Better Business Analytics: Reporting

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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.

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.

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.

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

Next Generation DSRs - Analytic Freedom !

Current Demand Signal Repositories don't play well with others. Their data is locked away behind layers of security and you can only access it through the shackles of their chosen front-end for reporting. There is no good way to get that rich dataset into other tools: you have to copy it into a new database and new data structures. (In some cases you may have to do this twice, once to rearrange the data from the DSR into a format you can understand, then again to match the data structure needs of the downstream tool.)

For small-scale models (do we do those anymore?) that sip data from the original repository you can do this through the reporting engine and live with the pain, for large scale modeling it's really not an option.

I want freedom. Freedom to analyze with whatever tools I need: The freedom to report in Business Objects, visualize in Tableau, analyze in R and run existing applications (order-forecasting, master-data-checking, clustering, assortment optimization, etc.) directly against this data. (I'm not endorsing any of these tools and you can replace the named software above with anything you deem relevant - that's kind of the point).

Much of this freedom comes from a simplified data model, enabled by new database technologies (massively parallel processing, scale-out, in-memory and columnar). See more details at data handling.

It also needs a security model that is handled by the database NOT the reporting layer or as soon as you get to the underlying data you can see lot's of interesting things you shouldn't :-)

I suppose I could live with a little less freedom if a DSR offered all the tools I need but I don't think that's realistic. Not all DSR reporting layers are equal, data visualization is hit and miss, and as I posted in An Analytic name is not enough while there are some good DSR based analytic applications you will find many use pseudo-analytics and some have no analytic basis at all.

Do you think, perhaps, that the Next Generation DSR will provide the best reporting, visualization and analytic tools available? Sorry, I don't think so. DSRs cover a dizzying array of analytic need and developing robust, flexible analytic applications, even assuming easy access to the data, is an expensive proposition for any DSR vendor to do alone. I anticipate a few strong analytic "flag-ship" tools will emerge alongside more me-too/check-the-box applications packed with pseudo-analytics.

So, what can the Next Generation DSR do to help?

make it (much) easier to get at the data in large quantities,
make it (much) easier to bring analytics to bear on that data. (Perhaps with an integrated analytic toolset)
open up the system to whatever analytic tools work best for you
make it easy for other software vendors to provide add-in analytics on the DSR data/analytics platform.

Think about that last point for a moment, no DSR vendor is big enough to provide state of the art analytic applications in all areas, but make it easy enough to integrate with and it could enable specialist analytics vendors to offer their tools as add-ins to the platform. (This could be good news for the analytics vendor too, it removes the need for them to install and maintain their own DSR just to enable the analytics)

Let's look at an example.

Today if you want assortment-optimization capability, you can

wait for your DSR vendor to develop it and hope they use real analytics; or
search for another solution and work to interface the (very large) quantities of data you need between the applications;
write your own (always fun, but you had better know what you are doing) and you will still need to interface the data.
decide not to bother

All but the last one of these are slow - I'm guessing 12 months plus.

In the NextGen world, if you want to new analytic capability, you could still write your own, it's easy to hook up the analytic engine, or, just go to the DSR's analytic market-place and shop for it.

Next Generation DSRs - Analytic power !

To handle real Analytics (see my recent post Reporting is NOT Analytics) you need real Analytic power. BI tools are based on the language they use to interrogate the database (typically SQL) and with no library of analytic tools - it's not nearly enough.

I've written a number of posts now on the next generation of Demand Signal Repositories. DSRs are the specialized database and reporting tools primarily used by CPGs for retail Point of Sale data.

So far, I've looked at the challenges (and big opportunities) around handling the large quantities of data involved: better database technologies, scale-out platforms, true multi-retailer environments, effective data blending and dramatic simplification of data structures.

Taken as a whole this get's the necessary data into one place where it is relatively simple to overlay it with the BI or analytic tools of your choice and still get good performance. This is the starting point.

Now, we can get to the fun stuff, Analytics. Let's start by addressing a widespread misunderstanding

Reporting is NOT analytics

Next Generation DSRs - data blending (part 2)

My most recent post on Demand Signal Repositories bemoaned their general lack of ability to rapidly ingest new and interesting data sources (e.g.: promotions, Twitter feeds, Sentiment analysis, Google trends, Shipment history, master data, geographic features, proximity to competitor stores, demographic profiles, economic time series, exchange rates etc.).

As a result, analysts spend far too much time collecting/copying data into ad-hoc data marts to enable useful modeling work. At the extreme, you can move Terabytes of low-level data from a perfectly good database into another one (probably on a lower powered machine) so as to manually merge it with a few hundred records of new data you need for analysis. This is slow (actually very slow), error prone and leaves very little time to do valued added work.

Based on questions from blog readers via email, I think that I failed to call out how big the gap is between where we are now and where we should be. Let me spell it out:

If I go to my (or your) IS department now and ask "how long would it take to integrate XXX data into the DSR so it is loaded, cleaned, gap-filled, matched to appropriate dimensions and ready for some interesting analytic work." I would expect to hear back "between 6 and 12 months" and that's assuming they have both some developer availability and the necessary access to add/modify data stuctures - some DSRs are locked down tight. If I went to the DSR vendor, it may be a little faster, depending on just how tightly the data structure is tied into their product release schedule. But here's the thing - I want to do this, myself, in real-time and certainly in less than a day.

Tools such as Alteryx are designed to do data blending. Alteryx in particular, seems to do especially well handling geo/demographical data, some of which comes with it as standard. They also have a number of pre-defined macros to help you get at standard data source like Google Trends and Twitter. If I understand it correctly, it does this by loading all data sources into memory. Perhaps it constructs it's own data repository on the fly, but, certainly, it does not touch the source database's data structure at all.

This would work well for relatively small quantities of data. Let's say you are examining annual sales for a product group by store - you aggregate that to a few thousand records of POS data in the DSR, load it into Alteryx, geocode the locations, match up the geo/demographic data you are interested in and you are ready to run some math. I doubt that would take more than a couple of hours. There is also some analytic power in the platform and at least some level of R integration if you wish to extend it further. For ad-hoc small (sub 10 million record?) data analytics this looks really good.

What if you want to do your modeling at a much lower level of detail though? Do you have the capacity to match across billions of records outside the DSR? Perhaps, but it's going to cost you and why move it all into another database on another expensive server when you've already paid for those in your DSR? What if you to run analytics repeatedly, do you really want to do geocoding and ad-hoc matching every time you want to use census data in an analysis? Chances are the stores haven't moved :-) and the most frequently updated census data, I think, isn't updated any more often than annually.

Better to do it once, load it into new data structures in the DSR and enable it for ongoing reporting/analytics or... did you want to force even basic reporting through the data blending platform because that's the only place you can match up multiple data sources ? I didn't think so.

If would definitely look at something like Alteryx for ad-hoc work. If you can also use it to source, transform, handle dimensional matching, deal with missing data etc. and load the results back into your DSR (where you just defined new data structures to receive it), I think you might have something.

Next Generation DSRs - data blending

Over the last few months I've written a series of posts on Demand Signal Repositories. These are the specialized database and reporting tools primarily used by CPGs for reporting against retail Point of Sale data.

There are a number of good tools in the market-place and you can derive substantial value from them today but the competitive landscape is changing...fast. Existing tools found a market because they are capable of sourcing, loading and reporting against vast amounts of data quickly. To do so they have employed a variety of complicated architectures that are now largely obsolete with recent advances in technology that can make solutions: faster, cheaper and more flexible.

Cheaper alone may be a win in the market today, but if all we do with this new power is report on "what I sold last week" more quickly and at a lower price-point I think we are missing the point.

The promise of a DSR has always been to explain what happened but much more importantly why and existing tools struggle with this:

they do not hold a rich enough repository of data to test out hypotheses.
their primary analytic tools are report-writers and pivot-tables (by which I mean that they really don't have any)

We'll come to analytics in a later post, but for now let's think data because without that there isn't very much to analyze.

Imagine that I've spent a few hundred thousand acquiring point of sale data into my own DSR and now I want to really figure out what it is that drives my sales.

How about weather. Ignore for the moment whether or not a future forecast is useful, but how about using weather data to explain some of the strange sales in history so that I don't trend them forwards into the coming year? I can get very detailed weather data from a number of sources, but can I, a system user, get that data into my DSR to start reporting against it and better yet, modeling? Probably not

How about SNAP, the US government 's benefit program that funds grocery purchases for roughly 1 in 6 US households? SNAP can drive huge spikes in demand for key products and I can easily go to usda.gov and find out exactly when SNAP dollars are dropped into the marketplace by day of the month and by state. With a little time on Google I can even see when this schedule has changed in the past few years. Can I, a system user, get this data into the DSR for reporting/modeling? Nope.

The same is true for many additional data sources you wish to work with (Promotional records, Twitter feeds, Sentiment analysis, Google trends, Shipment history, master data, geographic features, proximity to competitor stores, demographic profiles, economic time series, exchange rates etc.).

These are all relatively easy to source datasets but if the DSR vendor has not set it up as part of the standard product, you are out of luck: the technical sophistication necessary to source, load and , especially, match key fields data is beyond what a super-user, and in many cases, a system administrator can handle. Can it be done? Maybe, depending on your system, skill-level and security-access, but it's going to cost you in time and money.

Matching data in particular can be a real bear - it will be rare that you are matching products at the same level of granularity (item, location, date) and with the exact same key fields. Far more common to be matching weekly or monthly data to daily, state or county data to zip-codes and product groups to shoppable items. And do it without losing any data, sensibly handling missing data and flagging suspect data for manual follow-up.

So if you really want to do some analysis against e.g. SNAP what must you do? Download a small ocean of detailed POS data so you can (carefully) join it to your few hundred records of SNAP release data in a custom database or analytic app, build the models and then (because you can't write the results back out to the DSR) build a custom reporting engine against these results. This makes no sense to me.

The solution is something called data-blending which tries to reduce the pain of integrating multiple data sources to a level that you could contemplate it in near real-time. While I have not yet seen a solution I would call perfect the contrast with the standard, locked-down, DSR scenario is impressive.

Much of what I have seen so far happens at the individual's level: where you are doing the match in-memory and without impacting the underlying database or fellow users in any way. In many cases, particularly for exploratory work, this is preferable, but it's far from an ideal solution if you need to process against the detail of the entire database or have multiple needs for the same data.

The future, I think, will include such ad-hoc capability, but I suspect it also includes a more flexible data model that let's an administrator rapidly integrate new data sources into the standard offering.

The right tools for (structured) BIG DATA handling - more Redshift

In my recent post on The right tools (structured) BIG DATA handling, I looked at using AWS Redshift to generate summaries from a large fact table and compared it to previous benchmark results using a columnar database on a fast, SSD drive.

RedShift performed very well indeed, especially so as the number of facts returned by the queries increased. In this initial testing I was aggregating the entire fact table to get comparable tests to the previous benchmark, but that's typically not how a reporting (or analytic) system would access the data. In this follow-up post then, let's look at how Redshift performs when we want to aggregate across particular records.

Recently, I have been working with a new-to-me BI tool that has reminded me just how much speed matters. I'm not mentioning any names here, and it's not a truly bad tool, it's just too slow and that's an insight killer!

Continuing my series on Next Generation DSRs, let's look at how speed impacts the exploratory process and the ability to generate insight and, more importantly, value.

Many existing DSRs do little more than spit out standard reports on a schedule and if that's all you want, it doesn't matter too much if it takes a while to build the 8 standard reports you need. Pass off the build to the cheapest resource capable of building them and let them suffer. Once built, if it takes 30 minutes to run when the scheduler kicks it off, nobody is going to notice.

Exploratory, ad-hoc, work is a different animal and one that can generate much more value than standard reports. It's a very iterative/interactive process. Define a query, see what results you get back and kick off 2-3 more queries to explain the anomalies you've discovered: filter it, order it, plot it, slice it, summarize it, mash it up with data from other sources, correlate, .., model. This needs speed.

Next-Generation DSRs (multi-retailer)

This post continues my look at the Next Generation DSR. Demand Signal Repositories collect, clean, report-on and analyze Point of Sale data to help CPGs drive increased revenues and reduce costs.

Most CPG implementations of a DSR support just one retailer's POS data. OK before someone get's back to me with "but we have multiple retailers' POS data in our system", I'll clarify:

Having Walmart and Sam's Club data in the same DSR does not count (as the data comes from the same single source, RetailLink) and I bet you are still limited as to what you can report on across them.
If you have multiple-retailer's POS data set up in isolated databases using the same front-end... it does not count
If you have the data in the same database but without common data standards ... it does not count.
If you have the data in the same database but with no way to run analysis or reports across multiple retailers at once... it does not count.

So, yes, a number of CPGs have DSRs that support multi-retailer POS data sources, very, very few (if any?) have integrated that data into a single database with common data standards so they can report and analyze across multiple POS sources at the same time.

Does it matter? I think so, multi-retailer ability opens up big opportunities around promotional-effectiveness, assortment planning, supply-chain forecasting (demand sensing) and ease of use.

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 !

Next Generation DSRs - Scale Out !!

Last week, I posted my thoughts on how new technology enables a simpler and faster database to support your DSR applications.Next Generations DSRs (data handling). Over the next few posts I'll extend that idea to show how speed and simplicity are essential to your personal productivity, user experience and the ability to apply powerful analytic tools to your data.

In the meantime though I came across a great post in Rob Klopp's "Database Fog Blog" regarding Redshift. Redshift is Amazon's cloud-based, columnar, parallel database.

Remember that my interest in database technology is all about feeding my insatiable desire for data to drive value-added analytics, my own area of expertise. To that end, I have become adept in a number of programming languages and relational database systems and while I'm a lot better than "competent" I am not "expert". Rob clearly is an expert in this field and I will be following his posts carefully.

Here's a highlight from his post Thoughts on AWS Redshift:

... if you can add nodes and scale out to improve query response then why not throw hardware at performance problems rather than build a fragile infrastructure of aggregate tables, cubes, pre-joined/de-normalized marts, materialized views, indexes, etc. Each of these performance workarounds are both expensive to build and expensive to operate.

He goes on to talk about why scale-out has not been generally adopted and how Amazon Redshift changes the game by making it easy to acquire and release processing power on demand.

The answer does not have to be Redshift, perhaps it's Impala or Hekaton or... whatever. Bottom line for me is that new technology enables DSR's that are simpler and faster and that creates a fundamental shift in system capability.

FYI - I have done some DSR-scale testing with Redshift and the results were very impressive. More on that soon.

Next-Generation DSRs - data handling

This post continues my look at the Next Generation DSR. A DSR (Demand Signal Repository) holds data, typically Point of Sale data, and that data volume is big, not Google-search-engine big, but compared to a CPG's transaction systems, it's huge. Furthermore, the system is required to rapidly load large quantities of new data, clean it, tie it into known data dimensions and report against it in very limited time-frames.

But scale and performance needs aside, why have most (though not all) CPGs chosen to buy rather than build the capability? After all, it is primarily a business-intelligence/database application and most businesses run a number of them. One key reason is that it's challenging to get business reporting performance at this data scale from existing technology.

This post looks at how this problem gets solved today and how newer database technology can change that landscape.

The Next-Generation DSR

CPGs have had access to Point of Sale (POS) data now for many years and many of them use a Demand Signal Repository (DSR) to gather, clean and report on this data. (Actually most of them use a number of DSR's and even when they do have just one, still can't handle truly cross-retailer analytics).

I've been involved with a number of these systems as a software-buyer, a system-administrator, consultant and most recently, leading the analytic development at Orchestro.

There are some excellent tools available and, in their current form, they can help you drive both additional revenue and reduced costs when used well. However, in my experience many of these tools have been sold in under the guise of "saving time" through reporting automation. That's valuable, but it's not "finding a new sales opportunity" valuable.

I think we are still in the infancy of DSR development: systems are operating at the limits of the technology they were built on and necessary trade-offs mean that being good at one thing (e.g. speed) makes it more challenging to be good at others (e.g. analytics).

The next generation of DSR can be dramatically more effective. In particular, it will be:

Back to blogging on "Better Business Analytics"

It's been quite a while, just over 12 months in fact since my last blog post. In that time, I've been hard at work developing analytic applications for the Orchestro DSR. (Orchestro's off-shelf alerting tool is especially cool and something I am very proud of contributing to). I enjoyed my time at Orchestro, they're a good team and have big plans, but one key thing I found out about myself is that I prefer working real-life problems to developing software for someone else to have all the fun :-)

So, I'm now back full-time on consulting and I will occasionally blog on topics of interest to me. Expect to see more soon on:

Next-generations DSRs (Demand Signal Repositories)
Retail supply-chain analytics
Handling (BIG-ish) data for analytics
The right tools for the job (Predictive Analytics, Business Models, Optimization)
Some more thoughts on store-clustering
Inventory modeling at retail (and why it's different, again)
Order forecasting using POS data
Further thoughts on SNAP and other ignored demand drivers
and if there is something you would like to hear more on ... just drop me a line.

Business Analytics - finding the balance between complexity and readability

In this blog I try to present analytic material for a non-analytic audience. I focus on point of sale and supply chain analytics: it's a complex area and frankly, it's far too easy whether writing for a blog or presenting to a management-team to slip into the same language I would use with an expert.

So, I was inspired by a recent post on Nathan Yau's excellent blog FlowingData to look at the "readability" of my own posts and apply some simple analytics to the results.

The right tools for (structured) BIG DATA handling (update)

A couple of weeks ago, I ran a somewhat rough benchmark to show just how much faster large database queries can run if you use better tools.

The right tools for (structured) BIG DATA handling Here's the scenario: you are a business analyst charged with providing reporting and basic analytics on more data than you know how to handle - and you need to do it without the combined resources of your IT department being placed at your disposal. Sounds familiar?

I looked at the value of upgrading hard-drives (to make sure the CPU is actually busy) and the benefit of using columnar storage which let's the database pull back data in larger chunks and with fewer trips to the hard-drive. The results were ..staggering. A combined 4100% increase in processing speed so that I could read and aggregate 10 facts from a base table with over 40 million records on my laptop in just 37 seconds.

At the time I promised an update on a significantly larger data-set to see whether the original results scaled well. I also wanted to see whether query times scaled well to fewer facts. Ideally querying against 5 facts should take about 50% of the original 10 fact aggregation queries.

The right tools for (structured) BIG DATA handling

Here's the scenario: you are a business analyst charged with providing reporting and basic analytics on more data than you know how to handle - and you need to do it without the combined resources of your IT department being placed at your disposal. Sounds familiar?

Let's use Point of Sale data as an example as POS data can easily generates more data-volume than the ERP system. The data is simple and easily organized in conventional relational database tables - you have a number of "facts" (sales-revenue, sales-units, inventory, etc.) defined by product, store and day going back a few years and then some additional information about products, stores and time stored in master ("dimension") tables,

The problem is that you have thousands of stores, thousands of products and hundreds (if not thousands) of days - this can very quickly feel like "big data". Use the right tools and my rough benchmarks suggests you can not only handle the data but see a huge increase in speed.

Business Analytics - The Right Tools For The Job

Whether your analytic tool of choice is Excel or R or Access or SQL Server or ... whatever, if you've worked a reasonable range of analytic problems I will guarantee that at some point you have tried to make your preferred tool do a job it is not intended for or that it is ill-suited for. The end result is an error-prone, maintenance nightmare and there is a better way.

Business Analytics - The Worst Use of Excel ever ?

Excel is a great tool and I use it a lot. It's available on almost every business user's desktop and it's highly extensible (with some sensible design) through add-ins and programming but it can't do everything; push it too far and the results can be nasty.

Here are my nominations for "The Worst Use of Excel ever" awards.

10 resolutions for Better Business Analytics

Firstly - thank you Santa for reading my Christmas list. I love the T-shirt - "Statistics means never having to say you're certain". With the holiday season coming to a close my thoughts are turning to the New Year and even a certain excitement about getting back to work. Time for some new year's resolutions !

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