With the influx of analytics, metrics, and stats, we all can benefit by improving our data analysis skills. This doesn’t only mean being fluent in reading graphs and interpreting numbers; good data analysis skills enable you to apply the right data to the right problems or projects, thus turning information into recommendations, strategies, and knowledge.
Experts are always raving about the importance of collecting data, but if you don’t have the skills to read data, the data is ultimately useless. Don’t let valuable information go to waste. Start improving your data analysis skills today.
Measure the Right Data:
When you have data, it’s easy to throw everything onto a graph and compare it all as is. You might be able to see all the numbers in one place, but you won’t garner much insight if you do it that way.
For example, you want to measure the engagement rates of your marketing channels. You gather up your data and you plop it on a graph. You get something that looks like this:
But it doesn’t really give you a clear perspective, does it? That’s because you’ve failed recognize the different data. It’s like comparing apples and oranges, or in this case, fruits and stores.
Let’s say fruits equal search engines, social media, email, etc., while stores represent desktop, tablets, and smartphones.
As you can see from the graph above, the highest engagement is coming from smartphones, which is good insight. However, within the graph, it confuses matters because users are most likely using social media, search engine, and emails via smartphones. Going on with my analogy, you don’t know which fruits the customers are buying from which store, all you know is which fruit and which store is most popular. The data is, at best, limited.
The ability to segment data and ask whether or not each set of data is on the same rung of relevance is an important step in improving your data analysis skills. This issue is often resolved when you have a defined problem to help solve.
Practice the Art of Application:
After everything you learn through reading, watching, or listening, you must apply it to something specific.
The better you get at recognizing what is applicable and what isn’t will give you confidence when handling data.
The key is to be specific. Pick one problem.
For example, let’s say you want to determine how much you want to spend on your next marketing campaign. How will you do that with data? By learning about your customers, of course. How much you should spend on marketing should be related to how much each new customer is worth.
Organize and Layout Data:
The culmination of data can result in a jumbled mess of information. To properly analyze it, identify patterns, and communicate the figures to others on the team, you’ll need to make it presentable. The way you present the data will have a great impact while identifying value.
If you are measuring the success of your website’s traffic, you might pull out Google Analytics for everyone to see at the board meeting. But how should you present it? Daily? Weekly? Monthly? Annually?
It’s easy to exaggerate results or highlight minor failures and successes if you misrepresent graphs. The daily fluctuation of your website makes it hard for you to spot trends. Yes, you may be able to identify when the product launch occurred or compare sales on Black Friday to Cyber Monday, but there isn’t a whole lot it can recommend.
However, if you zoom out and view your website’s analytics as charted monthly or annually, you’ll be able to see the gradual increase or drop in traffic. This helps identify areas of concerns that were not visible with a narrow vision.
For example, in the graph above, you may be able to see that your website’s traffic suffered a dip from July to October and then rose. What happened there and how can you prevent that summer drop from happening again this coming year?
Remember that data is a tool to help you communicate an idea. Keep it simple, and at all cost, avoid presenting exaggerations.
Know When You Need More Data:
Now that you’ve got the basics, you want to start analyzing and improve your business. Hold on, though, because another quality of skilled data analysts is that they are able to see when they have an insufficient amount of data.
Let’s say you are implementing a marketing campaign that includes paid ads on Facebook, Twitter, and Google Adwords. Three days in and you decide to look at the analytics.
Comparing conversion rate (number of people who click on the ad and then went on to make a purchase) to click through rate (number of people who has seen the ad divided by the number of people who click on it), you can see that Facebook is by far out-performing the other two channels.
Since these marketing programs tend to charge businesses per click or per impression, you want to be sure that those who see or click end up converting.
With a limited amount of data, it’s easy to give up on Twitter and Google Adwords, but that might be a mistake since impressions, clicks, and conversions fluctuate. Also, Facebook, Twitter, and Google Adwords have different algorithms for targeting your specific audience. Adwords uses keywords and Facebook targets demographics—which is a whole other thing you’ll need to test to get any valid results.
To draw a conclusion with limited data is something you need to avoid doing, even when you are eager to analyze. A data analysis skill is being patient.