Digitizing Subject Matter Expertise Part 3: Implementing Digital Knowledge Strategies

By Michael Guilfoyle

Industry Trends

Note: This is the third blog of a three-part series that examines human expertise as part of digital transformation. Digitizing subject matter expertise is critical for companies that want to implement prescriptive, agile operational processes, such as maintenance. The term for doing so is called digital knowledge. Digital knowledge is the discipline of encoding human expertise and data from across silos, digitizing these insights, and then driving them into relevant decision flows. Analytics are key tools for digital knowledge, though many companies have yet to use them in this way. If you missed them, read Part 1 and Part 2.

Implementing Digital Knowledge Strategies

To remain competitive and thrive, industrial companies need to integrate human expertise into digital transformation. Doing so means overcoming the barriers inherent in tribal and stranded knowledge and capturing that expertise. Digital knowledge is the means for doing so.

Digital knowledge is the discipline of encoding human expertise and data from across silos, digitizing these insights, and then driving them into relevant decision flows. This enables the subject matter experts to make better and faster decisions that improve operations.

Implementing a digital knowledge strategy requires a specific, though relatively straightforward, methodology. This combines a knowledge-centric orientation, a range of analytics techniques, artificial intelligence (AI), and technology. Companies wishing to do so should:

  • Begin with the business problem and relevant subject matter experts, not data. Lack of a business problem-centric orientation is often the undoing of many digital transformation projects. Companies too focused on data become overwhelmed by the volume and complexity. Reactively, digital transformation initiatives become a series of complicated IT projects, as leadership deems that group most capable of managing data issues. When companies begin digital transformation by first defining the business problem, the expertise and decision flows needed to solve it becomes central to the task. As they know the business best, SMEs drive the knowledge creation process, adding the necessary context and decision flows for solving the problem. By removing the gap between problem identification and the knowledge needed to solve it, companies accelerate the speed of digital transformation.

  • Let the data format and sources guide the analytic techniques. When it comes to implementing knowledge-centric digital transformation, both simple and complex techniques can be applied. What is important is that the technique be suited to the data, rather than applying a one-size-fits-all approach. These data are likely to be a mix of structured and unstructured formats, including work logs, applications, event reports, images, emails, manuals, historians, the internet, etc. A range of techniques may be needed, from decision trees to cognitive analysis, for example.
  • Speed continual improvement by removing data science skill barriers for SMEs. Once engaged in digital transformation, SMEs will want to continually innovate to improve and simplify aspects of the business that impact their role. However, they lack critical advanced data science skills. Eliminate that gap with tools designed to assist knowledge-centric analysis, such as semantic search, natural language processing, drag-and-drop data mashups and blending, guided and auto-generated models, and model libraries.
  • Amplify knowledge to create new value. As decision flows become digitized and operationalized, the potential exists for connecting them to drive additional value. Secondary and tertiary interdependencies can be identified and leveraged, providing a more holistic, informed view of the business. The insights provided by the connected decision flows can create new value. For example, the flows can be pushed out as role-based recommendations to any affected personnel, including engineers, finance, and field workers.

As with any business transformation, achieving timely, measurable results determines success. Some of the world’s largest industrial companies are using digital knowledge strategies to drive significant business benefits. Many are realizing results in half the time of prior efforts, all without having to bring in an army of consultants.


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