3 Data Acquistion Trends To Watch Out For in 2017

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The DAQ (Data Acquisition) market, growing at a cumulative average growth rate of 6% and currently valued at over $2.26 billion as at 2015, is expected to cross the $3 billion threshold by 2020. The market is primarily driven by the proliferation of open source software standards, interoperable abilities, the adoption of Ethernet, and the need to cut on distribution losses that continue to be incurred. However, the market still faces significant skilled labor constraints as an advanced technology such as this requires highly specialized skills in computer languages such as FORTRAN, PASCAL etc. Also, cost considerations are a major bone of contention as are security issues owing to the widespread popularity of wireless technology. Below, we take a look at 3 data acquisition trends to watch out for in 2017 as follows:


  • Open Machine Learning and Deep Learning

Late last year, Google open-sourced its machine learning platform- TensorFlow. A few weeks later, IBM followed suit by releasing SystemML-its machine learning technology- into the open source community. These initiatives join a growing plethora of already existing open source machine learning platforms e.g. DL4J that is used to implement deep learning in Java. Data technologists and scientists now have the world’s leading algorithms at their fingertips if they wish to carry out advanced predictive analytics. This is expected to propel the innovative ways by which we create value from data to levels previously unimagined.


  • Big Data Strategies Beyond Hadoop

After several years of technology-focused on the adoption of Hadoop and other related alternatives to traditional databases, expect a shift toward more business-oriented data strategies in 2017. Such carefully crafted strategies are likely to involve Chief Data Officers (CDOs) as well as other business leaders, and should be guided by the creation of business value from data and innovation opportunities. The latest trend of exciting advances in data engineering and data science techniques should spark a myriad of creative business opportunities, with the data infrastructure playing a supporting role. Real benefits to companies such as DAQifi will be best achieved through strategic alignment of the right technologies with high value opportunities in order to support innovative solutions.


  • An AI-Enabled World

Having gone out of favor in the 1970s, AI (Artificial Intelligence) is proving hot once again. Examples such as medical diagnosis, stock trading, facial recognition, and autonomous vehicles are exciting the imaginations of present-day technologists. In addition, the power of parallel, distributed computing is now more accessible than ever before, making it much easier to experiment with numerous novel ideas. Also, the rich data that is needed to feed machine learning algorithms continues to be more diverse, prolific, and readily available than ever before. While it may take a little bit longer to perfect your self-driving car, you can definitely expect your life to get better in 2017 as a result of the innovative uses of AI.

As with every passing year, 2016 has proved that with innovation comes acquisition. While it may be difficult to predict where the next great IT revolution will emerge from, the future looks great for data loggers in these three spheres heading into 2017.