Long before Google Glass wearers made the news (and became pariahs within San Francisco-area coffee houses and restaurants), research projects at huge companies like IBM and Microsoft sought to bridge the gap between the capture and storage of corporate knowledge and intellectual property, and the difficult-to-archive individual narrative that attempted to make sense of this important, yet mostly disconnected content.
The effort of transcribing a personal experience or individual learning in context to our projects, business initiatives and other corporate artifacts (e.g., presentations, documents, spreadsheets) is incredibly difficult to accomplish in a way that can then be utilized by our knowledge management systems.
The problem with knowledge management (KM) is not a matter of data infrastructure -- whether your data resides on premises, in servers that you manage versus out in the cloud is irrelevant (to some degree) to the argument -- but with a user experience that fails to align the needs of the complex, non-linear playback mechanisms of the human brain with our systems of record.
How we collect and store information within these systems can be very structured and limiting compared to the way we experience learning. While some may dream of a Johnny Mnemonic future in which data can simply be uploaded to the brain like a massive external drive, the reality is that much is lost in the process of documenting and translating our learning into a form that can be added to, and found again, within our complex data and document archiving platforms.
The rise in social collaboration improves the collective experience of KM, but still does not resolve this fundamental difficulty in transcribing the wisdom and knowledge in the head of an individual into a consumable, reusable, contextual result. To build the next generation KM platform, we need solutions that can:
- Improve the distribution of knowledge and ideas, quickly and seamlessly
- Automatically identify patterns and themes within that content
- Expand upon, refine and convert that knowledge based on those patterns, and in context to our requirements, ultimately making it searchable (i.e. finadable)
- Correlate those patterns and themes, and take appropriate action -- with those actions also tracked and measured, as an extension of the ideas
I became interested in this problem space in the late 1990s while CEO of a small software startup in the San Francisco Bay Area. We set out to solve the knowledge transfer problem for software development and consulting organizations, coming at the problem from the software configuration management (SCM) space, and leveraging a leading SCM platform (Rational Software's ClearCase) as our engine, with a simple HTML-based front-end that connected content, people and actions in a massive contextual web akin to the many social graphing efforts underway today.
Of course, we had neither the supporting technology available today nor the funding and personnel to achieve our vision, much less what companies like Facebook and Microsoft have been able to accomplish. But this work set me on a path that has shaped much of my career, in search of a better social and KM solution that could help individuals and teams improve their ability to more easily transfer what was learned into a format that be utilized by others.
In the article "The Future of Computing," in the November 20, 2014 edition of Time Magazine, columnist Alex Fitzpatrick writes about the rise of artificial intelligence, gesture-based devices and software that learns from the actions you take and the content you consume. While the use of the term the Internet of Things (IoT) has left many wondering what the hype is all about, the reality is that the high-tech industry as a whole is rapidly approaching what seemed like pure science fiction even five to 10 years ago. From gamification and machine learning to smart watches and virtual reality goggles, the idea that we will soon be able to capture what is important to us from our business activities and seamlessly add these experiences to our collective knowledge is not so far-fetched after all.
Within the Microsoft sphere, there have been huge advances to making this future a reality. Ideas and technologies to watch for in the next year include:
Content Aggregation and Personalization
We are still a largely document-centric world, and so much of the path toward a truly digital workplace will come through the centralization of our content, organized around our personal needs. Microsoft's Office Graph provides the machine learning behind some of its newest search-based solutions, including its personalized Delve interface, which collects content that is relevant to you based on your projects, your management chain, and your social network, learning about you from your activities (or lack thereof) and refining what it presents based on your patterns of usage.
The idea is not to take away your ability to search, but instead these tools seek to improve your productivity by surfacing what is most relevant to you and your team. For example, Delve may provide documents or workflow reminders at the top of your screen based on past-due project tasks, or due to a meeting scheduled for later that afternoon.
Social Capture and Correlation
As the internet has matured over the past decade, video has become the fastest growing format for data consumption. As part of its overall NextGen Portals strategy, Microsoft has once again leveraged the Office Graph to improve the ability to capture and catalog videos content through Office 365 Video, available now as a pilot through the Office 365 First Release program. Through a simple drag-and-drop interface, videos are easily added and then made searchable and available through any device.
Within the partner community, solutions abound: Harmon.ie's Collage platform, for example, federates the social activities from leading cloud platforms so that artifacts are not lost as business activities span your most important cloud services. And the SharePoint-integrated Glyma platform provides a powerful mapping solution to help you capture, curate, find and correlate business activities, such as video and audio recordings of customer or team meetings, to key business processes and KM workloads.
One of the most powerful demonstrations from the 2014 Worldwide Partner Conference, held in Washington DC this past July, was of the new Skype translation services, a combination of audio tools (like Dragon Naturally Speaking, by Nuance) combined with Skype translation services, available today in preview only. The technology could revolutionize communication, removing the language barrier for real-time communication, but also simultaneously transcribing and recording those conversations to be captured, curated and made available through your KM platform. Combining voice interaction with artificial intelligence brings us solutions like Cortana, which, through machine learning, will adapt to our usage patterns and provide an increasingly personalized digital concierge service.
Presence and Gesture Awareness
While presence awareness has been available through tools like Microsoft Lync for more than a decade, when combined with Office Graph and other machine learning technologies, we begin to see the possibilities in supply chain and operations management automation. While in a category of its own, gesture-based controls -- using touch, as with your Surface device, or without touch, as with Kinect for Xbox -- provide a new playing field for how we interact with our personal and business platform, helping technology manufacturers to better shape the tools we use to match the ways in which we interact. This will become increasingly significant within mobility platforms, which are quickly becoming the de facto method through which the world communicates online.
Several articles and blog posts have come out over the last few weeks about the death of enterprise content management (ECM) and KM, in general. On that idea, we'll have to agree to disagree. While the traditional models of capture and storage may be on decline, with what AIIM.org president John Mancini calls the shift from "systems of record to systems of engagement," the need for knowledge management has not decreased. However, the need for better methods to capture and consume data has increased. With the wealth of technology advances underway, I predict that the next few years will become a new golden age for KM as solutions become more personalized, more powerful and more relevant.