Opinionated analytics tools provide easier learning curve, more consistency

By Ariane Bernard


New York, Paris


The reason I discuss opinionated and un-opinionated analytics tools in my recent newsletter and blog is that analytics products come in un-opinionated and opinionated flavours.

The big analytics suites like Google Analytics are un-opinionated. They cover a whole range of possible users, companies, and goals that you may be trying to serve by heading into your Google Analytics suite. 

So we are not going to be talking about un-opinionated analytics products here because they don’t have an inherent world view. How they are configured, the kind of views they give of data, is very much decided by the organisation that puts it in place or the individual user putting together a report.

Google Analytics is an example of an un-opinionated analytics product.
Google Analytics is an example of an un-opinionated analytics product.

You decide whether you want to see a number, a table of numbers, a trend line, a chart, a bar chart. You decide what constitutes a flow or a conversion. You’ll be tagging things by hand a lot on your site to support how you may want to understand your Web site usage. The analytics suite is just there to give you access to the data it may have — no assumptions of usage or relevance is built-in out of the box.

What does it mean for the storytelling point that our American data executive was making? It means that when we use un-opinionated analytics tools, we are going to be the ones to provide the narrative structure for contextualising the numbers we pull. There may be “bad data” or “misleading data” if we don’t understand the context for our own numbers.

But we are left to construct the frame of reference for the numbers — and, usually, we can provide these because we know our businesses. We know what KPIs to use as baselines and as worth tracking. We know what period of comparisons are meaningful for us. But we will have to work hard at it. And, importantly, this may fall quite a bit short of the kind of usefulness we may want to help our business.

Meanwhile, we have a whole other class in the opinionated analytics product category. And while these can provide a more friendly approach for the users of the suite — particularly for non-data folks — we have to remember that opinionated analytics tools essentially provide the narrative built-in. They come with forceful views of what numbers to pay attention to and for what purpose. They also make various assumptions about referentials (we will look at this later).

Opinionated data tools can be more user-friendly and come with the narrative built in.
Opinionated data tools can be more user-friendly and come with the narrative built in.

This is great to democratise data. This is also great when you consider that people go from one company to another and that having some consistency in how everyday tools work makes for more portable knowledge. Someone who goes from Company A to Company B will have to deeply learn the specific Google Analytics of their new company. Not GA itself, but everything in how GA was rigged, making your GA and my GA quite different.

There are also ways to opinionate (not a word) your un-opinionated analytics: Google Data Studio is basically an opinion layer on top of your un-opinionated pile of data. They provide the narrative that data analysts at your company think are useful to understand your data in the context of what is most useful for your business.

Notice one thing: Google Data Studio is a selected view of your data. It’s not all the data. This is where opinionated tools often do their best work: subtraction. 

But for their ease of use and clarity of purpose, opinionated analytics tools also have areas of opacity. Think: ways in which the product proposes a very specific read of your data, a specific baseline, assuming that it’s right for you at all times. 

The most data-literate in your organisation (usually data analysts) will usually remember these caveats when they are using this type of tools. And they will be able to see when you are hitting the edge cases of this type of tool. But — and this is the paradox of these tools that are usually oriented at a broader user groups in the organisation — the more casual user is not going to necessarily detect when the opinions of the tools have led to these edge cases in the first place. 

So, yes, the opinionated analytic tool came with much clearer context and storytelling for you. But on the other hand, its strong opinions can make edge cases harder to spot. 

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About Ariane Bernard

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