Computational Overload, Single Source of Truth, and Making Sense Out of it All

The all-too-common phrase, “single source of truth,” is often thought of as the gold standard of data analytics, but in reality, the truth of analytics is often a moving target with multiple sources that are often unconnected, frequently outdated and out of your control.

How do CPG manufacturers and retailers analyze potentially thousands of SKUs across multiple brick-and-mortar and online retail outlets? It’s both an art and a science — and it requires going beyond traditional methods and tools, and being just a little bit obsessive about the behind-the-scenes technology. When we look under the hood and understand not only what’s on the surface, but how the technology and the hidden data works, it allows us to make decisions quicker, better and faster.

That type of analysis calls in many different sources of data, from ad analytics, real-time POS information and even obscure external information such as weather patterns that may impact shipping, unexpected influencer marketing and supply chain challenges. Getting a single snapshot of the “truth,” static and frozen in time, is impossible, and attempts to do so often result in misleading results. Attempting to capture that snapshot will only lead you down a rabbit hole of endless and often meaningless distractions that are obsolete by the time you see them.

In the world of Amazon in particular, retailers and CPG manufacturers often miss the mark when it comes to understanding analytics, focusing mostly on surface details while overlooking what’s going on behind the scenes. It may seem as though some of the Amazon data does not match your own, and those inconsistencies may lead to missed opportunities. Understanding what lies underneath, however, will help you make sense of it all, and may even yield a competitive advantage.


What Lies Underneath the Surface?

It’s easy to miss out. What the customer sees on your Amazon catalog only touches the surface, and consists mostly of marketing language, keywords and descriptions. But what lies underneath the surface? Naturally, you want to understand the data that’s driving sales and make use of it, but which data? Often you will find, whether it’s on Amazon or elsewhere, that there is no single source of truth, and much as database analysts would like to make you think otherwise, it’s not always possible for that single source to even exist.

Making Sense of it All

In analyzing the patterns of sales on Amazon, for example, there are countless backend activities that are actually within your control. The “computational overload” one may initially face may actually be beneficial in the end, if it’s approached right — and with the right tools to make sense of it all.

That computational overload, often warned against by CIOs and retail analysts, can often yield a competitive advantage. Think of it as a well-tended garden filled with delicious vegetables versus a field of crabgrass and weeds. It really has little to do with what’s on the surface of the soil (or in the case of data analytics, what’s on the surface of retail patterns), but more about the roots and what’s going on underneath.

Once accomplished, the catalog data between multiple outlets can become more consistent, and it becomes possible to create a normalized product catalog — allowing you to better understand the relationship between each item and each item’s different channels, the relationship between those items and consistency within the channels.

Single Source of Truth? Try Four Sources of Truth

The “single source of truth” myth is pervasive throughout business, industry and technology, but in reality it’s seldom possible to achieve — and even if it were, it may not even be a desirable goal, as “truth” as a static, frozen-in-time snapshot does not really capture the whole picture.

Success will hinge on understanding all sources of truth — and resolving how those sources may even differ from your own information. When dealing with an Amazon catalog, it’s always a little deceptive. It’s not possible to issue a simple query and get good data, and in fact there are multiple sources of truth to understand. Navigating to the “Seller Central” location will show four areas (bulk, cost, HTML and operational). The most obvious, the HTML version, is only one source, and this consists of the front page and metacode, but don’t be deceived into thinking that this is anywhere near the complete picture.

The “bulk” download area rarely matches the HTML and may be off by multiple SKUs, and often with fields in the wrong places, so it is essential to match those up automatically and frequently.

But when clicking the “cost” icon you can see how Amazon is not always consistent between the HTML on the item catalog and the downloaded items catalog. Further, the operational catalog may even contain outdated fields, which may cause some further inconsistencies.

Lastly, the “operational” catalog may even include outdated fields that are no longer relevant, and an analysis of this area may yield important information about why orders may not be matching up.

These seeming inconsistencies do not mean that Amazon means to confuse you; on the contrary, this wealth of information can yield a tremendous advantage to those who understand it, know how to normalize it and are able to put it together with an automated system to make the best use of it.

Now That You Understand the Data, What can you do with it?

The level of analytical detail, which may be incorrectly misnamed as “overload,” must be paired with detailed levers and automated execution, however, to make sense of it and to make it actionable. There is power in the quant behind your ecommerce site, behind the keywords, the language and the surface layer — and yes, it is possible to understand what goes on behind the scenes on your Amazon site, your Walmart portal and other online outlets, how they interrelate and interact, and to turn this vast confluence of data and complexity into the biggest advantage a retailer could ever hope for.

With a system of real-time updates and an alert-based, actionable interface with autonomous updates, you can achieve a real balance, avoid computational overload, and dive deep and search for important hidden trends and gain a true competitive advantage. 

Meagan Bowman is Founder and CEO at Stonehenge Technology Labs. A game-changing SaaS subscription platform, Stonehenge’s STOPWATCH solution serves brands, manufacturers and their elected agency partners of all shapes and sizes.  The channel-agnostic decision science delivered through STOPWATCHs proprietary middle layer directs online-assisted, augmented and autonomous actions to be executed by CPG teams, to profitably grow their online sales distribution through all click-based retail transactions.

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