Lessons from TDWI: Want Analytics with Your Fries?

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At the risk of aging myself, I’ve probably attended 70 or so TDWI conferences and executive summits, but the recent TDWI Las Vegas was different. It marked the organization’s 20th anniversary and reinforced the importance of analytics, which was a big topic among attendees, speakers and vendors.

As a TDWI faculty member for the fourth consecutive year, I taught a whole-day class on social analytics, which focused on driving business values with big data. But more on that subject in my next post. In this blog, I’d like to step back and address changing analytics dynamics.

This vital area has come a long way from its roots in data management, reporting and BI. I reminisced with TDWI president Steven Crofts about all the changes that have taken place over the years. It was fun to remember when TDWI, aka The Data Warehouse Institute, was about innovations in data processing and warehousing. Fast-forward two decades: We’re talking about big data, social analytics, machine learning and cognitive computing.

The same quantum leap holds true when talking about analytics, which has evolved into highly automated, somewhat transparent solutions for ingesting, integrating and leveraging vast amounts of information. As analytics mature, organizations of all types are looking at how they glean greater business value. Some industry segments, like pharmaceutical, manufacturing and retail, are ahead of the curve because market disruption has led to adopting new analytical capabilities and advanced workloads to produce real-time fraud and risk analyses as well as quality control and other complex insights.

Many companies I spoke with are in the midst of retooling their analytics foundations to be more successful. Others are making bold moves. The manufacturer of an industrial French fry maker is using sophisticated analytics and sensors to better monitor equipment vitals (e.g., the filter is dirty, heating element isn’t hot enough, etc.). In doing so, they can assure their restaurant customers that their equipment is performing optimally.

It doesn’t stop there. They also want to listen to the social signals of their customers’ customers—the folks eating fries—to better understand satisfaction levels through trend analyses. If they learn through social analytics that customers complained about substandard fries at their customers’ establishments, they could proactively help the restaurant take action. As a result, this company will be able to differentiate themselves by helping their customers before something hurts their brand. How cool is that?

Regardless of where companies are in the analytics adoption curve, there are major drivers accelerating change across the entire data landscape. As business analytics mature, there will be continued movement along these four pressure points:

  1. The user community is changing. Companies must deal with a new and growing set of users. You no longer need to be a major nerd or a Ph.D. to drive business analytics. I call this “the convergence of the suits and hoodies.” Thanks to the Google generation, we now have users with a very different view of how to gain access to information and why. The “hoodies” are all about collaboration; the “suits” have a more traditional data management view. Together, they are driving significant changes in how companies maximize data value. Both perspectives need to be taken into consideration.
  2. The business side now runs the show. IT used to be in charge of most analytics projects because the business side had to rely on IT to supply whatever was needed. While IT still plays a vital role in enabling technology, the shoe is on the other foot. Business has a louder voice in expressing what they want and need as well as how they leverage data to produce actionable business insight.
  3. New economic and technology advantages drive innovation. Open source solutions like Hadoop can make business analytics more affordable and therefore more accessible as does running diverse workloads on commodity hardware. Moreover, new technologies, such as in-memory analytics, faster storage and compute capabilities as well as cloud and distributed architectures, are making a big difference by delivering increased speed and scalability to perform automated and advanced analytics.
  4. New data types produce more mature views of information. For the past 20 years, companies have looked longingly at some data they wished could be incorporated but couldn’t because the technology wasn’t there or it was cost prohibitive. Now those barriers are breaking down, enabling organizations to add social, machine and sensor data for diverse and interesting data perspectives.

New types of data can serve a wider community of users who will want to mix, match and mash up information to yield value in responding to business needs. This will lead to a more mature mantra, from any company looking to innovate: “Put the right data, on the right platform, at the right time, for the right workload.”

In my next post, I’ll offer more insights from TDWI by sharing experiences and more real-world examples from my class, “Social Analytics: Driving Real Business Value with Big Data.” Until then, drop me a line at [email protected] to share your mantra for business analytics.

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