5 Principles for Creating an Intelligence Factory: Rick Chavie, EnterWorks CEO
Earlier this week, following the 2017 Fortune Brainstorm Tech Conference, I provided commentary on using Assisted Intelligence as a helping hand. Let’s take the discussion a step further by outlining the five principles for creating an intelligence factory for your business.

EnterWorks’ heritage is in military intelligence, collecting heterogenous data from around the globe to serve up secure, role-based views of easily consumable content.  We did this by applying our virtual data model technology to enable meaningful relationships across data entities for a single, cohesive view of content.  Our work for the NSA and other agencies remains largely classified, but the premise of our platform endures as a building block for having a data foundation at the “atomic” level that can be recombined in unlimited ways.

Our idea for the “intelligence factory” is making AI a “consumable science” for decision-makers in your company. Just as Henry Ford’s Model T democratized the automobile. As the narrator said in the movie Seabiscuit: “They called it the car for Everyman…Of course, the real invention wasn’t the car; it was the assembly line that built it. Pretty soon, other businesses had borrowed the same techniques.”

Your intelligence factory should support your full organization with shared, foundational data.  ERP systems focus too much on transactional data. Big Data can be an ocean that drowns you in data. But, a factory approach to intelligence assists everyone in decision-making roles.

We see five principles for building an Intelligence Factory for your business:

#1:  Like a factory, you need 6 sigma-type data discipline. Sure, you can derive insights from messy data, but the upside in cleaning, linking, and enriching data is compelling.  The tough way to learn this principle: companies who exposed bad product catalog data on the web, then saw “one click away” in action as customers moved to competitors.

#2:  The data equivalent to a factory bill of materials is a content in context model. With a multi-domain master data platform such as EnterWorks, you can weave combinations of product, digital assets, customer, brand, location, collections, and time to help you personalize your website, conduct precise marketing campaigns, offer optimized offers based on preference and behavior, and much more.

#3:  Functional silos are okay again.  Having a common, shared data store for all essential data means you can run a job shop approach if you like (i.e. your data production uses category teams to focus on an event such as a seasonal launch) but it is not necessary to do so.  Just define your processes and roles and handoffs and approvals around your functional specialists and skillsets and silos, then let the system support the orchestration of data through the content lifecycle.

#4:  Enable precision in the face of complexity.  Orchestrate business rules, data relationships, and behaviors around “atomic” level data that can be recombined at will by the decision-maker. What are the building blocks for this approach? First, assess the types of decisions (one-time, repeat decisions, complex choices). Second, assess whose decision (yours, your customers, your trading partner).  And finally, determine the value that comes from those decisions (financially, strategically, and competitively).

#5:  Apply AI tools to assist your team, whether in simulating alternative futures, applying pattern recognition to identify crucial inflection points, or in simply increasing your chances of avoiding decisions that are not viable.

The most invigorating interaction I had on the topic of AI in decision-making was with Babur Ozden, Founder and CEO of MAANA, a knowledge management platform for encoding expertise digitally to accelerate digital transformation across enterprises such as Chevron and Shell. But you don’t need to be facing the expensive investments that oil companies make in drilling to realize the benefits of assisted intelligence.  Companies of all sizes face complex decisions, but those that can leverage the instincts and intuition of their people in combination with AI can race through the alternatives for a competitive advantage.  So, put your own intelligence factory to work!

Rick Chavie

Rick Chavie

Rick Chavie was appointed CEO of EnterWorks in May 2015. He came to EnterWorks after serving as SVP, Global Solution Management with hybris and SAP’s Customer Engagement and Commerce group, where he brought together digital and physical commerce and CRM assets for seamless customer experiences. Mr. Chavie brings industry experience from his leadership roles at retailers such as The Home Depot and C&A. He brings technology experience from his role as the global marketing leader for NCR’s retail and hospitality business, and management consulting expertise from his partner roles at Deloitte and Accenture, where he served clients across retail, branded consumer and wholesale verticals. Chavie is a Harvard MBA, a Fulbright Scholar in International Trade, and a summa cum laude graduate from the University of St. Thomas in Minnesota. He is a noted speaker at industry events, an author on the wholesale industry, and frequently comments on commerce, marketing and customer engagement topics.