Nobody is immune, not even official statistics bodies. Although it's wise to correct mistakes and sharing a revised figure that improves a country's economic outlook is a good thing, when the top authority on national statistics has to correct its data yet again, it only means one thing: something is wrong with the data.
When we talk about the importance of having good data sources and clear, organised analytics, we mean it seriously. Your data not being 100% reliable may not affect a country's GDP, but it will certainly affect your business (much more than you think).
If you want to avoid unpleasant surprises about your sales, your customers or any metric that matters to your business, it all comes down to putting your data in order and cleaning it. Yes, cleaning it. What in English is known as data cleansing is the key to ensuring your analytics are reliable. And that's what we're going to talk about in this article.
Get your notebook out (and even your broom) and prepare to thoroughly clean your data. In addition to the importance of data cleansing, we'll also explain the first steps you need to follow to carry it out correctly.
Its name says it all: data cleansing is the data cleaning process that any company or institution carries out to organise its data and ensure its reliability. This process involves eliminating incorrect, duplicated, outdated or incomplete data.
This type of data is known as dirty data. Data that, for reasons as varied as poor integration of data sources or incorrect visualisation, is practically useless. Except for one thing: getting in the way of your work.
Incorrect or inaccurate data may seem completely harmless, but behind that facade of uselessness lurks a major risk for your company. Dirty data can destroy your business. Literally.
Think of it this way: if your data is incorrect and all your analytics are based on information that doesn't reflect reality, your business decisions will never be the right ones. In fact, this data can push you into designing strategies and actions that go directly against what's best for your business.
Against all odds, having erroneous or unreliable data is actually quite common. Especially when we consider that companies now have more and more sources of information and data that overlap with each other. Sometimes information is not power.
That's why it's so important to put data cleansing into practice regardless of your business and your relationship with data. Whatever your metrics or your dependence on data, it's always better to know if something is wrong. It's always better to know so you can react in time:
What if what your business understands as a conversion is not what your data source is interpreting as one? Imagine tracking website visits instead of final purchases. It sounds unlikely, but trust us: you wouldn't be the first person it's happened to.
One of the main benefits of data cleansing is clarity about which metrics are being considered and how they are being calculated. It's very common to use different tools (marketing tools, for example) and for each of them to calculate things in their own way. Making sure they align is essential.
Knowledge is power if you know what to do with the information. Often the truth isn't as pleasant as we'd like, but it's always the best starting point for turning things around and improving them.
Thanks to data cleansing you can identify your weak points both in data collection and in your business. Having visibility into the data that's failing also lets you fix it and understand the true reality. Even if it hurts, it's always better to know.
With the data on the table it's easier to define a roadmap. But above all, it's easier to define a roadmap that actually works and benefits your business.
Thanks to data cleansing you can redefine your priorities (prioritising retention over acquisition if you see that users aren't coming back, for example) and make more strategic decisions based on reliable data.
The question is: where do you begin? Knowing how to start scrubbing and organising your data, and even assessing whether you actually need to carry out this process, isn't easy. We're talking about an in-depth analysis process that involves many variables.
As always, everything will vary depending on each case. Your data sources, the tools you use, the metrics you track in your business… All of these will be decisive when it comes to putting your analytics in order. But despite this, there are steps you will need to follow regardless:
The first step is, without question, the most important of all. A step that involves doing a comprehensive scan of all your data and data sources. The data audit involves reviewing absolutely all your information to identify the dirty data we mentioned earlier: duplicated, incorrect or incomplete data.
This part of the process may be the most complex and exhausting. It's a retrospective and in-depth analysis that will involve reviewing the reliability of the tools you use, cross-checking data from each one, and validating that the data you're seeing matches reality.
Once you've evaluated the quality of your information, your first decision awaits: which data to keep. You may find different ways of measuring an important data point, and even though both may be valid, you'll need to settle on one option.
This process will help you reassess which metrics are important for your business and which ones you've been overlooking all this time. You may discover new data points you didn't know about that are vital for your business. You never know!
Now it's time to get cleaning. With the data on the table and your priorities clear, it's time to start discarding information that adds absolutely no value to your business. In addition to eliminating the data and metrics you don't need, you'll also need to filter out duplicated ones or those not being measured correctly.
Removing data means recalculating your results. If you remove the information that was distorting reality, you may find that the picture changes (and considerably). Part of data cleansing also involves redefining what you previously took for granted or believed to be completely real.
But it doesn't end there! In fact, now the most important question arises: how do I know my new analytics are reliable? Nobody wants to make the same mistake they were trying to fix. That's why it's important to validate the integrity of the new data.
After defining new analytics you'll need to make sure they work. This part of the process may involve waits (data takes time, you know) and errors (almost nothing works first time). The important thing is to always keep a close eye on whether the new setup is producing reliable data.
This final step is optional but highly recommended. If the advantages of data cleansing are clear, imagine automating the process and ensuring your data is always up to date and accurate.
If even official statistics bodies have to carry out data cleansing initiatives, it's clear that this is a matter of great importance for all companies, whatever their size. Clean, reliable and up-to-date data is indispensable for your business to run smoothly. That's no secret.
At Boost we know all about putting data in order. Yours and that of many other companies we've helped with data cleansing and data visualisation to bring clarity to their priorities and make more strategic decisions.
If you want to understand the state of your data and what you need to do to clean it up, reach out and we'll get to work.
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