Although the popularity of Big Data is increasing day-by-day, the concept stems from a very simple theory. Businesses mostly rely on transactional data, stored in relational databases, for decision making. However, there’s also a treasure-trove of unstructured, non-transactional data available to them in the form of emails, blogs, photographs, sensors, etc. that can be harnessed for gaining useful insights. Today, since storing and computing data has become more affordable, organisations can utilize this non-traditional yet potentially valuable information for analysis and business intelligence. And this is precisely what Big Data helps companies do – manage volume, velocity and variety of data.
Since Big Data helps businesses organise and analyse diverse data types and capitalise on hidden relationships, it is imperative for them to manage their enterprise big data properly. But not only is moving and transforming it difficult, integrating it with organisational data flows, while also maintaining quality, is also a daunting task. So organisations need to build an efficient Big Data platform based on their needs, the infrastructural requirements for which span:
Today, the Big Data landscape is dominated by operational and analytical technologies, both of which have evolved differently to address varied demands. While the former focuses on capturing and storing real-time, interactive information, the latter is for complex, retrospective analysis. Being complementary, both of these are often deployed together. Thus, companies can utilize operational workloads to harness cloud computing architectures and scale quickly and inexpensively, and use analytical workloads to tackle sophisticated data sets and scale beyond resources made available by a single server.
Businesses must, therefore, evaluate their Big Data needs before planning the transition to ensure a seamless integration. They should focus on devising a strategy for evolving business architecture to incorporate Big Data with a view to increase business value.
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