Taking care of your business goes beyond making sure your supply chain runs smoothly, your services are high quality and your customers are satisfied. All of that matters, yes. But there are less visible aspects you also need to address to ensure your business is sustainable.
One of those key, often overlooked aspects is the quality of your data. What we know as "dirty data" or "poor data" is a major obstacle for any business. And no matter how important it is, almost nobody seems to pay attention: 82% of data held by Spanish companies is incorrect or out of date.
Beyond the economic consequences of poor data quality (losses of more than 321 million euros per year), this problem goes further than just the bottom line. Poor data quality disconnects your business from reality and prevents you from making the decisions needed to ensure its success.
Your digital strategy depends on your data. And without quality data, there is no good strategy for your business. Making good decisions depends 100% on how you collect, integrate and analyse your data — so here is a list of the changes you absolutely must implement in your day-to-day.
Resilience is key to a future-proof business. Beyond sales, results or the number of customers your business has, there is one thing you must always keep in mind: an agile and flexible business will always be better placed to respond to changes in the environment. Without that flexibility, the future can get complicated.
Improving your data quality directly affects your business's preparedness and adaptability. With clear, useful and reliable data you can prepare your business for expected changes — and above all, for unexpected ones. Data quality helps you:
Make more strategic decisions faster – With quality data, your decisions stop being shots in the dark. You can react to changes and new opportunities more quickly.
Act with greater precision in your marketing campaigns – If your data accurately reflects reality, your marketing efforts will be more effective. You will know what is generating value for your business and invest with greater confidence.
Reduce costs from errors or duplications – When your data is properly integrated, it helps you spot things that do not add up. Duplicated data, inconsistencies, errors — you can fix them before it is too late.
Improve your customer experience – The more you know about your customers, the more you can personalise and optimise their experience. Quality data is key to identifying purchase signals and areas for improvement.
To bring order to your data and increase its quality, there are countless things you can do. As always, every company and business is different and will need to address different aspects of their analytics. But regardless of your business's challenges and data, there are a number of aspects you will definitely need to review. Here they are:
The way you collect your data is the foundation of your analytics. Your data sources, your attribution models, the metrics you track — everything starts with how you collect each data point, even before you stop to analyse it.
To improve data collection, there are 3 key questions you need to consider:
Data relevance – Avoid overkill. Choose carefully which data is truly important to your business and make sure those key metrics are correctly captured in your analytics. Focus your efforts on what really matters.
Source reliability – It is very likely that your data comes from different sources. Make sure you choose quality, trustworthy sources. The tools you select will be key to understanding whether your data is correct or not.
Collection model accuracy – There are multiple systems for collecting data. While some models rely on third-party data collection (search engines or apps), systems like server-side tracking let you design your own collection system that is far more precise. Consider these alternatives to improve data accuracy.
Sometimes, less is more. Especially if all the data you are storing is in complete chaos. In that case, it is time to grab the broom and start bringing order to your databases.
Data cleansing is precisely that — conducting an audit of your data to clearly understand what data you have, which is useful and which adds no value. The key in this step is to get rid of anything that clutters your analytics and prevents you from seeing reality clearly.
It is important to carry out this step regularly and to automate the process as much as possible. Many tools and data sources will allow you to run recurring cleanups and update your criteria so your data always remains relevant to your business.
The problem may come down to a communication issue — not between your teams, but between your data sources and tools. With so many software platforms, projects and tools, it is almost inevitable that data ends up siloed and not centralised anywhere in particular.
The key to solving this problem is to invest in a tool that centralises and integrates all your data sources in one place, under the same criteria. This will not only give you a single view of the situation, but also help you identify duplications, inconsistencies and errors.
Having a single dashboard to visualise your data will make your life much easier. And if that dashboard draws from all the data sources that are useful to your business, it will serve as your compass — one that always points north.
Ah, data validation. An indispensable step that is often ignored. Amid the whirlwind of collecting, storing and analysing your data, you probably do not treat this step as a priority. But trust us — it is.
Data validation involves reviewing your data and subjecting it to criteria that guarantee it is accurate, consistent and relevant. In other words, that it tells the truth. Because it would not be the first time a digital tool claimed to have sold X units when that was not actually the case.
For your data to be quality data, you need to be able to trust it. That is why good attribution models and regular validations will help you identify whether any of the data is imprecise or does not reflect reality as it is.
A quality data point is a useful data point. Simple as that. If your teams cannot leverage all of your business's data, then following all the steps above will count for little.
To ensure your data is useful, it must meet two requirements: it must be accessible and it must be understandable. And for that, nothing beats an intuitive and practical visualisation dashboard for all teams (and dynamic is even better).
When you design your dashboards, think about all the teams who will be using them. Try to design interactive solutions that allow filtering information, running specific searches or adapting charts to specific needs. That way, your data will always be relevant — and high quality, of course.
For your data to be quality data it all comes down to it being reliable, accurate and useful. In other words, it should help you get a real picture of what is happening in your business and your environment so you can make more strategic and useful decisions. If those requirements are not met, your data will be practically useless.
To change that, here is what we propose: write to us and at Boost we will analyse the quality of your data in depth (from collection through to integration and current visualisation) and propose a tailored solution. A quality solution that is genuinely useful for your business. Promised.
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