Better Business Analytics: 2013

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.

I've done a lot of supply chain network design projects and consider myself to be an expert. Had I had this book from the start, I may have got to expert status a lot faster.

With experience in supply-chain and an academic background that includes mathematical-optimization, when the need arose to build supply chain network optimization models I just did it. Then I learned many, valuable, real-world lessons the hard way- by getting it wrong.

There are a number of books available that cover this area: I have dipped into a few, as needed, and I have not read most of them so I really can't say this is the best book available on the subject. I can say this is one of the very few analytic books on any subject that I have read cover to cover.

Ignore SNAP and your product may not be on the shelf when it's most needed - and that means lost sales.

SNAP is the “Supplemental Nutrition Assistance Program” (formerly known as “Food Stamps”) in the United States which puts food on the table for 46 million people every month.

SNAP can drive big spikes in sales at the store. These spikes are large but short-lived and often pass undetected by reporting and forecasting systems.

Our whitepaper covers the causes of SNAP spikes, why they vary so much across regions and products, how to identify sales spikes and what you should be doing to maximize sales.

Download it now or visit our website for more information.