April 13, 2017

Analyzing the right data

0. A huge fraction of what’s important in analytics amounts to making sure that you are analyzing the right data. To a large extent, “the right data” means “the right subset of your data”.

1. In line with that theme:

2. Business intelligence interfaces today don’t look that different from what we had in the 1980s or 1990s. The biggest visible* changes, in my opinion, have been in the realm of better drilldown, ala QlikView and then Tableau. Drilldown, of course, is the main UI for business analysts and end users to subset data themselves.

*I used the word “visible” on purpose. The advances at the back end have been enormous, and much of that redounds to the benefit of BI.

3. I wrote 2 1/2 years ago that sophisticated predictive modeling commonly fit the template:

That continues to be tough work. Attempts to productize shortcuts have not caught fire.

4. In an example of the previous point, anomaly management technology can, in theory, help shortcut any type of analytics, in that it tries to identify what parts of your data to focus on (and why). But it’s in its early days; none of the approaches to general anomaly management has gained much traction.

5. Marketers have vast amounts of information about us. It starts with every credit card transaction line item and a whole lot of web clicks. But it’s not clear how many of those (10s of) thousands of columns of data they actually use.

6. In some cases, the “right” amount of data to use may actually be tiny. Indeed, some statisticians claim that fewer than 10 data points may be enough to get a good model. I’m skeptical, at least as to the practical significance of such extreme figures. But on the more plausible side — if you’re hunting bad guys, it may not take very many separate facts before you have good evidence of collusion or fraud.

Internet fraud excepted, of course. Identifying that usually involves sifting through a lot of log entries.

7. All the needle-hunting in the world won’t help you unless what you seek is in the haystack somewhere.

8. Google is famously in the camp that there’s no such thing as too much data to analyze. For example, it famously uses >500 “signals” in judging the quality of potential search results. I don’t know how many separate data sources those signals are informed by, but surely there are a lot.

9. Few predictive modeling users demonstrate a need for vast data scaling. My support for that claim is a lot of anecdata. In particular:

10. Somewhere in this post — i.e. right here :) — let’s acknowledge that the right data to analyze may not be exactly what was initially stored. Data munging/wrangling/cleaning/preparation is often a big deal. Complicated forms of derived data can be important too.

11. Let’s also mention data marts. Basically, data marts subset and copy data, because the data will be easier to analyze in its copied form, or because they want to separate workloads between the original and copied data store.

But notwithstanding the foregoing:

12. So what does this all suggest going forward? I believe:

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