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.
What is Big Data Analytics?
When answering that question, many metaphors apply.
- If big data is a haystack, analytics is how you find the needle.
- If it’s a huge wave, analytics is a surfboard.
- If it’s noise, analytics lets you hear the signal.
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.
Many Forms of Big Data Analytics
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 analytics are best used in different contexts. Like so much else in big data, it comes down to business problems and objectives. Are users seeking:
- Temporal patterns or geographical views of market data?
- Procedural insights from machine logs or sensor data?
- Correlations of behavioral patterns for a single product, multiple products or a yet-to-be-launched product?
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.
Predictive Analytics Produces Big ROI
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.
Big Data Analytics in Action:
- Testing and Failing Faster – R&D leaders can test their hypotheses before making big-bet investments. For instance, pharmaceuticals can use big data analytics to map patient co-morbidities to find potential risk when testing new medications.
- Finding “Win-Win” Alternative Treatments – By mapping broad and multi-sourced patient data sets, providers and healthcare organizations can find more effective (and cost-effective) treatments – e.g., pain management techniques or physical therapy versus surgery. Good for patients. Good for payers.
- Richer Portraits of Customer Profitability – Beyond churn risk metrics, there is competitive advantage when marketing knows which customers are worth keeping with lavish loyalty programs vs. those high-maintenance hagglers that the competition really deserve.
- Modeling for Black Swans - Insurers can apply advanced risk modeling techniques to big data to adjust capital reserves in advance of “black swan” scenarios or to strengthen anti-fraud capabilities by correlating their claims data.
Big Data Analytics Best Practices
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 analytics 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 for big data analytics (see haystack, finding needle in).
Big Data: A Counterintuitive View
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.
Learn how Teradata can help you get more out of your big data analytics.