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Tamr 2016 Data Management Predictions


2016 predictions from Tamr team, which includes Turing Award winner Mike Stonebraker and some of the most forward-thinking experts from the world of Big Data.



Here are the 2016 predictions from Tamr team Tamr team, which includes Mike Stonebraker and some of the most forward-thinking experts from the world of data.


 

 
Nidhi Aggarwal, Global Lead, Tamr Operations, Strategy and Marketing

In 2016 ...
  • Enterprise Sourcing - - and the massive savings opportunities to be gained by cataloging, unifying and analyzing all that long tail procurement data - will emerge as the proving ground for new data preparation and analytics approaches that move beyond traditional integration platforms.
  • Data preparation platforms will continue to become faster, nimbler and more light-weight than traditional ETL and Master Data Management solutions, allowing enterprises to get more answers faster by spending less time preparing data and more time analyzing it.
  • Advanced cataloging software will be able to identify much more "hidden" or "buried" data for analysis, allowing enterprises to get better answers to questions in their analysis.

 
Andy Palmer, Tamr Co-Founder and CEO

In 2016 ...
  • Big companies will begin to see the democratization of data preparation as a natural consequence of the democratization of analytics that has been driven by new products such as Tableau.
  • We will see the emergence of DataOps as a way for enterprises to manage and embrace the full volume and variety of their data - - helping them rapidly deliver data that enables and accelerates analytics.

 
Mike Stonebraker -- Tamr Co-Founder/CTO and 2014 Turing Award winner

In 2016 ...
  • Data science (and its technology complex analytics) will break out in 2016. How to integrate this technology into DBMSs will emerge as a major issue in this space.
  • The net effect of "one size does not fit all" is that most applications will use multiple DBMSs, each optimized for a portion of their requirements. Work on multi-DBMS "wrappers" (so-called polystores) will intensify.

 
Ihab Ilyas, Tamr Co-Founder and Professor, University of Waterloo

In 2016 ...
  • Large scale data management will see more "data integration" than "systems integration." Connecting data and unifying semantics will be more important than connecting systems and unifying interfaces.
  • Data quality will move from being a one-shot ETL exercise into a continuous process in the data production pipeline -- informed by analytic and reporting tools and enforced in all levels at the business intelligence stack.
  • Data curation activities will present a new type of workload that mixes OLTP, OLAP, streaming, and user interaction activities in a way that warrants awareness of users, samples, provenance and other metadata as first-class citizens.

 
Michael Brodie, Research Scientist, MIT Computer Science and Artificial Intelligence Laboratory

In 2016 ...
  • We will see a turning point for data science. We've seen remarkable successes in specialized domains (such as particle physics, drug discovery, and text/image/speech understanding). Meanwhile, there's been almost-uniform failure in business practice due to the false promises of trivialized point-and-click, self-service tools. The next wave of data science will involve ecosystems of more rigorous tools requiring expertise in domains determined by the problems being addressed.

 
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