I had the opportunity to speak at TDWI in Chicago today. It was a tremendous venue and a well organized event. Thanks to the TDWI team. I spoke on the topic of machine learning and the big data revolution. The slides are below, although they are not all self-explanatory.
3 key points from the talk:
In the 20th century, scale effects in business were largely driven by breadth and distribution. A company with manufacturing operations around the world had an inherent cost and distribution advantage, leading to more competitive products. A retailer with a global base of stores had a distribution advantage that could not be matched by a smaller company. These scale effects drove competitive advantage for decades. The Internet changed all of that.
In the modern era, there are three predominant scale effects:
-Network: lock-in that is driven by a loyal network (Facebook, Twitter, Etsy, etc.)
-Economies of Scale: lower unit cost, driven by volume (Apple, TSMC, etc.)
-Data: superior machine learning and insight, driven from a dynamic corpus of data
The Big Data Maturity curve
This is the barometer for any enterprise seeking competitive advantage, based on data. Many companies are beginning to utilize new techniques to reduce the cost of data infrastructure. But, the competitive breakthrough comes when an enterprise moves to the right side of the curve: Line of business analytics to transform operations and new business imperatives and business models. I alluded to a number of companies that I admire for leading on this side of the curve: CoStar, StitchFix, and Monsanto.
A proven and repeatable methodology for applying the value of data science and machine learning in the context of an enterprise. With thousands of successful engagements, we have learned a lot about what works (and what does not). I've seen companies achieve major breakthroughs leveraging this methodology, often ending months/years of frustration. Any organization can lead the revolution with AnalyticsFirst. Let me know if you are interested!