Monina asks, "What tools are useful to help marketers dig deep into their organization's data?"
The answer to this question depends on the level of skill a marketer has in data science, specifically the technical and statistical skillsets. I'd put the available tools in categories of beginner, intermediate, and advanced. Beginner tools help marketers extract and report on the data itself. Intermediate tools help marketers start to understand patterns and relationships in the data. Advanced tools help marketers manipulate, transform, and distill the data.
- Beginner: Spreadsheets, Google Data Studio, the various data sources
- Intermediate: IBM Watson Studio, Tableau Software, IBM Cognos
- Advanced: R, Python, SQL, Scala, Spark, Neo4J
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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 menina asks what tools are useful to help marketers dig deep into the organization's data? So, tools are part and parcel of the data science world.
And if you remember, if you recall, data sciences, four major skill sets, business skills, scientific skills, technical skills and mathematical skills, the tools that you use at each in each of those domains are dependent on your skill level, especially the technical and statistical tools.
The answer to this question really depends on your level of skill as a marketer.
What do you bring to the table? What are you comfortable with, that you can use to gain value remember a tool is nothing more than a tool is literally a tool by itself.
It does nothing.
If you You haven't laying around, it won't do the work itself, no matter what the vendor promises you It's never happened.
So, like a frying pan.
If you can't cook a frying pan, no matter how nice it is, or how expensive it is, is not going to help you, you got to have the skills to back it up.
So I would put the tools question in three categories, beginner, intermediate and advanced.
And beginners tools for marketing data science, are specifically about helping marketers report on the data they already have.
And extract data and maybe do a little bit of visualization.
So let's um, let's actually look at these.
So bring this up here.
So beginner tools, again, this is all about extraction of data, right and, and basic manipulation of data.
So you have things like Google Data Studio, fantastic tool for getting data from a couple different data sources.
And just looking at it right just being able to put together very simple dashboards, Microsoft Excel, the venerable spreadsheet is one of the most popular applications in business for a reason.
It's enough that for people to understand the basics of data analysis, and you can actually do quite a lot with Excel or the spreadsheet application of your of your choice if you use Google Sheets, for example.
And of course, the platforms themselves Google Analytics, Facebook analytics, Twitter analytics, your marketing, automation analytics, all the platforms have their own basic analytics built in.
And those are the data sources that in many cases you are going to be pulling data from.
So those would be the sort of the beginner level tools.
If we move up one level, intermediate tools, help marketers start to understand patterns and relationships with the data.
You start manipulating the data, you start putting multiple datasets together or multiple variables together to see the relationships between things to try and dig in and gain some insights like why did something happen and for This you have intermediate tools like IBM Watson Studio, and IBM Cognos.
Both of those are fantastic tools.
I prefer Watson Studio because it has the ability to also scale up to an advanced tool but certainly for with things like the SPSS visual model are built in.
It's a really powerful tool for helping you manipulate and transform your data and, and do some advanced statistical operations, some more sophisticated statistical operations, and of course, Tableau software.
Tableau is the market leader in visualization.
For reason, it is fantastic software, not too hard to get started with.
But you can do some really amazing advanced stuff with it.
So I would classify those as the intermediate tools for marketing data science.
And then for advanced tools.
advanced tools really are about helping you manipulate your data, transform it, distill it down, run advanced algorithms against it.
If you've seen me talk about machine learning and artificial intelligence.
These are some of the The tools that you'll use to get into that level of analysis where you want to understand what caused something, you want to see hidden relationships in your data, you want to use AI to distill down the data into just the parts that matter.
The two big ones here, of course, are are in Python, two programming languages.
And then the ancillary tools that you need to be able to extract data at advanced levels, things like, you know, command shells and being able to work with API is natively at the sort of the bare metal version of your computer.
And so these tools really are sort of the the highest level of of data science in terms of a tool perspective that you're going to be doing the technical and statistical stuff with.
There's no right or wrong answer and There are tons and tons of tools and vendors that I haven't mentioned here.
These are the ones that I've used and can speak to.
And I've used them recently and can speak to their effectiveness at what it is that they do.
There are tools and vendors out there for every level of skill and every budget.
So keep that in mind.
These are not just the right answers.
These are the ones that again, I've used and I've seen other people use very, very recently.
And other business partners use particularly on the on the beginner side, you know, people should be using Data Studio and it's okay to be using spreadsheets in the beginning as as a beginner, that's a great place to get comfortable.
So if you are uncomfortable with data science, nothing wrong with firing up your spreadsheet and just trying techniques out you can do again a lot of things moving averages, Interquartile ranges, all these statistical techniques, you can do within a spreadsheet as well.
So it's a good way to get your feet wet.
As you progress in your skills as you progress in your growth as a marketer, and as a data scientist, you will naturally run into situations where you're like, the tool I'm using right now just doesn't cut it, I can't do what I want to do in this.
And that's when you know, it's time to move up to that next level, when you when you start to get really frustrated, and you're like, ah, if only this thing did this, then it's time to start looking at Okay, what are some, some more solutions that are available? And these are not hard and fast rules.
You may become so fluent in the statistical stuff or in the programming stuff, even in something like you know, Visual Basic and excel that you might just leap straight to Python and you might just leap straight to our and bypass that intermediate stage.
Again, there's no right or wrong answer except that if a set of tools is starting to hinder your growth, then it's time to move up.
That's really the only guideline that can give their so these tools useful.
They need training So make sure that as you buy tools you also by training and by time for training, because you need to skill up on these things.
And the training part is more important than the tool part.
You can learn statistics, and never really use more than the spreadsheet.
Or you can buy, you know, Tableau software, and never pick it up and never use it.
That's a waste of money, you will always do better training yourself first, and then buying the tools later.
So keep that in mind.
But good question.
It's an important question because a lot of people have questions about what tools and it's really about what's in the the big computer up here first, so if you have follow up comments, please leave them in the comments box below.
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