How streaming analytics, the rise of data science and the growth of cloud could change the digital transformation path for enterprise in the coming months. This year will be the when real-time big data analytics will come to the forefront of the enterprise. Although it has been said before, this year will see a convergence of several factors that will make this prediction a reality. Companies are increasingly using cloud platforms and advanced data processing solutions to derive essential business insights to enhance operational processes, improve customer service and provide executives with critical data points. This market shift is driving the push to create more value from big data and investments in real-time analytics, and growing the need to use data science and machine learning for greater insight. In the coming months, these five factors will enable enterprises to unlock the advanced power of their data: Enterprises are steadily moving their on-premise IT and data processing to the public cloud. This trend is expected to accelerate through this year, driven by the growing availability of pre-built, reliable, scalable platforms-as-a-service (PaaS) for every possible application development and deployment need across the organization. Developers and everyday business users will use these cloud application platforms to design and operate applications, easier and faster, with minimal coding, while focusing on the core business logic. Additionally, the main concerns related to security are diminishing, as the public cloud becomes more robust and secure. This is validated by the growing use of public and private cloud by traditionally cloud-shy conservative businesses like large financial services companies and banks, even for critical business processes. The total cost, complexity, and burden of trying to manage, scale and run large application devops on private infrastructure will only make the cost of public cloud services more and more attractive to enterprises. Real-time analytics and stream processing will truly arrive in 2018. Owing to a large number of successful early adopters, proof-of-value, and proof-of-concept projects, enterprises will begin large-scale implementation of stream processing and advanced real-time analytics as part of their core data processing infrastructure. It will be driven by the key business objectives including competitive pressure, a growing need for fast data processing, the ability to act on business opportunities in real-time, and the demand for contextual and time-relevant customer experiences. To meet this demand, vendors will start offering vertical end-user applications like pre-built churn analytics, anomaly detection, predictive maintenance, recommendation engines and customer 360 frameworks on big data platforms. There will be a shift to derive higher value from data lake investments. Transactional platforms will connect in real-time to big data lakes to enable faster, intelligent processing of data as it arrives. Direct business intelligence (BI) solutions running on top of the data lake providing scalable, fast interactive response to queries spanning very large data sets will get critical mass adoption.
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Yves Mulkers
Scoop.it!
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