BIG DATA ANALYTICS‘BIG’ TRENDS FOR 2017

February 10, 2017/0 Comments

With the advent of big data analytics came myriad advancements in technology and data science including cognitive computing, cloud data services, and predictive business models. The applications of big data analytics and data science have been reshaping business processes and companies. In 2016, companies moved projects of data management and analytics into production in order to interrogate both external and internal data to know their customers and improve efficiency.

BIG DATA ANALYTICS‘BIG’ TRENDS FOR 2017

Though the most surprising trend of 2016 that no expert saw coming was a sudden decline in Map Reduce, one of the core components of data management and processing engine Hadoop- which is still a mainstay for data cleaning, acquisition, and cleansing of unstructured data, 2017 will definitely have a lot more surprises in the store. Here are some picks for the ‘hottest’ big data analytics trends in 2017-

  • Tradition programmers dealing with data science and big data analytics will have to enhance their data science skills to stay informed, relevant, effective and employable in their careers.
  • Numerous enterprise application projects on high-priority will focus on developing machine learning, artificial intelligence (AI), cloud data services, predictive business models and cognitive computing.
  • Innovative enterprise projects and strategies will focus on conversational chat bots, auto- captioning, streaming media analytics, embodied robotics cognition, predictive business models cognitive computing and IoT and embedded deep learning.
  • Data scientists will be responsible for additional operational duties including designing, monitoring, deploying and managing real-world projects and experiments, machine learning, A/B testing, predictive business analytics inline to customer touch points and business processes.
  • Data scientists will start working within multidisciplinary, integrated cloud data services development environment which incorporates access to project tracking tools, deep algorithm libraries, compo sable containerized services and robust governance and security controls.
  • Myriad open source tools focusing on cognitive computing and IoT and embedded deep learning will become a part of app developers’ desk, extending and supplementing Spark, Hardtop, and R.
  • ‘Self-taught’ data scientists coming from a non-traditional or different professional background will work with experienced and professional data scientists.
  • Most of curation and training data in the initiatives of advanced analytics will come from the environments of crowd sourcing.
  • Data scientists and analysts will establish their profession in data science by boosting their visibility in numerous competition communities like Top coder and Kaggle.
  • The different stages of development in machine learning will be automated by advancement and up gradation in unsupervised learning.
  • Enterprise applications will advance in order to leverage the value obtained from machine learning, predictive business models, cognitive computing, cloud data services and AI.
  • Enterprise applications developing will be done in such a way that it runs on data management tools in addition to the dynamic and intelligent semi-autonomous drones.
  • Professionals able to produce AI-powered solutions and products that can combine embodied cognition, deep learning, cognitive computing, predictive analytics, data management tools, geospatial contextualization, robotics, IoT fog computing and emotion analytics will be in high demand.

Big data era is here and it has a lot to offer than just analytics and data management for corporate space. The entire planet and even the interstellar space will be monitored, sensed, optimized and managed by cognitive IoT- Big data analytics is definitely going to present even ‘bigger’ opportunities in the coming years! All hail data science!


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