Strong evidence indicates that imitation and innovation have been driving the spread of Enterprise 2.0 tools. Using modeing techniques,McKinsey found that 35 percent of the companies had adopted social technologies in response to their adoption by competitors.
Every industry is becoming an analytics industry because of the inclusion of data-driven technology. Traditional industries, such as healthcare and finance, are actually purchasing analytic technologies with the intent of becoming digital leaders in their industry. IoT is regarded as a future trend, and according to Gartner, by 2018, six billion connected things will be requesting data support. This requires tools that are future-proofed to handle the mass and types of data that Gartner is forecasting.
Big data is now moving from the sole care of data scientists and becoming accessible to employees throughout organizations. The mystique surrounding data analytics is falling away, with tools designed to let non-technically-minded people understand metrics.
What's keeping leaders from adopting machine learning? Well, tools are still evolving, practitioners are scarce, and the technology is a bit inscrutable for comfort. But five vectors of progress are making it easier, faster, and cheaper to deploy machine learning and could bring it into the mainstream.
For anyone that runs facilitated workshops that need Sticky walls to post idea cards on, here is a cheap and easy way of making a portable and re-usable 'sticky wall'
"In most group chats, important things get lost in the noise. Also, remember the chat messages and file transfers you missed because you were offline during lunch? Exactly. Not cool. So we built a better chat service. Simple, common-sense stuff."
You've probably encountered the usual issues of group decision making… People who dominate the conversation, quiet people whose ideas never get heard and all those post-it notes you have to write up.
GroupMap solves this by capturing individual thinking first, then reveal the group perspective, all in real-time. Now that's true collaborative decision making.
Capability maturity models have been around for a while in other disciplines, most notably in software development projects. Almost all of the models owe their origins to the collaboration between the US Department of Defense and the Software Engineering Institute of Carnegie Mellon University. The Capability Maturity Model was originally a tool to assess processes - in particular the processes of a contracted third party. In that sense its intent was to reduce risk.
"Remember to complete the on-line profile and bring a copy with you to the event! It should take no more than 15 minutes of your time and will be paidfor by KIN. We will use the profile during Ian Corbett's session on 26th March."