Note: This is the second 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 is a key tool for digital knowledge, though many companies have yet to use them in this way. If you missed it, read Part 1.
Tribal Knowledge Continues to Confound
Going back decades, capturing and applying knowledge has been one of the most continually pursued, mission-critical tasks undertaken by industrial companies. New technologies, training, and educational methods, as well as systems and work tools, have been used to try to capture and transfer tribal knowledge and best practices from individuals to organizations.
Entire boutique industries have risen (and fallen) trying to “crack the code” of tribal knowledge. Additionally, countless internal initiatives have been funded and then died on the vine. Caught between the need to create organizational knowledge and the perceived prospect of compromising job security of their high-value experts, companies have struggled to progress.
Now, many experienced individuals, including the often thousands of these subject matter experts (SMEs) in large organizations, are reaching retirement age. As a result, businesses face losing the collective experience and expertise of these workers while simultaneously competing in disrupted markets.
Stranded Knowledge Persists in Digital Transformation
In addition to dealing with the challenge of tribal knowledge, industrial companies have a long history of leaving actionable information stranded in data and their sources. This trend continues despite their improving ability to access this information. Vast amounts of knowledge lie trapped in sources that are often tightly siloed and collected, but rarely used, or not part of an “active” knowledge base application.
These data are usually a mix in structured and unstructured formats. Examples include photographs, audio, spreadsheets, paper-based work orders, emails, reports, maintenance logs, streaming video, confidence ratings, industry-specific standards and process flows, social media, etc.
At the end of the day, data is everywhere but knowledge remains largely hidden. If large industrial organizations cannot identify, access, contextualize, and share this critical subject matter expertise, which is their intellectual property, opportunity for transformation will remain limited.