Next Generation Point of Sale Analytics

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.

Note

• 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.

• Data handling
• It's all about speed
• Scale out
• Structured BIG DATA handling with RedShift
• more Redshift
• Analytic Freedom

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

• True multi-retailer capability
• Data blending
• Data blending (part 2)

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.

• Reporting is NOT Analytics

Can you buy good analytics? Yes, but there are also a number of pseudo-analytic solutions in the market that have little to no analytic power - caveat emptor!

• An Analytic name is not enough

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.

• Analytic Power
• Bring the analytics to the data

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.

• Analytics are for everyone

Analytics are for everyone !

Analytics are for everyone! Well, not building analytics, no. That needs a high level of expertise in statistics, machine-learning, optimization, programming, database skills, a healthy does of domain knowledge for the problem being addressed and a pretty wide masochistic streak too.

Using analytics, now that is for everyone, or at least it should be. We all use analytics, and, I think, the best examples, we use without thinking about just how complex it is.

Is there anyone out there that hasn't used an electronic mapping service (GPS) for directions? Even ignoring the electronics, these are remarkable pieces of engineering! An extensive, detailed database of road systems and advanced routing analytics to help you find the best route from A to B without sending you backwards down one-way roads or across half-finished bridges.

Perhaps you're thinking it's not that hard? Could you build it? What if I got the data for you? No? But you can use it right? They are not perfect, mostly I think because of data cleanliness problems, but they are close enough that I don't travel far from home without one.

More examples. Anyone used a search engine? How about an on-line weather forecast? How about web-sites that predict house-values? Recommendation engines like those used by Amazon and Netflix? All heavy analytic cores wrapped in an easy to consume, highly usable front-end.

These are, I think, among the exceptions in analytic applications - good analytics AND good delivery.

I talked about pseudo-analytics in a recent post: shams with no basis in science wrapped in a User Interface with the hope that nobody asks too many questions about what's under the hood. This is not good analytics.

Unfortunately even good analytic tools get under-used if they have not been made accessible to the poor people that have to use them. Spreadsheet tools probably top the list for unusable analytic applications: unusable that is by anyone except the person that wrote them. Sadly though, I have seen many examples both in reporting and applications where so little effort was put in to User Experience that any good analytics is almost completely obscured.

Building new analytic capability is a highly skilled job. Delivering analytic results in an easy to consume format so that it gets used is also a highly skilled and, frankly, often forgotten step in the process. After all we do build analytic tools so that they get used. Don't we? Sometime I wonder.

Visualizing Forecast Accuracy. When not to use the "start at zero" rule ?

I recently joined a discussion on Kaiser Fung's blog Junk Charts , When to use the start-at-zero rule concerning when charts should force a 0 into the Y-axis.  BTW - If you have not done so, add his blog to your RSS feed, it's superb and I have become a frequent visitor.

On this particular post, I would completely agree with his thoughts was it not for this one metric I have problems visualizing, Forecast Accuracy.

Data Visualization - are pie-charts evil ?

I'll be speaking next week at the Supply Chain Management Conference at the University of Arkansas on how data-visualization enables action.   

Good visualization is fairly easy, unfortunately, building bad visualizations that are hard to use, easy to misunderstand and that obscure and distort the data you are trying to present is even easier - many analysts can do it without trying to.



In honor of the event, I'm resurrecting a post I created a couple of years ago "Are pie charts evil or just misunderstood".  I wrote this around  the time I was moving away from a trial and error approach  (and 20 years of trial and error effort does get you cleaner visuals) to attempting to understand why some visuals so clearly work better than others.  

It turns out that there are some great frameworks to help in building better visuals.  Join me next week and we'll talk about human graphical perception, chart junk and non-data ink.

Enjoy !

Data Visualization - enabling action

I'll be speaking next week at the Supply Chain Management Research Center Conference at the University of Arkansas on how data-visualization enables action.

The basic premise (and one I firmly believe) is that the hardest part of any analytic project is not defining the problem, doing the analytics or finding the "solution", it's enabling action. Far too many otherwise excellent analytic projects, tools and reports go unused because the results are presented in a way that is somewhere between difficult-to-understand and incomprehensible.

Are pie-charts evil or just misunderstood ?

OK - I admit it: when I see a pie-chart in business analytics, my blood pressure rises and, yes, I am apt to tell the analyst exactly what I think of the monstrous, hard to read, waste of space and ink they created.

I am most definitely NOT the first person to suggest that pie-charts are over used and close to useless.  Google 'Pie Charts are Evil' and see for yourself.  This is an area where career analysts tend to agree, yet, in practice, pie-charts are very, very heavily (ab)used.  In the hope that I can influence even a handful of people to create fewer of these eyesores I'm adding my voice to the argument.