7 Big Data analytics and BI Trends for 2017

Deepaktableau

In 2016, many Organizations began storing, processing, and extracting value from data of all forms and sizes. Going ahead, systems that support large volumes of both structured and unstructured data will continue to rise. Circa 2017 - the market will demand platforms that help data custodians govern and secure big data while empowering end users to analyse that data. These systems will mature to operate well inside of enterprise IT systems and standards. Besides the convergence of IoT, cloud, and big data will create new opportunities for self-service analytics. Here are our predictions for trends that will drive the big data and self-service business intelligence in 2017.

1.Big data becomes fast and approachable.Sure, you can perform machine learning and conduct sentiment analysis on Hadoop, but the first question people often ask is: How fast is the interactive SQL? SQL, after all, is the conduit to business users who want to use Hadoop data for faster, more repeatable KPI dashboards as well as exploratory analysis. In 2017, options will expand to speed up Hadoop. This shift has already started, as evidenced by the adoption of faster databases like Exasol and MemSQL Hadoop-based stores like Kudu, and technologies that enable faster queries.

2. Organizations leverage data lakes from the get-go to drive value. A data lake is like a man-made reservoir. First you dam the end (build a cluster), then you let it fill up with water (data). Once you establish the lake, you start using the water (data) for various purposes like generating electricity, drinking, and recreating (predictive analytics, machine learning (ML), cyber security, etc.). Up until now, hydrating the lake has been an end in itself. In 2017, that will change as the business justification for Hadoop tightens. Organizations will demand repeatable and agile use of the lake for quicker answers. Which means that in 2017, customers will demand analytics on all data. Platforms that are data/ source-agnostic will thrive while those that are purpose-built for Hadoop and fail to deploy across use cases will fall by the wayside.

3.The convergence of IoT, cloud, and big data create new opportunities for self-service analytics. We’ve already made substantial progress on the IoT front with primarily enterprises in the manufacturing, automotive, and logistics sector using it to power their growth engine. It seems that everything in 2017 will have a sensor that sends information back to the mother ship. IoT data is often heterogeneous and lives across multiple relational and non-relational systems, from Hadoop clusters to NoSQL databases. In 2017, demand is growing for analytical tools that seamlessly connect to and combine a wide variety of cloud-hosted data sources.

4. Self-service data prep becomes mainstream as end users begin to shape big data. The rise of self-service analytics platforms has improved Hadoop’s business-user accessibility. But business users want to further reduce the time and complexity of preparing data for analysis. Agile self-service data-prep tools not only allow Hadoop data to be prepped at the source but also make the data available as snapshots for faster and easier exploration. We’ve moved past the tipping point toward modern BI, according to Gartner. And we’ll continue to see Organizations of all sizes democratize analytics, leverage trusted and scalable platforms to encourage people to uncover insights in their data.

5. Self-service analytics extends to data prep. While self-service data discovery has become the standard, data prep has remained in the realm of IT and data experts. This will change in 2017. Common data-prep tasks like data parsing, JSON and HTML imports, and cross-database joins will no longer be delegated to specialists. A report from IDC indicated that spending in APAC on self-service visual discovery and data preparation market will grow 250% faster than traditional IT-controlled tools for similar functionality. In the near future, non-analysts will be able to tackle these tasks as part of their analytics flow.

6. Analytics will be everywhere, thanks to embedded BI. Analytics works best when it’s a natural part of people’s workflow. In 2017, analytics will become pervasive and the market will expect analytics to enrich every business process. This will often put analytics into the hands of people who’ve never consumed data, like store clerks, call-center workers, and truck drivers.

7. IT becomes the data hero. It’s finally IT’s time to break the cycle and evolve from producer to enabler. IT is at the helm of the transformation to self-service analytics at scale. IT is providing the flexibility and agility the business needs to innovate all while balancing governance and data security. And by empowering the organization to make data-driven decisions at the speed of business, IT will emerge as the data hero who helps shape the future of the business. When that happens, data literacy will become the fundamental skill of the future. In 2016,LinkedInlisted business intelligence as one of the hottest skills to get you hired and ranked statistical analysis and data mining among the top five skills in India.In 2017, data analytics will become a mandatory core competency for professionals of all types. And people will expect intuitive BI platforms to drive decision-making at every level.