Andrea asks, “What is your go-to strategy to analyze your business data?”
The exploratory data analysis process is a very straightforward, proven one:
– Goal and strategy
– Data collection
– Attribute classification – dimensions and metrics, data types
– Univariate analysis
– Multivariate analysis
– Data quality analysis – missing, NaN, inf, -inf
– Anomaly and breakout analysis
– Data requirement verification
– Data preparation – centering, scaling, etc.
– Feature extraction
– Feature augmentation
– Feature selection
– Other applications
This process is one that you cannot shortcut if you you want a good outcome.
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What follows is an AI-generated transcript. The transcript may contain errors and is not a substitute for watching the video.
In today’s episode, Andrea asks, What is your go to strategy to analyze your business data? That strategy is generally the same.
It’s a defined process, regardless of what kind of business data you’re looking at marketing data, sales, data, finance data, and so on and so forth.
And it is a many, many, many step process that in the data science world is called exploratory data analysis.
And what that means is exactly what it sounds like, explore the data to find out what’s in the box, like, what are the things that are in this data set that we need to know about in order to make decisions about its quality? Can we use this thing? Can is the data any good? and so on and so forth? So let’s look at those steps.
What is your goal and strategy for the data? And this is specific to the the data itself? Do you have a data strategy around the state? Do you know what you’re going to use it for? Do you know why you have it? You know how you got it? That part is vital, because there’s this thing called lineage which is part of governance, that is essential for understanding where the data came from, and being able to in an audit document where you got it from, and and that it was, it maintains integrity throughout your pipeline.
Second step, of course, is data collection got to get the data from somewhere.
The third step is attribute classification.
What have you got in the state of what are the dimensions? What are the metrics? What is the categorical continuous variables? And what are the data types? If you think about something like a sequel database, the sequel database can have many, many different types of data, variable character line fields, numeric fields, integers, floating point fields, can have binary fields, large text blocks.
So what is what is the stuff right? What? What kind of data did you get? The next step is once you know what you have, doing what’s called unitary analysis, and that means when you’re doing basic statistics on the fields that you’ve gotten this data, so for numerical fields, what’s the average or the mean? What’s the median central tendency? first, second, third, core tiles? What are the the summaries of numeric fields for non numeric fields? What are the width of length of the fields? What are the types of fields that they are the number of counts, number of distinct entities, if you get a bunch of, for example, first names, how many times is the name Julie up here, within that, that field, so you have to do this university analysis and create and collect statistics about the different types of data that you have.
You may also at this point, do some sorting, just to get some a rough sense of what are the most prominent features.
The second step is multivariate analysis, where you start looking at relationships between fields.
For example, you have first names, you have last names, you have certain first names go with certain last names, you have birth date, to the certain years, months, days that have relationships to certain types of names.
So multivariate analysis, lets you understand the different types of relationships in a data set for something like, for example, commute, a consumer finance, you could look at, say complaints, what are the the the strength of the complaints? How long are they? And is there a relationship between the length of the complaint and the strength of the complaint to people who are angry or leave longer complaints, for example? So that’s a multivariate analysis.
After that, you have to do data quality analysis, What’s missing? What’s what things that have errors? What things have values that are outside the bounds of what you would expect? Do you need to do any kind of data preparation? This will help you answer that question.
After that comes anomaly in breakout analysis, which is, okay, say that they do have? Are there weird spikes in? Are there trends are there trends and changes, especially if there’s time involved a time series.
So not only breakout detection, very important, then you get to a really important part, which is called Data requirement verification.
In the golden strategy section, you set out what the data was for, why you have it, and how you’re going to use it.
And where it came from.
Data requirement verification, now you analyze the data set, you know what’s in the box, you have to very be very thorough in matching up, did we get what we needed? Or are there pieces missing are the pieces that do not meet the requirements of the project.
And this is the part that men, many, many folks skip over, I have skipped over it in the past, and I have paid the price for it by not being able to do the work that I needed to do.
So that’s super, super important.
The next step is, if as long as you’ve met the requirements, and the date is in good condition, you’re going to do a whole bunch of data preparation.
So centering and scaling the data if you need to removing anomalies, cleaning up the data essentially, after that, you do a step called feature extraction, where you pull out attributes from the data to make new types of data, especially if we’re going to be using this data for any kind of machine learning.
And feature extraction is is making new fields from existing fields.
So for example, if you have a date, that when a landline form is filled out, you can extract that the day of the week, the week of the month, the month of the year, the quarter of the year, and the day of the quarter, and so on so forth, you can create new features from that, because those features may be useful for modeling.
At this point, you may also choose to do feature augmentation.
feature augmentation occurs when you’ve done the requirements gathering, and you verified the data meets those requirements.
But you know, through your own domain expertise, there might be additional data from third parties that could supplement I could add new things.
Even though they’re not part of the original requirement, they could add value.
So you may choose to use augmentation, again, that requires a lot of domain expertise.
If you’re looking at, I don’t know, cardiac data.
What are the things that as a cardiologist you wouldn’t you know, other data exists out there, someone like me, who is not a medical professional would not know how to do that, I would know that there might be other data out there.
But it wouldn’t necessarily know what it is or where to go looking for it.
The next step is features selection, great, you’ve made this huge data set with thousand columns, right, and you’ve extracted and augmented this thing.
And it’s super huge and robust.
Now, you need to remove anything irrelevant.
So feature selection is what features are important.
If you’re doing any kind of supervised learning or looking at trying to analyze the specific outcome, say, like, you know, customer value, or their features that simply have no mathematical relationship to customer value, if so, you can drop those.
And finally, you would do either modeling or other applications of your data, where you’ve done the analysis, you understand this data left, right up, down.
Now, what are you going to do with it? Are you going to take it right from this point as insights and, and or generate insights from it manually? Or you’re going to turn it over to machines for further analysis? What are you going to do with the data? So that’s the strategy for analyzing business data is extensive.
It requires a lot of time, and it requires domain expertise.
So this is not something that you can do quickly.
Generally speaking, not if you want to do it well.
And it’s not something that you can do cheaply.
Again, not if you want to do it well.
Now if this is data, you student Hello, making a ridiculous like Instagram meme out of you don’t have to spend, you know, months to do that.
But if you’re going to be betting your business health, or your customers health or wealth on this data, you better do this exploratory data analysis process really, really well.
And that takes time and money.
So great question Andrea.
You have to do it as well as you possibly can within the limitations that you’re given for the data and for the overall business.
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