As the internet grew in popularity, the marketing industry was quick to see that it had become an important channel for reaching out to potential customers. Websites increasingly began to host ads that were often unconnected with the site content. Advertising content became more widespread and unfortunately, also often obtrusive.
The development and use of self-service analytics has brought with it a new role in many organisations: the citizen data scientist. But is this genuinely a new role, or is it just a new name for a business analyst? Is this a definition thing? Business analysis is broadly defined as
Telecoms players don’t get a lot of positive press coverage, if we’re honest. With roaming charges and expensive long-term contracts, it can be hard to find anyone prepared to stand up for the telco industry. I think that needs to change. I have been part of this community for almost 20
A recent survey by Management Events shows that analytics has finally taken the pole position as a top strategic technology and companies are keen to capitalize the potential locked in their data. The needs for analytics are vast and varied. How can IT best manage diverse analytics needs? I will give you
In 1901, Gottlieb Daimler predicted: “The global demand for motor vehicles will not exceed one million—simply because of the lack of available chauffeurs”. Today, most people drive themselves and self-propelled cars are rapidly becoming a reality. Are we likely to see the same situation for data scientists, with more and
Over the past few months, I have travelled extensively in Europe and other regions talking to partners and clients about how they are tackling the demands of General Data Protection Regulation (EU GDPR). What I have learned is that the level of readiness, coming up on a year to the
Data visualization has been an issue ever since the development of screens. I thought it might be interesting to look at how business intelligence (BI) has changed over time. On the video I use SAS Visual Analytics (VA) to show you. Since the best examples are always practical ones, and
Predictive modelling solutions typically rely on the availability of good quality data and subsequent models which are estimated in advance using historical data. For example, a credit scoring framework to predict propensity of customer default requires the use of clean processed transactional and demographic data. Datasets used for this type
The SAS Data Management team has been on a GDPR roadshow. In addition to customer meetings, we were also privileged to meet academics and journalists who are helping customers navigate implementation choices. In Slovenia, I had the opportunity to reflect with Miran Varga for Delo on some of the journey
Today there is a lot of talk about why analytics matter. New times and the new digital world calls for new ways of working. We know that we have to increase the productivity of knowledge work, and analytics is the key. The big question is HOW. How to unlock the
