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Big Data Analytics

What is Big Data Analytics?

Big Data Analytics

If big data is a haystack, analytics is how you find the needle.
If it’s a huge wave, analytics is a surfboard.

There is some truth in all of these analogies, but unless your business is a treasure hunt, it’s best to think of big data analytics in terms of value-adding actions that actually move the business forward. Specifically, companies spend too much time, effort and money on big data preparation and loading, and not nearly enough on applying analytics to find difference-making insights. To get there, companies need to find tools to make the process of data preparation more efficient. This will greatly increase the organization’s “analytical agility.”

Is critical to note that big data analytics isn’t one approach or tool. Big data visualizations are needed in some situations, while connected analytics are the right answer in others. In fact, there is risk for organizations that are too application-centric in their thinking. In fact, different types of big data analytics are best used in different contexts. Like so much else in big data analytics, it comes down to business problems and objectives.

Big data analytics is often about predictive capabilities – to find a needle before it gets lost in the haystack, if you will. Yes, big data analytics drives the familiar recommendation engines on popular ecommerce sites. But it’s also about operational actions guided by market sensitivity. Gaining deeper understanding of the structure and nature of relationships between people and processes and defining patterns that lead to user-defined outcomes.

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