Toward the end of 2017, Harvard Business Review published its annual list of “Best-Performing CEOs in the World”, based on a number of HBR’s chief executive ranking metrics.
One of the greatest movies about invention over the past 35 years has to be “Back to the Future.” Who could ever forget about time travel via the flux-capacitor-equipped DeLorean in this pop culture classic? The thrill of discovery and persistence through initial failures is what drove Doc Brown’s pursuit of his invention throughout the entire movie.
The Inventor, our third cognitive persona in this series, was inspired by this same thrill of discovery evident within Doc Brown’s passion for invention, with even more relevant Kingland inspiration coming from our passion for data. The Inventor’s role within our Cognitive Platform is to discover literally anything of interest from a “mountain” of data – trends, relationships, quality issues, and more.
Topics: Text Analytics
My Dad’s favorite advice to me growing up was “Be a Gentleman and a Scholar.” If I didn’t hear that daily, I heard it at least once a week. During the 22 years of my kindergarten-through-college student life, it was a very quick way of saying:
I’ve collected sports cards ever since I was 11 years old. Something about amassing and organizing a collection of cards that chronicled the bios and stats of my favorite sports stars always appealed to me. Included early on in this collection pursuit was a bonus lesson on the basic laws of supply and demand – generally speaking, the tougher the sports card is to pull from a pack of unopened cards, the higher the market value. The most popular sports stars create the most demand, also driving market value higher. I still remember the exact moment 25 years ago in 1993 when I pulled a rare (at the time) card of Michael Jordan from a pack of basketball cards – the perfect combination of scarcity and star power! The card immediately vaulted to the top of my collection.
Where is Kingland focused within the overall Cognitive / AI / Machine Learning space? Our passion and investment are in two primary areas:
- Extracting data / important information out of unstructured content
- Learning from vast amounts of data to make discoveries and decisions
Examples of Enterprise Data Management Requests
Regulatory documents, government registries, corporate sites, news and industry trade journals, product documents, you name it – they all represent important sources that contain an incredible amount of valuable data, insight, sentiment and analysis that is ripe for processing. Clients approach Kingland because of a great opportunity for efficiency – they typically already staff teams of data / business subject matter expert analysts to manually extract data and information out of these types of unstructured sources, yet they continue to pursue higher quality, better timeliness, and more productivity from these teams when managing data from such sources.
Historical data from the enterprise data management programs within our clients offer a level of quality management and predictive capabilities, if harnessed appropriately. In one example, legal entity data that has been classified with NAICS codes can offer machine learning opportunities to build models that can verify the classification accuracy of millions of entities, including additionally predicting the NAICS classifications of newly onboarded entities. In another example, retailers benefit from recommendations learned from past consumer behavior, for the benefit of timely product and complementary offerings.
For years and years, regulatory and legal requirements have created hundreds if not thousands of processes that require companies to capture information in documents or records. This information includes details about their customers, suppliers, market activity, and many other facets of their business. I don't know a company on the planet that really values all of this documentation. However, in the era of Artificial Intelligence and Natural Language Processing, we're discovering that all of this documentation may be a goldmine of data for these large enterprises.
At Kingland, we use a variety of Artificial Intelligence (AI) techniques for text analysis as we build cognitive and enterprise data management solutions. One of the most sophisticated is the neural network. Inspired by (but certainly not the same as) human biology, neural networks can learn to answer questions about complex data by observing and storing examples of subtle patterns in the connections between nodes, which are loosely analogous to neurons in the human brain. Most neural networks have multiple layers, with information flowing from one layer to the next (and back again in some cases), often transforming the input into more and more abstract representations as it progresses through the layers.
There is a lot of hype around Cognitive Computing. In the past month, a Google search discovered nearly 16,000 articles devoted to the topic. For perspective on the hype, Forbes contributor Bernard Marr wrote, "Today another revolution is underway with potentially even further reaching consequences... Cognitive computing, machine learning, natural language processing - different terms have emerged as development of the technology has progressed in recent years. But they all encapsulated the idea that machines could one day be taught to learn how to adapt by themselves..."
Many of us are homeowners. We can easily remember the first house we purchased and the day we "closed" on it - all the paperwork that we signed, the new debt that we now have, etc. Most of us treated that house as one of the most important physical assets that we have because we spent more money on it than any prior purchase. We painted rooms, cleaned the carpets, and ensured that the appliances were in working order. For a moment, think if you would have taken a different approach - done nothing. At first you wouldn't be able to tell the difference, but over time the walls would reveal where the paint chipped away, the carpet would show dark spots in certain areas, the doorbell would stop working, and the garage would smell like old garbage - you get the picture. This same decay can happen with your data, if not properly maintained.