Big Data and analytics company, Think Big is opening its first office outside the US, a global headquarters in London.
The company which was bought last year by the data analytics giant Teradata, says it is growing quickly in the rapidly expanding area of big data as companies move to re-invent the ways they manage, store and analyse corporate data.
Until now, it has been based in the US with offices in New York, Boston, Chicago, Salt Lake City and San Franscisco but now it is looking to expand further. "Now we have the backing of a $7bn company [Teradata] we can really grow the Think Big business internationally," Rick Farnell, the VP of field operations for the company told ZDNet.
Now, Farnell said, the company is going to use London as its international hub for all its operarions outside of the US and will be opening up office in London, Dublin, Munich and Mumbai. The new focus on analytics is an opportunity "for companies large and small to really change the game on how they use data to dive their business", said Farnell.
Think Big aims to help companies implement and integrate open source technologies like Apache Hadoop and Spark as well as NoSQL databases like Apache HBase and Cassandra and MongoDB.
The approach is simple enough in theory with Think Big helping companies bring different analytics software together. "We are operating in a world where long-gone are the dashboards where you could pull up a dashboard of what happened last month," said Farnell. "If you don't work with models that can interact with your business in-flight and can work on changes that are happening in your business at that exact moment then you are dealing with analytics from a previous generation."
But isn't one of the key issues in this area the people - getting systems analysts and others to think in a completely new way about the way businesses work? "Yes, and the Think Big Academy has really been a secret weapon for us," he said. "We are teaching programmers how to become data engineers using all the new tools technologies and techniques or taking mathematicians and data engineers and turning them into data scientists."
The aim is for them to be able to write better algorithms because they have all of the data and they don't have to make guesses, he said.