The Rise of Big Data, Analytics of Things
After the espousal of the internet of things (IoT), businesses are looking forward to ‘analytics of things’—where big data analytics not only tackles the business challenges but also upends the way businesses function. Even before data emerged as an important element, businesses recognized the need for a system that could deal with the explosion of information.
In 2006, Hadoop was introduced as an open source distributed storage and processing system. And, initially, it emerged as a big data platform for cost-effectively storing voluminous data. At that time, many enterprises grappled with the coexistence of big data lakes and data warehouses.
Prior to the advent of big data, organizations were operating their reporting systems using data warehouses with diverse data management tools. Some of the businesses migrated their entire reporting systems to Hadoop, while others viewed Hadoop as secondary storage.
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As companies further tried to extract more insights from their big data investments, it led to the emergence of two important trends in big data analytics as a service (AaaS) and data monetization. In both the aspects, the big data industry witnessed yet another major transformation. Today, data monetization plays an imperative role in transforming big data. Sectors such as healthcare, manufacturing, and governance, for instance, depends significantly on data extracted through several remote IoT devices.
For the successful adoption of big data, a strategic big data strategy is the most important factor. The strategy should define the fundamental principles of analytics, architecture, and transformation roadmap. Rather than starting from scratch, organizations can adopt industry-centric solutions which can be customized and transformed in synchronization with the strategy. That is where businesses can get time to improve their in-house skills, create an analytics center of excellence (CoE) and frameworks of governance to metamorphose all aspects of people, process, and technology.
Organizations need to ensure that the components of the architecture are pluggable and agile. While developing solutions, architectures should be summarized with use cases rather than vice-versa, to keep them more organized and easy to evolve.
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