Why 'Data' and 'A.I.' always go together

Data and AI A.I. and data.

You almost always hear the two terms spoken in the same breath. Why is that?

If you're a founder trying to learn more about these topics, whether it's to improve your workflows or your products or some aspect of your operations, here's the introduction to a business owner on what people mean when they insist on saying the two together.

A.I. needs data to do anything.

At its core, A.I. is an algorithm, which in plain language is a process that takes inputs and produces outputs. Just like your car, which is just a piece of metal that sits in the garage until it has fuel to run it, an algorithm alone with no data to process cannot do anything useful. In fact, he can't do anything at all.

This means that if you want your business to benefit from AI, the first task is to gather and shape your data. This can be a real stumbling block, according to Phong Nguyen, founder of data science consultancy Partners in Company. "According to clients we've worked with and spoken to, the barriers to greater data orientation are usually the basics of having clean, consistent data and having it centralized and secure," says -her.

This usually means either pulling your data from spreadsheets or bringing your data together from multiple platforms, such as a customer relationship management (CRM) platform and a marketing form, in a centralized repository, where the data can begin to be combined and compared for analysis. Typically, it will still need to be cleaned up and normalized in various ways to make sure it's consistent and in the right form before data teams can draw the correct conclusions and then build on the data with the AI.

Also, most A.I. needs large amounts of data to produce reliable results, for the same reason you need a large sample of anything to make a judgment within reason. We're all familiar with political polls, where professionals typically claim greater than 95% accuracy on how the population as a whole plans to vote in an election by sampling around 300 people.

This is for a simple choice between two options. If you're trying to create more complex predictions, such as differentiating between types of customer behavior in your marketing data, you'll want to start with several thousand samples. Often you will use a lot more to have great confidence in your results.

How much data are we talking about? Proper statistical analysis can give you an accurate number for what you're trying to do, but as a rule of thumb, hundreds of thousands of rows are usually inferior to machine learning based analyses. "I'm not used to working with anything less than a million rows," says Chantel Perry, a veteran data scientist at large companies and author of the book Data Newbie to Guru.

And for something like marketing analytics, where the customer trends you're trying to understand may vary from day to day and month to month, you want some also enough to collect data over a long enough period to make useful predictions: "You want to be in business for at least six months and collect data on your customers for at least six months," says Perry.

So now you understand why A.I. needs data. This dependence also goes the other way. The truth is, you can't have one without the other.

A lot of data comes out of A.I.

Just like A.I. algorithms need data as input, their output is often some form of data.

Suppose your marketing data is analyzed in such a way that you find that you have eight major customer groups. You may also find that different customer groups should receive different types of pitches or advertisements. Those ...