that even when data is accessible, it’s often conflicting because different applications are usually not updated at the same time. The expert system from Ataccama identifies what data has been most recently updated, as well as any master version of a dataset that has been designated. That capability makes it simpler for end users to identify discrepancies between different datasets.

There’s a big opportunity to improve data management using machine and deep learning algorithms capable of identifying patterns. But data tends to vary widely across industry segments, which means AI models and expert systems need to be trained to recognize, for example, the nomenclature specific to that industry.

Obviously, it becomes easier to train AI models when data is organized. And the higher the quality of the data employed to train an AI model, the more accurate it becomes. As such, organizations looking to invest in AI would be well-advised to first focus on the fundamentals of data management. Otherwise, they will find themselves hiring very expensive data scientists who will spend most of their time sorting out low-level data plumbing issues that should have already been addressed by internal IT staff.

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