Big Data 2014: Powering Up the Curve
Last year, I predicted that 2013 would be the year big data analytics started to go into mainstream deployment and the research we recently commissioned with Enterprise Management Consultantsindicates that’s happened. What really surprised me though is the extent to which the demand for data blending has powered up the curve and I believe this trend will accelerate big data growth in 2014.
Prediction one: The big data ‘power curve’ in 2014 will be shaped by business users’ demand for data blending
Customers like Andrew Robbins of Paytronix and Andrea Dommers-Nilgen of TravelTainment, who recently spoke about their Pentaho projects at events in NY and London, both come from the business side and are achieving specific goals for their companies by blending big and relational data. Business users like these are getting inspired by the potential to tap into blended data to gain new insights from a 360 degree customer view, including the ability to analyze customer behavior patterns and predict the likelihood that customers will take advantage of targeted offers.
Customers like Andrew Robbins of Paytronix and Andrea Dommers-Nilgen of TravelTainment, who recently spoke about their Pentaho projects at events in NY and London, both come from the business side and are achieving specific goals for their companies by blending big and relational data. Business users like these are getting inspired by the potential to tap into blended data to gain new insights from a 360 degree customer view, including the ability to analyze customer behavior patterns and predict the likelihood that customers will take advantage of targeted offers.
Prediction two: big data needs to play well with others!
Historically, big data projects have largely sat in the IT departments because of the technical skills needed and the growing and bewildering array of technologies that can be combined to build reference architectures. Customers must choose from the various commercial and open source technologies including Hadoop distributions, NoSQL databases, high-speed databases, analytics platforms and many other tools and plug-ins. But they also need to consider existing infrastructure including relational data and data warehouses and how they’ll fit into the picture.
Historically, big data projects have largely sat in the IT departments because of the technical skills needed and the growing and bewildering array of technologies that can be combined to build reference architectures. Customers must choose from the various commercial and open source technologies including Hadoop distributions, NoSQL databases, high-speed databases, analytics platforms and many other tools and plug-ins. But they also need to consider existing infrastructure including relational data and data warehouses and how they’ll fit into the picture.
The plus side of all this choice and diversity is that after decades of tyranny and ‘lock-in’ imposed by enterprise software vendors, in 2014, even greater buying power will shift to customers. But there are also challenges. It can be cumbersome to manage this heterogeneous data environment involved with big data analytics. It also means that IT will be looking for Big Data tools to help deploy and manage these complex emerging reference architectures, and to simplify them. It will be incumbent on the Big Data technology vendors to play well with each other and work towards compatibility. After all, it’s the ability to access and manage information from multiple sources that will add value to big data analytics.
Prediction three: you will see even more rapid innovation from the big data open source community
New open source projects like Hadoop 2.0 and YARN, as the next generation Hadoop resource manager, will make the Hadoop infrastructure more interactive. New open source projects like STORM, a streaming communications protocol, will enable more real-time, on-demand blending of information in the big data ecosystem.
New open source projects like Hadoop 2.0 and YARN, as the next generation Hadoop resource manager, will make the Hadoop infrastructure more interactive. New open source projects like STORM, a streaming communications protocol, will enable more real-time, on-demand blending of information in the big data ecosystem.
Since we announced the industry’s first native Hadoop connectors in 2010, we’ve been on a mission to make the transition to big data architectures easier and less risky in the context of this expanding ecosystem. In 2013 we made some massive breakthroughs towards this, starting with our most fundamental resource, the adaptive big data layer. This enables IT departments to feel smarter, safer and more confident about their reference architectures and open up big data solutions to people in the business, whether they be data scientists, data analysts, marketing operations analysts or line of business managers.
Prediction four: you can’t prepare for tomorrow with yesterday’s tools
We’re continuing to refine our platform to support the future of analytics. In 2014, we’ll release new functionality, upgrades and plug-ins to make it even easier and faster to move, blend and analyze relational and big data sources. We’re planning to improve the capabilities of the adaptive data layer and make it more secure and easy for customers to manage data flow. On the analytics side, we’re working to simplify data discovery on the fly for all business users and make it easier to find patterns and catch anomalies. In Pentaho Labs, we’ll continue to work with early adopters to cook up new technologies to bring things like predictive, machine data and real-time analytics into mainstream production.
We’re continuing to refine our platform to support the future of analytics. In 2014, we’ll release new functionality, upgrades and plug-ins to make it even easier and faster to move, blend and analyze relational and big data sources. We’re planning to improve the capabilities of the adaptive data layer and make it more secure and easy for customers to manage data flow. On the analytics side, we’re working to simplify data discovery on the fly for all business users and make it easier to find patterns and catch anomalies. In Pentaho Labs, we’ll continue to work with early adopters to cook up new technologies to bring things like predictive, machine data and real-time analytics into mainstream production.
As people in the business continue to see what’s possible with blended big data, I believe we’re going to witness some really exciting breakthroughs and results. I hope you’re as excited as I am about 2014!
Quentin Gallivan, CEO, Pentaho
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