Data Analytics — Simply Explained
If you want to do business in the new digital economy, you have to deal with data in some way. But are you harnessing its full potential? Here you will find an explanation of what data analysis is, what you can use it for — and how to proceed.
It has been said that the world's most valuable resource is no longer oil, but data. What is certain, in any case, is that they have one key feature in common: Both need to be refined. Crude data, like crude oil, only really comes into its own once we've processed it.
Data analysis is about just that: oh treat, interpreting and presenting (often large amounts of) data -- thus drawing conclusions.
Common types of data
Companies that today have a digital presence are sitting on huge amounts of data. They may use some, much will be left behind — and even more they probably don't know they have access to. For example, a typical Increo solution, an online store, has data on
- what channels their customers come from
- how your customers use the site — that is, exactly how the customer journey looks
- conversion rate and churn rate What leads to sales and where does it cost?
- advertising and campaigns in different channels — how well do they break through the noise?
- ranking in Google search
In addition, there are solid sales figures (which products work and don't?) and industry- and company-specific figures.
What can you use it for?
Here, of course, comes the big question: What are we going to do with all this? The short answer is that what you want to get out the other end, output, is useful insights. So not only interesting and clever numbers, but insights that give you a a better basis for decision making.
For example, there may be information about which campaigns work (how much value do you get per penny on the different surfaces?) or about visibility (is it easy for our target audience to find us?). This kind of insight allows you to take action Precise Measures rather than shooting in the blind.
Thanks to tools such as Google Analytics and Facebook's own dashboard, this has become much easier, and “anyone” can today perform basic data analysis. But if you go a step further, there's more value to be gained.
Dennis Janszo, Developer at Increo
When 1+1 = 3
It really comes in handy when you combine several different sets of data. For example, traffic/volume figures become far more useful if you view them in the context of conversion figures.
But, of course, it does not stop there. If you additionally connect both SEO data and the crown value of the various conversions, you can begin to estimate the value of increasing your position in a Google search. These kinds of long inferences require a more trained hand — but are all the more useful for making the most optimal choices possible.
A mini guide to data analysis
Whether you're an experienced data analyst, or just want to dip your toes in, here's a little how-to:
1. Map your data
Do you really know how much data you have available? The challenge here is to find every conceivable and unimaginable source of data — especially if you use a lot of third-party sources. Remember that the more sources and a more holistic picture you have, the longer conclusions you can draw.
2. Make some hypotheses
Diving into a sea of data without a plan often ends in high time consumption and little yield. You may Of course hit flashes too when shooting in the blind, but we recommend that you start by making some clear, defined targets. Here it may be helpful to talk to someone who has done it before. If you are going to get good answers, it is important to know what you should ask.
3. Collect and clean
Cleaning and washing: It's a bummer, but necessary. Much of the data you find is probably both incomplete and meaningless on its own. It may also need to be processed or transformed before it can be used. It is at this step that it often stops for those who try to do everything without help.
The easiest method is to import data from Analytics and make your own measurements in Excel. It's spartan, but it works. However, if you want to get the most out of your data, we need to go up a notch, and in services like Microsoft's Azure, you can create models and pipelines that provide a new world of insights.
4. Visualize!
Now begins the funniest part. If you have received good help from your digital partner to put the data into the system, you can start interpreting and visualizing. There are great off-the-shelf solutions like Google Data Studio and Microsoft PowerBI. Are both your hypotheses and models sound, you can now get clear answers. Did the assumptions vote? Maybe the data shows something you couldn't even imagine?
And yes, if you truly are serious about creating data-driven decision making, this is of course where you connect AI and machine learning to make predictions based on historical data.
Why all this?
Using data is basically about taking faster and smarter decisions. There is no need to sigh when you sit on the answers that make you let go. And if you use these analyzes manually today, with the right tools you can save a lot of time and hassle by automating the processes.
Do you take data seriously and manage to make it useful? insights, it becomes a resource that makes you both faster and more agile. Yeah, a bit like oil.
