Book Review: The AI-Powered Enterprise

Below is an extended version of a review I posted on Amazon of The AI-Powered Enterprise:: Harness the Power of Ontologies to Make Your Business Smarter, Faster, and More Profitable, by Seth Earley, CEO of Earley Information Science (EIS). The original review can be found here. Full disclosure, I was given a copy of the book to review.

Not long ago, everyone was exclaiming how data was the “new oil”.  Well, if companies recognize that data are such a valuable commodity, then they need to recognize the importance of investing time and effort in preparing and managing their data/information assets so that AI technology can produce the best-possible value-added outcomes.

In his book, The AI-Powered Enterprise, Seth Earley outlines key principles to apply when developing information products. The information products can include internal collaboration tools, customer-facing ecommerce websites, content marketing programs that deliver personalized content, and other data or content-centric offerings.

The early chapters of the book provide a trove of practical advice on how to create a robust foundation for any knowledge management project. Seth emphasizes the importance of building an ontology that links concepts from information silos across the organization. And he provides case studies with relatable examples from his consulting practice that make (arguably) dry topics like ontology development come to life.  

Later chapters describe how AI can enhance marketing, ecommerce, sales and employee productivity knowledge products.

The maturity models in the final chapter lay out the stages of competency for content optimization, customer information, product information, knowledge management and overall orchestration of the preceding components across the enterprise. These detailed models are valuable tools for planning and governance of any internal or external data or content development project. The inclusion of these maturity models and case studies that put the models in context makes The AI-Powered Enterprise a practical handbook for both executives who are overseeing the digital transformation of their organization and managers responsible for implementing and managing information products.

As someone who has been involved in designing information products and advising publishing companies on digital transformation projects for over 30 years myself, a few of my favorite takeaways include:

• Never embark on an AI project without a fundamental understanding of the information sources that will be used and how internal and external customers will use the enhanced information.
• Every data science or content analysis project requires investment in preparing the data sources to determine completeness and consistency. This data preparation steps aren’t sexy and are described by such terms as data scrubbing, data cleansing, data normalization, data wrangling, data harmonization, and quality control. I’ve even seen the term “data janitor” used to describe data preparation tasks.
• There are technologies that can help with some of these tasks, but I agree with Seth when he warns against letting overzealous salespeople convince you that their AI solution doesn’t require data preparation work.
• Knowledge management cures information overload.
• Search is conversation.

AI and Machine Learning (ML) are dynamic fields that are likely to continue to grow exponentially. Seth’s book provides a superb way to play catch-up in the field of knowledge management to put your company in a strong position to benefit from new technologies—and avoid costly missteps.

I highly recommend this book for anybody involved in the development or management of data projects—and that includes digital content management and content marketing programs.

Data Drive Efficient Market Transactions

Much of what we do in our business lives can be reduced to a market model. We buy, sell, arrange meetings, seek funding, invest, hire, travel, …. You get the idea. All of these activities require connecting parties to each other and, if done appropriately, they result in making the best match between parties.

In many transactions, the price of a good or service is the key variable on which to make a match. In others, price may not be a major factor at all. In fact, the two economists who just won the Nobel Prize in Economics specialize in making markets in areas such as organ donations or matching medical residents with hospitals, where price is not the central variable to match.

Making Markets was the topic of one of the sessions I moderated last week at the Data Content 2012 conference. Data Content has been at the forefront of data publishing advancements over its celebrated 20 year history. In the Making Markets session we focused on the most common and well-understood type of B2B transaction: connecting buyers and sellers.

The three companies represented on the Making Markets panel help connect buyers and sellers in specialty markets: CapLinked in the investment sector, by bringing together potential funders and companies seeking funding; The Gordian Group in the construction sector by matching contractors to job order contracts; and Fabricating.com in the custom manufacturing segment by matching industrial companies with manufacturers of specialty parts.  The speakers emphasized how operating in the “neighborhood” of the transaction creates an opportunity to collect transaction-related data which in turn add more value to the match-making process—creating a virtuous circle.

Other sessions at Data Content touched on how data collection and data management are only a differentiating factor when hard work is put in to cull together hard-to-aggregate data or clean messy data. If it’s too easy to compile the data, you won’t have a defensible resource. But that’s perhaps the most distinguishing benefit of becoming a market-maker: the transactional data that is generated by the match-making process become a unique data asset that cannot be replicated.  These secondary data can be organized and used for industry benchmarks and can be fed back into the matching algorithms to build a continuous improvement loop.

As pointed out by my colleague Russell Perkins in his closing presentation at Data Content, in the era of Big Data, data produced by specialty publishers may just be the special ingredient that helps “solve the ‘ last mile’ problem to make Big Data actionable”. In particular, the trusted and verified contact information supplied by publishers can help make the final connection between buyers and sellers.  In the Making Markets session, we saw ample evidence that structured data supplied by B2B data publishers can be put to use to drive efficient transactions throughout the match-making process.

The Semantics of Big Data

I had the pleasure of attending the Big Analytics Road Show in Boston this week. The presenters and sponsors did an outstanding job of describing the “big data” ecosystem. They even offered clear descriptions of Hadoop and MapReduce for non-technies, which is quite an achievement.

The most rewarding aspect of the day’s program, however, was its emphasis on how the data can be used to add value to business decisions. Consequently, the focus wasn’t on acquiring massive quantities of data (although zettabytes and yottabytes were mentioned!)—or even on the value of organizing big data sets. Instead, the program provided many examples of how analysis of structured and unstructured data in tandem can lead to new insights that can improve business processes and marketing decisions.

Years ago, at InfoCommerce Group we coined the phrase “data that can do stuff” to describe the advantages of well-designed data products. In essence, a data product that is designed to meet a defined need of a target audience becomes a decision tool when analytics are applied. With the era of big data upon us, even textual data and real-time streams of behavioral data can be leveraged via semantic and pattern matching technologies to obtain data that can do stuff. Furthermore, the different types of data can be overlaid to achieve higher levels of insight into customer behavior or patient outcomes, for example.

The takeaway point: data analysis tools and techniques that used to be available only to big life-science companies and search engines are now entering a phase where the costs make the technologies more widely accessible. However, as someone mentioned at the Big Analytics event, Gartner Group places big data at the peak of inflated expectations on its hype cycle curve. I agree with Gartner because of the level of noise surrounding big data. Nonetheless, with proper alignment between the data, business goals, and execution, opportunities to benefit from big data—or should I say big analytics—exist today.

Secondary Data Usage in Healthcare

I was guest speaker at the March 22, 2012 “Let’s Talk HIT” series hosted by Scratch Marketing & Media in Cambridge, MA. The topic I chose was Secondary Data Publishing in Health. Health Content Advisor’s parent company, InfoCommerce Group, has a long history of guiding business media companies in constructing data products, but increasingly we are finding interesting examples of secondary data products that develop as a by-product of technology companies. Electronic Health Records (EHRs) represents one of the more compelling examples of information technology that has the potential to spawn a new generation of data products.

Scratch Marketing has posted the video of the talk, which was structured as an interactive group discussion, in 8 parts. See their YouTube page for the list of segments: http://bit.ly/H9Wjk9.

See the event recap by Lizzie McQuillan at Scratch Marketing here:

http://scratchmm.com/2012/03/event-recap-let%E2%80%99s-talk-hit-with-janice-mccallum/

Also, for a provocative view, read Marya Zilberberg, MD, MPH’s takeaway from the evening’s discussion:

http://evimedgroup.blogspot.com/2012/03/how-our-healthcare-spending-is-like.html

Thanks again to Scratch and the many Boston-area (stretching all the way out to the Berkshires!) health IT, public health, healthcare publishing, entrepreneurs, and marketing experts who attended and participated in the discussion. Scratch Marketing added Twitter handles to the video, which helps tremendously in identifying each speaker.

Event Planning for 2012

At this eventful time of year, I thought I would hold off from sending a long post and instead focus on conference and event schedules. Don’t worry, the year-end review/look ahead post will be forthcoming after the 1st of the year.

There are so many good events to choose from, especially in the healthcare and health IT spaces, that it’s difficult to decide where to devote time-constrained resources. The Events page that we added to the Health Content Advisors site earlier this year lists all major events that I or my colleagues will be attending. At this point, only past 2011 events are listed, but we’ll update the list over the holiday period.

Somehow, I chose a fantastic mix of live events to attend last year and I hope to make a repeat appearance at all of these events in 2012. I’m making plans for #HIMSS12, February 20-24 in Las Vegas now and hope to add the SIIA IIS conference, January 24-25 in New York to the list for 2012.

When we update the Events page, we’ll add links to blog posts, pictures and videos from the events. As a preview, here’s a short video interview I did with HCPlive.com at the Health2.0 conference in San Francisco:

Also, please check out my previous post on Using Game Dynamics that includes a link to a video of my session at Data Content11 that focused on using game dynamics in market research and provides examples from healthcare research, including PatientsLikeMe.   

That’s it for now. Happy holidays and best wishes for a 2012 that exceeds your expectations!

Janice