We have been talking about analytics platforms for some time, but it does feel a bit like the debate does not inspire the enthusiasm it deserves. A major SAS study on readiness for AI, carried out in spring 2017, found that only 12 percent of respondents felt that their analytics
Beyond the wow around artificial intelligence is the reality. If you don’t use AI to make actual business decisions, it’s probably a waste of time, effort and money! From recommendations for your playlist to the next-best offer to decisions about customer targeting and company strategy, we are all looking for
At the German SAS Forum, Sascha Schubert and I ran some hands-on sessions or workshops on data science and analytics. You might say that this was nothing new, but I noticed some changes from previous forums that I think may point towards wider changes in the analytical landscape. Here, then,
This is the second article in a three-part series on how to reduce uncertainty in the supply chain for lower costs. The first was about reducing uncertainty downstream.This article deals with reducing uncertainty within the company. There are several ways to define uncertainty. I like two angles on uncertainty (which are not
Twelve years ago, in 2006, Time magazine made an interesting move in deciding on its “Person of the Year.” Instead of one of the many eminent scientists, world leaders, or stars of stage and screen who had influenced the world that year, Time chose to recognise a social phenomenon. It
When I ask people what they think the Internet of Things (IoT) is all about, the vast majority will say “smart homes,” probably based on personal experience. If I say that it is also about industries making using of data from sensors, then most people’s immediate reaction is to think
It is a truth universally acknowledged – if not always acted upon – that advanced analytics, and AI in particular, need data. What’s more, it needs lots of data, and it needs to be relevant and of good quality. Companies with access to more data tend to perform better, which
In my previous blog post, I talked about how both retailers and consumers can benefit from applications of artificial intelligence and discussed some compelling use cases. I would like to take this post a step further and delve into a particular bugbear of mine: the struggle to find the right size. In
Much of the discussion about AI centres on what the algorithms can do, as well as the potential for change and/or disaster. But what about the team behind the algorithms? Few discussions have focused on this most essential group, and not just the modellers, but the data sources, the trainers,
Analytics platforms have a lot to live up to. The scope may be fairly straightforward, but expectations can be high, and there is a wide range of users and customers, all of whom have slightly different needs. This post explores what IT decision makers can expect from an analytics platform.
