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Six Big Data Trends to Watch in 2017 and Beyond

Smart cities, Pokémon Go, Google’s AlphGo algorithm, and much more- 2016 were a happening year from the technology viewpoint. The year has set new milestones for futuristic technologies like Augmented Reality (AR), Virtual Reality (VR), and Big Data. Out of these technologies, Big Data is poised for a big leap in the near future as the corporate systems that support a massive amount of both structured and unstructured data keep on rising. As time advances, the big data becomes more approachable and starts expanding beyond Hadoop.

Let’s have a glimpse of the latest trends in big data that can decide the direction of enterprise IT systems while establishing new standards:

1.       Companies want to get the most from data lakes

Enterprises have started realizing the importance of the data lake. Today, many companies are ready to spend money for having a proper data access together with a plethora of services that can offer significant pools of big data for improved insights. However, the challenge to find skilled data scientists who can make sense of the available information needs to be addressed to enable companies to leverage data lakes. We can see the gap between Master Data Management (MDM), operational apps, data warehouses and data lakes is getting narrowed as the companies actively focus on business-driven applications.

We can certainly assume that the organizations will gradually shift from the “build it and let it come” approach to a business-driven approach. In today’s B2C model, businesspersons want to address customers with personal touch, and it is possible through facilitating real-time process claims in a user-friendly interface. Let’s take an example of an eCommerce site: any eCommerce site must show individual recommendations and feedback with real time price checks to succeed in the intensifying competition. Similarly, healthcare institutes have to come up with a real-time settlement of valid claims while blocking fraudulent claims to remain credible. All such objectives can be achieved by combining data analytics with the company’s operation systems. The combination is capable of driving initial and long-term value for the business and entrepreneurs are eager to get its benefits through enterprise-grade, cloud-based business apps.

We can also expect that in the upcoming days, companies will troll the treasure of information contained in papers, images, videos, and other corporate assets that are lying dormant for a long time. These assets are capable of giving a more detailed view of historical trends and product cycles, which can be used for planning. The data gained from such assets can act as supporting evidence for any trademark infringement or intellectual property violation cases.

2.       Big Data will get a boost from machine learning

Currently, big data investments are driven by three Vs: Volume, velocity, and variety as per Gartner. But looking at a diversified business scenario, it is fairly possible that variety will become the biggest driver for Big Data investment. The biggest indication of this is the rise of machine learning. This year onward, we will witness an increase in the activities related to the integration of machine learning and microservices in the corporate sector, and big data is set to get a massive boost from machine learning.

Previously, microservices deployments were focused and limited to lightweight services while machine learning was restricted to what we know as “fast data” integrations, which were applied to only narrow bands. Now, we can expect to see a paradigm shift from the legacy integrations to robust apps that leverage big data. The machine learning concept will be incorporated into such apps can facilitate usage of a large pile of historical data for better understanding of new streaming data.

3.    Self-service analytics on the rise with the convergence of IoT, Big Data, and Cloud

We can expect that self-service analytics will grow thanks to convergence and combination of IoT, big data and cloud in the future. As Hadoop becomes mainstream and adopts enterprise standards quickly, it is fair to assume that big data will grow up and integrate both IoT and cloud. In addition to this, the rise of metadata catalogs enables people in finding analysis-worthy big data.

Massive volumes of both unstructured and structured data generated by IoE (Internet of Everything) concept are mostly deployed on the cloud platform. Though innovations are going on in storage and data management services, it is interesting to see how data scientists and IT firms will address a last-mile challenge of accessing and understanding zillions of data. We can expect a huge surge in demand for analytical tools that facilitate self-service analytics for the businesses through helping them discover the hidden value in IoT investments.

4.       Moore’s Law will hold true

Experts are of the opinion that Moore’s law, a driving force for technological innovation from the late 20th century to date, will become obsolete in the next ten years. But, when it comes to databases the law will hold true for a long time because of increasing demand. We can certainly foresee that on-demand MPP (Massively Parallel Processing) databases like Google BigQuery and Snowflake will see an upward movement in popularity shortly. Such on-demand databases charge less for storing data and make entrepreneurs free from worries regarding data storage cost. As these databases are fast, scalable, and reliable, many companies will start using them in 2017 and beyond.

Furthermore, for organizations with integrated cloud-based data storage in place, the year 2017 will be a year of optimization and for the organizations with planning for moving data to the cloud, the year 2017 will be a year of transformation and transition. The focus will remain on eliminating unnecessary operational costs while enhancing business performance through data storage and access. Along with this, we cannot deny the dominance of public cloud services as they are more secure for storing the corporate data.

5.       Blockchain 2.0 is here to stay

Financial sector would be a great beneficiary of Blockchain that offers a globally distributed ledger while bringing a radical change in storing of data and processing transactions. Use of Blockchain 2.0 with enhanced features and improved performance can increase the efficiency of transformational use cases in financial services. Hackers find it impossible to hack the blockchain as it has a global presence. All the transactions are stored in blocks, and anyone can view the chain as the blockchain runs on all the computers distributed everywhere. Entrepreneurs consider Blockchain 2.0 as a cost-saving data storage method for having a competitive advantage. With enterprise-friendly features and additional safety, we can assume that Blockchain 2.0 is here to stay.

6.       Data Security Trends

Let’s face it. As the big data is expected to dominate the corporate systems, we cannot deny the incidences of havoc caused by IoT-related data breaches. Therefore, stronger administration is necessary for ensuring data security. Better security measures can help companies to prevent cyber attacks and data leaks, and tightening big data security will certainly remain in the trend for year 2017 and beyond. Data analytics can locate vulnerabilities and can predict cyber attacks with accuracy.

Companies likely to tighten up data access permissions to ensure that each user has access to the most relevant data. We will see creation or revision of data access permission policies done by the companies along with integrating technology to monitor and detect any potential process of copy, transfer, or retrieve data without authorization. It will be exercised by the businesspersons to prevent any unauthorized staff from getting critical data without access. Organizations are expected to take preventive steps against any potential internal misuse of data along with such unauthorized access.

Conclusion

That’s not all. Just like machine learning, deep learning gets starter and we’re inching closer to general AI (Artificial Intelligence) and conversational AI as big data has brought AI in vogue again. How AI interacts with big data will decide the future of enterprise systems and business processes.

This article is contributed by Nitesh, a digital marketer.

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