Big Data 2017: A Look Back on the Transformations and Trends of Data Analysis
In 2017, more and more companies around the world started discovering the transformations and new trends related to the growth of Big Data. For example, disappearance of data silos, transformation of processing systems, and creation of micro products/services…
This year, Big Data has evolved a lot. Data storage and analysis is now a common practice in almost every business. The analytical technologies have democratized so throughout the year. As a result, new trends are emerging in businesses that use the opportunities offered by data. These are the new trends that appeared in 2017, and will likely continue to grow in 2018 as Big Data continues to grow.
The disappearance of data silos
The data sets are no longer distributed in silos. Many registration systems directly feed Data Lakes, giving all employees of the company the opportunity to use them on the condition that they have the necessary permissions. Thanks to connectors offered by companies like Talend and Spark, new datasets can be added in a few hours, instead of a few days or weeks as was formerly the case.
The emergence of data processing systems halfway between the past and the future
Rather than rush to replace their transaction systems, most companies are developing new parallel systems that can use data from older systems while looking to the future. These new systems can relieve the old ones by taking care of intensive treatment tasks.
They thus make it possible to gain in efficiency. Thanks to tools like Google TensorFlow, R and Python, the new systems used by companies can facilitate Data Mining, and also allow adding Artificial Intelligence, which in particular make it possible to automate processes.
The democratization of micro-services and micro-products
Thanks to Big Data, and more specifically to software like Kafka and Cassandra, but also to Python and Google TensorFlow , companies can now process data in real time and at scale.
These real-time analyzes make it possible to create and develop micro-product and micro-service offerings for each customer in an automated way . For example, an insurer is able to create effective travel insurance from takeoff of the aircraft, throughout the duration of the flight, and ending the landing.
The rise of self-service customer services thanks to Artificial Intelligence
More and more companies are using Artificial Intelligence to help their customers find answers to their questions about products, invoices or technical questions. Thus, the customer can learn alone and find answers without the need for the intervention of a human employee.
With the emergence of domestic voice virtual wizards like Apple Siri, Amazon Echo or Google Home at home, this trend will accelerate. The consumers will get used to communicate with machines, and it will soon be possible to buy, to sell products or services and solve problems with the sound of the voice.