While people talk about analytics in general, there are really two distinct varieties: human analytics and inhuman analytics. First, there is analytics for and by humans, i.e., numbers, tables and graphs designed by humans for human consumption and consideration. Second, there is algorithmic analytics, originally designed by humans but then set off to make observations, decisions and perhaps actions on its own. I dub this "inhuman analytics," because that's what it is. It is incredibly important to understand the differences between these two things, related in name but little else.
Human Analytics
When most people think about analytics, they're usually thinking about things like Data Warehouse (DW), Online Analytic Processing (OLAP), Business Intelligence (BI), and related subjects.
This is a subject that is broad and deep, with many products and vendors that have evolved over time. But there is a simple unifying theme: these are tools intended to provide information to people, often in the form of graphics, so that those people can understand what's going on and take any action that may be appropriate.
Oracle, for example, has a wide variety of such tools:
Microsoft also has a variety of such tools.
Note that both companies illustrate their approach using screens and people. That's what this type of analytics is all about.
There are a wide variety of BI tools from many vendors, in addition to open source.
Inhuman Analytics
Inhuman analytics, a terms that no one else uses, so far as I am aware, is a whole different thing. This is also a subject that is broad and deep and undergoing constant innovation. It includes such diverse subjects as machine learning (ML), advanced statistics, operations research (OR) and related subjects.
In general, inhuman analytics are far more specialized than human analytics. They are nearly impossible for anyone but a specialist to understand. There is often lots of math involved. They are not primarily about presenting information so that it makes sense to human beings -- they are about figuring stuff out that most humans wouldn't be able to figure out at all, or figure it out with a precision that exceeds human capability.
Because of this, there aren't great pictures to illustrate inhuman analytics. But here's an illustration of the ML process from one company's ML toolkit:
Inhuman analytics are behind a large number of modern innovations, though they rarely get credit for it, since the way they work is essentially like magic to most people This is a vibrant subject with a rich history. I suspect I will come back to this in some future post.
Conclusion
Human analytics has many uses and is a good thing. The visual tools it emphasizes enables knowledgeable and motivated people to explore and understand a data set, and to track it over time. Sometimes you can even discover new things, particularly in the early stages of understanding and optimization
However, inhuman analytics are the serious, heavy-duty tools to help derive value from data. They can and regularlly do figure things out and solve problems that are beyond human capability, even with the aid of human analytics.
Human analytics has its place. But it's no substitute for inhuman analytics for serious value creation.
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