Some timely reminders of the downsides of relying on algorithms particularly in fast changing markets.
I am impressed by platforms that use machine learning to help leaders focus on on people, assets, events where decisions must be made rather than those situations that do not need change.
360Globalnetunstructured & structured data is key to this
helping insurance companies focus on potential fraud whilst helping bona fide claimants get claims processed faster. Analysing
Or Carto, enabling enterprises to join & analyse spatial and enterprise data to see the relationships and predict traffic flows, optimal locations and far more.
Or Logi Analytics bringing self-service analytics to people across an organisation to allow operatives and team leaders to combine experience, knowledge to interpret predictive analytics more accurately.
All three bring the power of self-service albeit in a secure, scalable and practical manner that bridges IT and the business user.
And allow users to embed analytics in enterprise apps and workflows/processes so that users can make & execute better decisions because they don't have to come out of the systems they are using for the job in hand.
Machine learning algorithms need to be trained, and to be trained effectively they require a lot of data. Quite often, machine learning algorithms are trained on a particular dataset and then applied to make predictions on future data, the scope of which cannot necessarily be anticipated. "What is an accurate model on one dataset may no longer be accurate on another dataset if the underlying characteristics of the data change," said Spencer Greenberg of ClearerThinking.org, in an interview. "That may be fine, if the system you are making predictions about changes very slowly but if the system changes rapidly, the machine learning algorithm may make very poor predictions, since what it learned in the past may no longer apply."
