But in today's always-on economy enterprises need to innovate on a continuous basis to keep up with new players that base their entire businesses on data and algorithms. Deliveroo and Uber are great examples of this.
To keep up with these new players, enterprises appreciate that innovation is most potent when it’s enabled at the edge – where the enterprise meets the outside world. Yet the speed of data creation, and the new sources and types of data that our hyper-connected world is creating, require more powerful analytics and algorithms than ever before. Why? Because business need to dig into the richness of this new data landscape to better understand what the data is really telling them.
Consequently, lines of business (LOBs) are creating a hybrid approach to analytic and algorithmic evolution. How? Data citizens within LOB functions are mixing open-source coding capabilities with solutions from independent software vendors (ISVs).
Is this a business risk or business advantage? It depends on how you bring everything together. Rather than buy multiple capabilities or use open source analytics – whether for traditional analytics, reporting, visualisation or to drive machine learning – businesses that want to push the boundaries of innovation should think about how to unify their analytic capabilities and data management engine. It can’t be a piecemeal approach that gets built up into a complicated mix over time. Otherwise, IT complexity and cost could end up stalling their ability to drive competitive advantage, revenue growth, effectiveness or customer satisfaction – tripping them up in the race to push innovation.
However your organisation chooses to collaborate and use data for innovation, SAS’ new cloud-based, open platform can power the future of your analytics, whether you code in Python, Lua or other languages. Find out more about SAS Viya.