How to build a big data supply chain

To get the most from big data, you must marshal new infrastructure and develop new collaborative processes. John Haddad of Informatica provides salient examples

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360-degree customer analytics
Most companies want to understand their customers better to increase loyalty and retention -- and upsell products or services. To do so, you need to develop a 360-degree view of the customer.

CRM software has long claimed to do this. Today, however, new types of data about individuals abound via social, mobile, and e-commerce channels -- as well as customer service records, telematics, sensor data, and clickstream data based on Web interactions. A true 360-degree view now means you must be able to access new data types along with traditional data ones, combine them, transform them, and analyze everything to discover new insights about customers and prospects.

This greater level of understanding, combined with big data algorithms for predictive analysis, enables organizations to predict customer behavior more accurately and provide meaningful recommendations. Knowing your customers better, including what they are saying and doing, enables you to deliver more value to them.

Real-time operational intelligence
Real-time operational intelligence is the ability to monitor and (optimally) respond to events in real time. An example of this in sales or marketing is known as "marketing to the moment." For example, via mobile device, a sales associate could be provided with information about a customer as soon as he or she walks into the store, including that customer's recent experiences on the store's e-commerce site.

Another example where real-time operational intelligence is especially important is in fraud detection. With more types of data -- whether generated through online behavior, social interactions, or transactions -- you can start to identify patterns that would have remained obscure before. When such data is collected in real time, companies can use predictive analytics to flag fraudulent events with greater certainty and avoid false positives.

Yet another example would be predictive maintenance. Cars now have more software embedded in them than in the past. Through sensor devices, manufacturers can collect information and predict the mean time to failure, as well as more easily inform customers when they should bring a car in for a service visit.

Similarly, for aircrafts, companies typically prefer to perform on-wing repairs, which are less costly than sending the aircrafts to the service facility. By collecting data that better indicates when minor service is needed, companies can preempt major repairs and reduce maintenance costs.

Managed data lake
The more data you have, the better you can develop a 360-degree view and operate in real time. But this can also be a double-edged sword. Data is cumulative and huge volumes are created when new data types are added.

Older companies in particular have large quantities of data on legacy systems, as well as mobile and social data that can potentially be used to extract business value. In many cases, you aren't sure what you want to do with the data just yet, but you know there's potential -- and you don't want to lose that potential by throwing the data away. Instead, you want to store it cost-effectively, so you can access it to discover new insights and trends.

This massive repository is called a data lake -- and must be properly managed or you end up with a swamp. Managed data lakes enable you to store all types of data at scale over the years for processing and analysis at petabyte scale. But even that is not enough. The data must also be be easy to search, cleanse, and govern while observing whatever privacy policies may be in place.

In addition, you must ensure the data is highly reliable and available. You need to make it easy for information consumers to prepare and analyze it and make it useful. The final step is to operationalize the insights that you discover in the data lake to create new products and services, improve customer service, and sharpen decision-making.

The business benefits of well-managed big data go beyond these four use cases, of course, but they provide good illustrations of how you can make big data work for your business. By becoming more data-driven, you can shorten the path to achieving your business goals.

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