Over the last few months I've been exploring the features I want to see in a next generation platform for point of sale analytics: It's simpler, faster and cheaper, supports rapid blending of new data sources and is powered up with real analytic capability.Looking back there are a lot of posts on this topic so here is a quick summary with links back to the detail.
I have no immediate plans to build such a system for sale but I do use systems with many of these features for ad-hoc analytics as they are flexible yet relatively easy to set up and tear-down without incurring substantial overheads. Consider this series more of a manifesto/buyers-guide.
I do see changes in the marketplace suggesting that a number of DSR vendors are at least considering a move in this direction. As to which one will get there first, I think it will be whoever feels least weighed down by their existing architecture.
Database technology has moved on dramatically over the last few years. For this scale of data, analytic solutions should be columnar, parallel and (possibly) in memory. This enables speed, scalability and a simple data structure that makes it easy to hook up whatever analytic or BI tools you wish.
If the only data you have in the system is pos sales for a single retailer, you can build a reporting system ("what sold well last week") but you will struggle to understand why sales change. Bringing in other data sources: multi-retailer, demographics, weather information, promotional calendars, competitor activity, socio-economic trends, Google trends, social media, etc. allow for much more insighful analtyics. It's not easy to do this though, particularly if your source database is locked down so that it takes a software engineer to add tables
The term "Analytics" in general use covers a lot of activities most of which involve little more than reporting. In some instances you can slice and dice your way through a dataset to find insight, reporting is not without value but it's not analytics. Not even close.
To get to real, deep insights you need real analytic tools. Depending on the taxonomy you are used to, we are talking about predictive and prescriptive analytics,machine learning, statistics, optimization or data science. Most of these tools are not new but they are not generally found in standard BI offerings and even when they are (e.g. reporting level R integration) you may struggle to apply the analytic tools at scale.
Finally, whether you build your own analytic tools or buy them in to run on your platform, clever math is not enough. If a user cannot comprehend the tool or it's suggestions due to poor user interface design and /or bad visualization choices it's worth precisely ... squat.