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15 data and analytics trends that will dominate 2017

Thor Olavsrud | Feb. 7, 2017
These 15 predicted trends will shape the big data and analytics market in 2017.

Hadoop distribution vendor Hortonworks predicts:

  • Intelligent networks lead to the rise of data clouds. As connections continue to evolve thanks to the Internet of Anything (IoAT) and machine-to-machine connectivity, silos of data will be replaced by clouds of data, Hortonworks says.
  • Real-time machine learning and analytics at the edge. Smart devices will collaborate and analyze what one another is saying, Hortonworks says. Real time machine-learning algorithms within modern distributed data applications will come into play — algorithms that are able to adjudicate 'peer-to-peer' decisions in real time.
  • More  pre-emptive analytics: from post-event to real-time and pre-event analysis and action. We will begin to see a move from post-event and real-time to preemptive analytics that can drive transactions instead of just modifying or optimizing them, Hortonworks says. This will have a transformative impact on the ability of a data-centric business to identify new revenue streams, save costs and improve their customer intimacy.
  • Ubiquity of connected modern data applications. For enterprises to succeed with data, apps and data need to be connected via a platform or framework, Hortonworks says. This is the foundation for the modern data application in 2017. Modern data applications are highly portable, containerized and connected. They will quickly replace vertically integrated monolithic software.
  • Data will be everyone's product. Data will become a product with value to buy, sell or lose, Hortonworks says. There will be new ways, new business models and new companies looking at how to monetize that asset.

DataStax, which develops and supports a commercial version of the open-source, Apache Cassandra NoSQL database, predicts:

  • The emergence of the data engineer. The term, "data scientist," will become less relevant, and will be replaced by "data engineers," DataStax says. Data scientists focus on applying data science and analytic results to critical business issues. Data engineers, on the other hand, design, build and manage big data infrastructure. They focus on the architecture and keeping systems performing.
  • Security: Growth of IoT leads to blurred lines. IoT's growth has largely gone unchecked, DataStax says. With a lack of standards and an explosion of data, it isn't entirely clear who is responsible for securing what. Most at risk are ISPs, which is why we'll see these providers take a leading role in the security conversation in the year ahead, DataStax says.
  • Hybrid wins, thanks to certain enterprise-ready cloud applications. It is becoming clear that many large organizations that have built their databases on legacy platforms would rather pull out their teeth than switch, DataStax says. Hybrid data architectures that encompass legacy databases, yet allow organizations to take advantage of cloud applications, will be a major focus for these organizations.
  • Cutting ties thanks to serverless architectures. DataStax believes the move to serverless architectures — applications that depend on third-party applications or services in the cloud to manage server-side logic and state, or that run in stateless compute containers that are event-triggers — will become more widespread in the coming years. The adoption of serverless architectures will have a widespread impact on how applications are deployed and managed.

 

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