Panel: Enterprise Data Unification in Practice September 26th, 1:45 – 2:45 ET Join Tamr’s panel at the Big Data Innovation Summit on Sept. 26, as Rich Miner (Google Ventures) discusses Enterprise Data Unification with Mona Vernon (Thomson Reuters), Martin Leach…

Strata Conference NY: Thursday, October 16 @ 11:00am EDT   Enterprises and organizations have access to a huge variety of diverse data sources today:  internal data sources, external public data sources, feeds from the Internet of Things and more.  They…

The first in a recurring series of posts featuring Tamr’s people answering thee questions about our aspirations and inspirations. Field Engineering Lead Min Xiao leads off … 1] In your wildest imagination, what problem or kind of customer problem can…

Tuesday, September 16 — 5:00  pm to 6:00 pm at 44 Thomson Place, Boston, MA Please join our friends from Thomson Reuters for the latest in its Knowledge Worker Innovation Series: Showcasing Innovators in a Big Data Ecosystem. We’re thrilled that the event…

Any Big Data effort that brings together data from multiple sources must determine when records in different sources are referring to the same real-world entity. This process is called record matching, and it needs to be accomplished even when the…

In his talk at the MIT CDO IQ Symposium on July 23, Tamr Co-Founder and CEO Andy Palmer shared his vision for “Data Quality Through Curation at Big Data Scale.” Click here to view the slides from this talk Palmer starts…

Tamr is a sponsor of TDWI Boston World Conference 2014: July 22-23 at Hynes Convention Center. At the TDWI Conference, Tamr will be providing live product demonstrations in the exhibit area to show you how to build a unified view of siloed enterprise data.  With…

This is a guest post from Oren Falkowitz, who will be presenting in more detail on this topic on July 15th at a free web seminar. Business leaders are embracing the increasing amounts of data available to them.  Whether to…

Among its well-known challenges, we are getting better and better at handling the volume aspect of Big Data; we buy more machines, we “shard” tables, and we even port solutions to clusters and MapReduce platforms.  But it is the “Variety”…

  Among Big Data challenges, Variety is the hardest to deal with through automation. Variety comes in the form of incompatible data formats, non-aligned data structures, and inconsistent data semantics. These are not merely a consequence of insufficient standards: although…