We kick off 2017 with excitement about big data and analytics still at fever pitch. Thankfully the discussion has now moved to business impact, and explores how good use of analytics is vital to make money, save money, and maintain competitive advantage. Data scientists now they have the sexiest job of the 21st century and are scarce.
Despite the hype, however, many companies are still struggling to work out how exactly to use analytics. It seems that moving from data to insights is still a big challenge. But there may be easier ways to get started with analytics than to rush out and recruit your own data scientists. I caught up with Colin Gray from the SAS Analytics On-Demand team to understand options available to organisations. Read more about our discussion and an invite to a Tweetchat on Friday 13th of January.
What’s different about how to start with analytics today?
There are now many more ways to manage data and tap into expertise. You can either set up your own analytics function in-house, or you can ‘borrow’ the analytics function, by using analytics-as-a-service. We offer this service as “SAS Results”. The decision will depend on your corporate strategy—whether you want to outsource or not—and the skills available to you. If you don’t have any data scientists, you may not be able to do your own analytics in-house. And then of course, there is the question about whether you have the necessary infrastructure and software.
How is analytics-as-a-service different from doing the work in-house?
Analytics-as-a-service is a bit like outsourcing, and shared services centres. Over the last decade or so, large companies have made huge savings by outsourcing back office functions, and sharing services. Analytics-as-a-service is just moving things a bit further up the shared services curve. Instead of having to make the investment, with analytics-as-a-service, you have analytics on demand. This means that you can use as much or as little as you need, so it’s really easy to scale up and down.
Is this an easier and lower risk approach to understand more about the potential benefits?
Yes, it’s a very good way to dip a toe in the analytics water without having to make any big investment. I think it is particularly good for small to medium sized enterprises, although any size organisation can benefit. You don’t need to recruit new staff, or incur any fixed costs, and you have no infrastructure or software requirements. You can use just what you need, when you need it. Analytics-as-a-service also delivers value quickly—usually within six to twelve weeks, compared with six to twelve months for setting up an in-house analytics function. And as a bonus, it probably comes out of an operational budget because it’s a service, rather than a capital expenditure budget.
