May you live in interesting times – or so the old Chinese curse goes. Well, we’re certainly doing that. In 2017, the global economy was firing on all cylinders, and things weren’t looking too bad. Since then, a mixture of protectionism, the Sino-US trade war and economic uncertainty has changed
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
Innovation labs were once hailed as the way in which established companies could “build in” innovation and ensure that it happened. For a while it was almost the default approach. However, in the last few years, several of these high-profile innovation labs have been shut down. It’s clear that not
There is a lot of buzz about real-time analysis just at the moment. Obtaining and analysing real-time data, the story goes, will enable us to respond instantly to things that need our attention. And it certainly is true that there are some things that are considerably improved by real-time analysis.
In seinem Buch „Competing on Analytics“ benennt Tom Davenport die Analytik als Grundlage nachhaltiger Wettbewerbsvorteile. Der Grund dafür ist der prädiktive Ansatz. Heutzutage ist es nicht mehr möglich, ein Unternehmen alleine mit Blick in den Rückspiegel zum Erfolg zu führen. Und Analytik erlaubt den dringend erforderlichen Blick in die Zukunft.
You could argue that it’s misguided for someone like me to say data science doesn't have to be difficult. After all, I’ve been in the industry for many years and should have a few tricks up my sleeve for dealing with data. But with the latest data visualisation technology –
If I were to believe the feedback I get, statisticians are among the most difficult people to work with. What’s more, they’re the only group that should be allowed to work in data analytics. It sounds harsh, but this may explain why big data projects continually fail. Businesses need statisticians who are both
Well OK, so there is an "i" in science, but being a data scientist is certainly not a lonesome job. Engagement with other team members is essential with data analytics work, so you never really work in isolation. Without the rest of the team, we would fail to ask all
As the age old idiom goes, the early bird gets the worm and the early adopter gets the break. New technologies give clear advantages to those organisations that figure out before their early-adopting competitors how to use them effectively, an advantage that recedes as others catch up, and the technology
In many ways it’s open season for open data; open data is one of those phrases we hear a lot but it’s not always appreciated as having value. The fact that it’s openly available is seen by some as proof that there’s no value in the data – unlike, for
Having worked in analytics for over 25 years, I’ve never really felt part of the ‘cool gang’. However that’s changing and all of a sudden, at long last, it is "chic to be geek!" Research published by SAS UK and the Tech Partnership reveals that from 2013 to 2020, the
I’m sure, like me, you've been annoyed at being stuck in a traffic jam in a city centre somewhere, or been frustrated at your kids leaving lights on, or annoyed with the heating coming on when the weather’s warmed up and you've not got round to adjusting the thermostat. Now,
There is lot of talk at the moment about data analysts or data scientists, but what do you need to be successful in these roles and what type of person do you need to be? The stereotypical view is that we’re ‘a bit nerdy’ and ‘walk around in white coats’,
Reading the latest issue of Intelligence Quarterly magazine has really brought home to me the limits of our human brain and its selective processing. Think about it: visualise a London bus in your mind. Even though you see them every day or have at least seen pictures, are you able