Tis the season for 2016 predictions, and Tamr has several to offer thanks to the forward-thinking minds of our co-founders, executives and advisors including:
+ 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.
2) Mike Stonebraker ─ Tamr Co-Founder/CTO and 2014 Turing Award winner
+ 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.
3) Ihab Ilyas ─ Tamr Co-Founder and Professor, University of Waterloo
+ 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.
+ 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.
5) Michael Brodie ─ Research Scientist, MIT Computer Science and Artificial Intelligence Laboratory
+ 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.
