Defining Big Data Visualization and Analysis Use Cases

Use these five use cases to spark your thinking about how to combine big data and visualization tools in your enterprise.

By David S. Linthicum, SVP, Cloud Technology Partners

The value of big data, and business intelligence (BI) that leverages it, differs from vertical to vertical, enterprise to enterprise. In many instances, enterprise IT shops that support BI don’t understand how to apply this technology to provide the best bang for their buck.

By defining and focusing on a few specific use cases for this technology, we can better define the value in our own applications and possibly uncover new uses we hadn’t considered. Some of these uses involve specific verticals, while others are more general in nature.

I see five core use cases.

Use Case #1: Business process improvement by coupling data analytics and visualization

By using big data analytics, businesses are better able to determine the state of their business as well as how productive their business processes are. Analytics can give us the raw facts, but when coupled with data visualization, we have the added benefit of shining a bright light on areas where the business processes fall short.

For example, using data visualization, a business can better understand the process of recording a sale and processing it for shipping. The enterprise can quickly grasp how that business process helps the business fulfill orders accurately and ship them promptly and thus satisfy customers. The technology helps an organization understand what part of the process needs corrective action as well as see the effect of this process on its bottom line.

Use Case #2: Business-critical applications enhanced with data analytics

Embedded big data analytics integrated into operational enterprise applications can provide significant business value. Such enhanced applications provide access to information that allows users to take corrective action automatically.

For example, enterprises can augment a shipping application with analytical information from shipping companies’ delivery records culled from (perhaps terabytes of) PDF files in the file system cluster gathered over the years. The organization might also combine this data with information from outside sources, such as complaints recorded in social media or blogs.

Use Case #3: Improving healthcare with data analytics and visualization

There is much low hanging fruit when it comes to the business value of this technology in healthcare. The best use case is the application of this technology to patient diagnoses and treatment. The healthcare system stores our information in different places in different formats, which results in difficult or impossible analysis of this data as a single cluster of information.

By using big data analytics, we can now gather all structured and unstructured healthcare data and place it on a single cluster for analysis by a BI tool. This improves healthcare professionals’ ability to determine patterns of treatment success by analyzing the holistic patient data and determining which treatments lead to the desired outcome.

Use Case #4: Improving retail with data analytics and visualization

The retail industry depends upon an in-depth understanding of their markets and customers to gain an advantage over competitors. The use of big data analytics has a huge strategic business value to retailers, allowing those who drive the BI tools to create models that determine the success of a product based on predictive data points that may be gathered from huge amounts of unstructured data.

This data may include demographics about the existing customer base compared to the number of times the product is mentioned within social media systems. Users may compare sales trends with weather patterns that may affect the use of the product (say, a down coat in a very cold winter). The idea is to provide retailers who make critical decisions with the means to slice and dice all the relevant data to determine where to place the product bets.

Use Case #5: Improving transportation with data analytics and visualization

Transportation systems depend upon efficiencies in moving people from one place to another. For example, airlines need to select the best and most profitable routes. Using Big Data analytics, they can predict the profitability of those routes by leveraging historical data visualized with key predictive metrics applied to data gathered from external sources.

Big data analytics allows the airline to gather many years of flight data from the government, including point of origin, number of passengers, on-time records, etc. They can then look at this data with known pricing information from other carriers. Add in predictive data, the number of times the destination of that route appeared in Web searches over the last several years, and the number of times it’s been mentioned on social networking sites as well. By modeling this data within a BI tool, the airline would have a good idea as to the past use and profitability of the route and predict ticket sales and ticket prices.

Finding More Use Cases

Of course, these are not the only set of use cases for big data and business intelligence, but the ones I view as the primary use cases as this technology moves forward. At this point, I urge you to define the use cases that are right for your enterprise. What has the potential to provide the most value?

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