You may find this article of interest.
A brief history of our (recent IM/IT) time
thanks for sharing this. It was a well written and informative post, which made me realize in how much our IM world has changed in a relatively short period. Just looking at your illustration (shared below) shows the rise and fall of topics - who of us remembers working on OPAC (Online Public Access Catalog - for the non librarians among us) and who would have predicted the enormous rise of SharePoint?
Thanks Dennie good to know someone reads them :-)
I started at BG Group in 1991 when most seismic interpretation and map creation was performed on paper using crayons, then digitized. Some transformations indeed have occurred since then with respect to how we create, use and find information.
A particular hobby of mine is machine learning which I did not elaborate on too much in the article. This too is becoming democratized. It is now possible for anyone to download a vast sum of human knowledge from the web (e.g. contents of Wikipedia) or elsewhere and using freely available OpenSource toolkits, perform your own analyses.Perhaps we are seeing similar trends being repeated in this space. that occurred with the computer and the Internet. Once a preserve of only the 'geeks' they are now mainstream. I see machine learning moving that way.
Many commentators spout its the end or diminishing role for the Library and Information Science Professional (LIS) in organizations. Things have changed for sure, but you can argue there has never been a more exciting time to be an information scientist in an organization - the possibilities to add value to the business are endless. Its a new take on an old art!
Thanks for sharing Paul. Really good article. I wonder whether SharePoint will continue to be a market leader in the future.This will no doubt depend on it's ability to adapt to the IM/CM changes.
Machine learning was on of the top 10 technology trends that Gartner predicted for 2016 - extract from the document below
Advanced Machine LearningIn advanced machine learning, deep neural nets (DNNs) move beyond classic computing and information management to create systems that can autonomously learn to perceive the world, on their own. The explosion of data sources and complexity of information makes manual classification and analysis infeasible and uneconomic. DNNs automate these tasks and make it possible to address key challenges related to the information of everything trend.
DNNs (an advanced form of machine learning particularly applicable to large, complex datasets) is what makes smart machines appear "intelligent." DNNs enable hardware- or software-based machines to learn for themselves all the features in their environment, from the finest details to broad sweeping abstract classes of content. This area is evolving quickly, and organizations must assess how they can apply these technologies to gain competitive advantage.