Teradata announced its intent to acquire Aster Data today. Obviously this is big news in analytics-land and I participated in a call where the two companies gave some quick information.
The driver for the acquisition seems t be an increasing focus on generally unstructured and untapped data and expanding the Teradata portfolio into this adjacent space. Teradata liked the patented Aster Data technology and felt it was unique and well proven. They also see faster growth in “big data” than in structured data. Of course a lot of the data flowing in to companies (sensor data etc) IS actually structured, it just doesn’t match the traditional Data Warehouse model.
Anyway, the opportunity that Teradata sees in this space requires new ways to think, new ways to store data and new ways to do analytics. I am not sure about the need for a new way to think – beginning with the decision in mind seems to work in both old and new world – but certainly new ways to store and access data are critical. Teradata likes how Aster’s nCluster allows them to push analytics to business users, though this says little about the role of data mining and predictive analytics which is a pity.
Aster, as I discussed before, manages relational and non relational data, social network/graph data, clickstream data and more. They aim to allow real-time interactive analytics for a business user but also should allow the kind of macro simulation that can improve micro decisions. Aster offers SQL-MapReduce that allows users to access and analyze both structured data (for which SQL is ideal) and less structured data. Aster also runs on commodity hardware with linear scalability and easy expansion (I recently talked on a Dell/Aster webinar for instance).
Specific examples of analytics that take advantage of Aster’s features include graph/network analysis, time series analysis. Both of these are proven areas for analytics – fraud is often best detected through network analysis and changes in behavior over time are critical to predictions – and Aster Data focused on making these easier and faster. Examples include targeted influential people in networks (as this Teradata customer discussed for instance) and analyzing web log, clickstream and CRM data to identify opportunities for marketing.
A graphic showed the two technologies running side by side with no integration which seems odd. When asked Teradata said that they see hadoop and MapReduce +Teradata as the current state alternative so they are not concerned that adding Aster Data to an existing Teradata customer is an additional purchase – essentially those customers get an easier to use alternative to an investment in hadoop/MapReduce. They also intend to keep the existing Aster Data products and develop “joint roadmaps”.
A couple of things concern me. First I dislike it when companies do acquisitions and then try and run the acquired business somewhat independently. This never seems to work and I much prefer it when companies do the hard work of integration, re-branding and re-organizing immediately.
Secondly the two primary use cases – time series analysis and social network analysis – were both things I felt Teradata had a handle on. Customers certainly use Teradata and partner products like SAS for social and other network analysis (though Teradata says this is not ideal on current hardware). Secondly time series. When I was briefed on Teradata 13.10 and its support for temporal data my take away was that this would help with the kind of time-based analysis so important to predictive analytics. Now it appears I was mistaken and that Teradata really needed help with time series analysis.
Finally there was no discussion of data mining or predictive analytics and no discussion of the SAS partnership. SAS has a long standing in-database partnership with Teradata and the beginnings of one with Aster Data so it seems like there should be a strong partnership post-acquisition but it would have been nice to hear this confirmed.
As always with merger announcements I am still hungry for details when it is all over – I want to know about roadmaps, combined products and integration. Over the coming months this will become clearer. For now, good luck to the teams as they work through the merger. Both companies have some great products so there is certainly great potential.