What's Possible with Big Data Analytics | Big Data Trends

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Enterprises often spend too much time and effort on collecting and sorting data, but not enough time applying big data analytics to gain valuable business insights. Companies need the right tools to make the process of data preparation more efficient and shift their focus to analytics.

When answering that question, many metaphors apply.

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. And that’s where many of the big data obstacles lie.

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.” Only then can they move past traditional analytics techniques, like statistical and transactional analytics that is commonly used for customer segmentation.

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. Different types of big data analysis are best used in different contexts. Like so much else in big data, it comes down to business problems and objectives. Are users seeking:

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.

Yahoo! Japan applies big data analytics tools for deep insights into customer behaviors and to tailor services and target ads – leading to $100 million ROI.

So what’s the best practice here? How can organizations make such analytical thinking the norm in strategic planning, resource allocation and performance management?

Thus, a broad-based platform for data discovery, rather than a single piece of software, is the way to ensure analytics capabilities are suitable for all types of data, from highly structured transactional and operational data to unstructured, semi-structured and multi-structured data. An “ecosystem” view of analytics environments that integrate open-source components is the right way to conceive of the big picture.

Yes, big data analysis allow companies to extract deeper customer insights than ever before and recognize previously hidden patterns. But, it’s how those insights lead to patterns than actually help the business that is the end game (see haystack, finding needle in).

Big Data? Or All Data? How important is the context of the data? Hear from industry thought leaders Ray Wang of Constellation Research, Martha Bennett of Forrester, Mark Smith of Ventana Research, and Blake Johnson of Stanford on new perspectives on looking at "big" data in your organization, regardless of size or amount of data.

See how Teradata can help you get more out of your big data analytics.