Five key Trends Shaping the World of Big Data in 2017
“Data really powers everything that we do .” – believes Jeff Weiner, chief executive of LinkedIn.
Big Data is currently the biggest buzzword of the IT industry. But what EXACTLY is big data? It is the large volume of structured and unstructured data generated out of the day-to-day workflow of an organization. This data is both important and useful as it can be analyzed for insights that lead to better decisions and strategic business moves. This immense potential of industrial data to revolutionize business performance is continuously driving the latest industry trends.
According to Gartner, there is “continuing enthusiasm for the big data phenomenon”. 2016 was a big year for Big Data, as many more businesses adopted improved data analytic tools to turn their enterprise data into resourceful information. Let us look into the critical big data trends that would dominate 2017.
1. “Variety” in Big Data:
Gartner defines big data as the three Vs namely:
- high-velocity, and,
- high variety information assets.
While all three Vs are growing exponentially, variety is becoming the single biggest driver of Big-data investments. This trend will continue to grow as firms seek to integrate more sources and focus on the wider platforms of big data. From schema-free JSON to nested types in other databases (relational and NoSQL), to non-flat data (Avro, Parquet, XML), data formats are multiplying and connectors are becoming crucial.
2. IoT Driving Big Data:
IoT and big data are two sides of the same coin. Billions of internet-connected ‘things’ is generating massive volumes of data. Capturing and analyzing this type of data can unlock new possibilities for businesses increasingly looking to derive value from all data. Harnessing this data for real- time analytics can also make a positive impact on our health, transportation, energy conservation, and lifestyle.
3. Data Security Administration:
As we continue to become more reliant on smart devices, inter-connectivity, and machine learning, it will become even more important to protect these assets from cyber security threats. We’ll see more investments in the security and governance components surrounding enterprise systems in 2017.
4. Movement to the cloud:
We will see the convergence of data storage and analytics, resulting in new smarter cloud- based storage systems. This enables immense flexibility and accessibility of data. Moving data analytics to the cloud accelerates adoption of the latest capabilities to turn data into action.
5. Usable Dark data:
The proliferation of big data has made it crucial to analyze data quickly to gain valuable insight. Organizations must turn their analytics towards the ocean of neglected data, classified as dark data, into usable data. This can give organizations a more comprehensive view of historical performance trends and product cycles that can be useful for future business planning.
To add: Emerging alternatives:
Hadoop, the widely popular software ecosystem for data storage and processing, declined rapidly in 2016 from the big-data landscape than expected. MapReduce, HBase, and even HDFS became less relevant than ever. The dominant 2017 trend point towards a shift in the learning of data science. The hottest topics being; streaming media analytics, embedded deep learning, cognitive IoT, cognitive chatbots, embodied robotic cognition, autonomous vehicles, computer vision, and auto-captioning. We can also expect to see mass deployment of a new generation of optimized neural chipsets, GPUs, and other high-performance cognitive computing architectures in 2017.
As big data continues down its path of growth, there is no doubt that these innovative approaches will be central to allowing companies reach full potential with data. According to Gartner, revenue generated from IoT products and services alone will exceed $300 billion in 2020, and that probably is just the tip of the iceberg. Given the massive amount of revenue and data that IoT will generate, its impact will be felt across all industry verticals. What these companies need to do now, is upgrade their data processing strategies, to take full advantage of the insights all this new data undoubtedly will deliver.
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