I believe that 2016 will be the year of the Data and Analytics. Here the top trends that we in Bigdatapump have noticed rising during 2015 and believe that will be the big rocks at 2016. We will make some deep dives (blog texts) into these items during this year.
Top hot topics 2016 (based on field research) on big data and analytics:
New EU legislation will require companies for new capabilities in customer data management processes. Companies will have 24 months transition time. This will be as an activity comparable to Y2K efforts.
Advertising network, external data and data sharing integrations with own customer data
Going beyond your own data and integrating to DMPs is a rising trend.
Everyting moves from desktop to mobile primarily
That’s how it just goes.
Mobility drives the ability to use location data and build services around that. Customers start to expect good location based services.
Real time analytics
New technologies enable now companies to move from batch processes to real time processing. Even fully.
Move from reporting to predictive and prescriptive analytics
Data science, analytics, simulation become as a default for the businesses.
Industrial Internet implementations - more Proof of Concepting and real investments start to take place
Big data moving to manufacturing business. Finally seriosly.
Usage of cloud PaaS services will explode.
Both Public Cloud and Hybridapproaches will become as a standard and lots of “fork lifting” from traditional DWHs to Paas DWHs will take place.
Data&analytics self service becomes a standard
BI and analytics self service will happen in real. Companies drive for breaking the data silos. New tools are taken into use.
Lack of competencies will drive to innovative and global sourcing models (don’t mean India-only with this one)
Need for experienced resources. Only very limited amount of those available globally. Companies will have challenges to recruit and keep the most talented data scientists.
New technologies shaping the direction
Constantly coming new technologies that will shape the use of data. More maturity on data streaming, Spark, Spark R etc are changing the approach on distributed data processing (options for Map Reduce).