I see two key themes making an important impact on the growth and transformation of Big Data.
First, data in real-time is transforming everything. As consumers, we already experience it everywhere we go. IoT hitting the mainstream only means this trend towards real-time will continue to explode.
Second, this has implications in how businesses think about and manage data, driving the need for connected data architectures to draw out the value from the raw material. The impact is already profound in every industry. Take retail, for example; the ability to analyze customer behavior down to the level of the hanger on the rack and map this in real-time to things like online visual search on your mobile phone is changing the concept of the loyalty program.
Here are five ways Big Data will impact IT and businesses in 2017.
FIRST is that intelligent networks will lead to data clouds.
The Internet of Things (IoT) and machine-to-machine connectivity are driving intelligent ecosystems where devices understand each other and work together in real-time, in the context of a larger need or purpose. This requires networks to expand and contract on demand, as well as messages to be routed and prioritized in real time. In this world, silos of data will be replaced by clouds of data. Freed from the need to conform to the rules of on-premise batch processing, we will see intelligent self-configuring networks that can enable the meaningful connections between devices. Crucially, these intelligent networks also provide the flexibility to enable new and faster delivery of data to the right place to be analyzed.
SECOND, artificial intelligence, real time machine and deep learning and analytics at the edge will accelerate.
It’s also now not uncommon for global enterprises to say they are going all cloud in the next decade to enable this change. So in 2017, ‘centralized-only’ monolithic software and on premise silos of data will truly start to disappear from the enterprise.
Smart devices will collaborate and analyze what each other are saying. 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. Yet data still has gravity; it’s still more expensive to move than store in relative terms. This will spur machine learning and analytics out at the edge, where the data was born and exists, in real-time, connected via the cloud.
THIRD, businesses will start to attempt pre-emptive analytics.
Most data analytics to date has been post event and reactive. This will move to real-time and pre-event analysis and action. The unique value creation for businesses comes not just from processing and understanding transactions as they happen and then applying models, but by actually doing it before the consumer, or the sensor, logs in to do something. I predict we will quickly move from post-event and even real-time to preemptive analytics that can drive transactions instead of just modifying or optimizing them. 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.
FOURTH, we’ll see the rise of connected modern data applications.
The notion of being able to connect data-at-rest and data-in-motion in different data platforms, across multiple cloud providers and on-premise with true application portability, is the central differentiator between the future of data versus the past.
For enterprises to succeed with data, apps and data need to be connected via a platform or framework. 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.
FIFTH, data will be everyone’s product.
In this new world, your data also becomes a product to be bought and sold. Software can be replaced; your data is irreplaceable and has value. Take Future Farming for example; at least one tractor and combine manufacturers I know is selling data analysis right down to the level of seed planting.
Consumers until now have largely given away the value of their purchasing power on social media and online commerce, but they will become increasingly savvy about how and whether they do this.
So whether you are a manufacturer moving metal or an individual consumer, in every industry I predict your data will become a product with value to buy, sell or lose. There will be new ways, new business models and new companies looking at how to monetize that asset. This means, you’d better start thinking how data is a product for your company in 2017 if you aren’t already.
(About the author: Scott Gnau is chief technology officer at Hortonworks)