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2017 BI and Big Data Trends

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 With every New Year come new trends. As we experience changes across the world of Analytics and Big Data, we took a look at sources we follow and trust and put together a Top Five 2017 BI and Big Data Trends to Watch. 

  1.   Big Data, no longer just Hadoop -- Tableau
  2.   Consider the Cloud -- Tech Republic
  3.   Query performance Matters --Information Management
  4.   Business-driven Apps drive Value for Data Lake -- MAPR
  5.   Big Data becoming part of Business Fabric -- Datanami

Read on to hear why, how and where BI and Big Data leaders in the space say enterprises are, or should be, focusing, to stay ahead of the curve and succeed with BI and Big Data.

 1:  Big data no longer just Hadoop, from Tableau 

 In previous years, we saw several technologies rise with the big-data wave to fulfill the need for analytics on Hadoop. But enterprises with complex, heterogeneous environments no longer want to adopt a siloed BI access point just for one data source (Hadoop). Answers to their questions are buried in a host of sources ranging from systems of record to cloud warehouses, to structured and unstructured data from both Hadoop and non-Hadoop sources. (Incidentally, even relational databases are becoming big data-ready. SQL Server 2016, for instance, recently added JSON support).

In 2017, customers will demand analytics on all data. Platforms that are data- and source-agnostic will thrive while those that are purpose-built for Hadoop and fail to deploy across use cases will fall by the wayside. The exit of Platfora serves as an early indicator of this trendRead the full article here.

2:  Movement to the cloud, from Tech Republic

Small and midsize companies and even large enterprises are mapping strategies that take more of their applications to the cloud and out of the data center, and this holds true for big data and analytics as much as it does for traditional transaction processing systems. Companies want to see reduced spend in their data centers and greater flexibility in terms of plugging into and out of solutions. The ability to do this comes with subscriptions to services and not having to lock in for multiple years to on-premises equipment.

An additional factor for big data and analytics is the difficulty that even large organizations have in finding the requisite talent to run in-house Hadoop clusters and processing. This is forcing many organizations to go to the cloud and to cloud services providers that offer the big data processing platform as well as the expertise.  Read the full article here. 

3:  In-Memory Analytics, from Information Management

 Unlike conventional business intelligence(BI) software that runs queries against data stored on server hard drives, in-memory technology queries information loaded into RAM, which can significantly accelerate analytical performance by reducing or even eliminating disk I/O bottlenecks. With big data, it is the availability of terabyte systems and massive parallel processing that makes in-memory more interesting.

At this stage of the game, big data analytics is really about discovery. Running iterations to see correlations between data points doesn't happen without milliseconds of latency, multiplied by millions/billions of iterations. Working in memory is at three orders of magnitude faster than going to disk. Read the full article here.

4:  Companies Focus on Business-driven Applications to avoid Data Lakes becoming Swamps, from MAPR

In 2017 organizations will shift from the “build it and they will come” data lake approach to a business-driven data approach. Today’s world requires analytics and operational capabilities to address customers, process claims and interface to devices in real time at an individual level. For example any ecommerce site must provide individualized recommendations and price checks in real time. Healthcare organizations must process valid claims and block fraudulent claims by combining analytics with operational systems. Media companies are now personalizing content served though set top boxes. Auto manufacturers and ride sharing companies are interoperating at scale with cars and the drivers. Delivering these use cases requires an agile platform that can provide both analytical and operational processing to increase value from additional use cases that span from back office analytics to front office operations. In 2017, organizations will push aggressively beyond an “asking questions” approach and architect to drive initial and long term business value.  Read the full article here. 

5:  Data Fabrics Spreading, from Datanami

The movement of big data is often associated with water. Indeed, the big data ecosystem is replete with watery imagery. Data streams flow into data lakes and reservoirs with the help of products like Flume, Cascading, and MillWheel.

But in the future, we may think of big data in other terms, such as fabric. In fact, that thread is starting to take hold among industry analysts like Forrester, which recently issued a report on the leading data fabric vendors, including Trifacta, Paxata, Informatica, Talend, IBM Syncsort,  SAP, Waterline Data, Oracle, Global IDs, and Denodo TechnologiesRead the full article here.

Now It's Your Turn

What trends do you see for 2017 around BI and Big Data? Are there any trends you think we’ve over looked? Share them with us, we’d love to hear from you in the comments below. 

Happy 2017 everyone!


Topics: 2017 Big Data TRends

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