Stories about Big Data Analytics often include platitudes from customers saying they now can do things they "never dreamed of." The problem with those statements is that most companies want to buy technology to fix an existing problem -- not to take care of things they never "dreamed of." If they happen to solve a problem they didn't know they had, well, that's the cherry on the sundae.

Technologists eager to share the transformative possibilities of new data management technologies have a messaging problem. There is no question that working in-database or in-memory shaves hours, if not days, off analysis. But, so what? For manufacturers the "so what" is perhaps a little easier to understand because this is one industry that is wasting data for lack of the means to quickly and effectively analyze it.

Modern manufacturing equipment is studded with sensors. Every part of the supply chain coughs out data, every step in production has data input (much of it automatically gathered), every repair or return of a defective item comes with a gold mine of information. Manufacturers certainly don’t need to go looking for data. In its 2011 Big Data report, the McKinsey Global Institute noted that manufacturing "stores more data than any other sector -- close to 2 exabytes of new data stored in 2010." Importantly, they noted that manufacturers need productivity growth and "will need to leverage large datasets to drive efficiency across the extended enterprise and to design and market higher-quality products."

So the data is there, but it is rarely absorbed, collected or properly analyzed. Let's look at a couple of examples of high performance analytics solving real world problems -- not those in someones dream.

Before we go further, though, let's talk about the difference between big data" and "high performance analytics" (HPA). For many, big data analytics (BDA) is simply a relational data base (RDB) or an online analytical processing (OLAP) cube in memory that does simple descriptive statistics. Yes, it is faster, but without advanced analytics like data mining, forecasting and optimization, organizations just get a poor answer faster. HPA means imbedding robust analytics into the database or into memory where the data is brought in or spreading the analytics task over multiple processors in a grid environment. This means the best answers arrive faster, and this is what drives real value. The examples below illustrate best practices in HPA.