Low-quality data and analytics carry a very high cost for your business. In fact, it hides many implications that ultimately translate into money badly invested, poorly strategic decisions and, above all, missed opportunities.
To fix it, the key is to bring order to your data and start steering your direction with clear, reliable information. But to get there, it is first important to identify what the problem is with your data. Because, as strange as it may seem, there are different types of low quality.
All of these data points are called Poor data. And although they share many characteristics, each type can be low quality for different reasons. In this article we explain which are the most common and how they affect your business.
The concept of quality is, for many people, something relative — something that depends on individual expectations. But when it comes to data, quality is something measurable with a direct impact on a business's results. The future of your company depends on the quality of your data.
But why exactly? Because the decisions you make day to day depend on Poor Data. And those decisions become better or worse results for your business. In fact, all of the following depends on your data:
Investing your time well – Poor data quality means more time cleaning it and checking it is correct, more time integrating it and verifying it matches across sources, and above all, more time analysing it.
The quality of your strategy – When your data and analytics are not high quality, it is easy to lose direction. It is practically impossible to make decisions ahead of time, be confident in the impact of those decisions and react quickly to unexpected problems or poor results.
Your responsiveness – To sell more, you need to aim better. With good data analytics you will know when, how and who to target with your different initiatives to increase your results.
To solve a problem, you first need to understand it. In the case of your web analytics, the key is to understand the root cause of your data's poor quality. Is it due to the way you collect it? Is the problem that data points do not match up? Is there something slipping past you?
Here is a short guide to understanding which types of low-quality or Poor data are most common in any self-respecting digital business:
No matter how much time and effort you invest in your analyses. If your data is inaccurate, the results will not be worth it. Data inaccuracy is a very common problem in any business and can turn any effort upside down.
Figures off by one, incorrectly entered names or swapped data points. A single small deviation or inaccuracy is enough for an entire digital business's analytics to lose the rigour needed to make the data useful. If this happens, there is no going back — everything needs to be corrected.
→ The best solution for identifying inaccurate data and putting an end to it is to take the time to compare it with reliable, quality data. For example, you can compare your real sales data with your digital campaign data.
Sometimes the problem is that we are unable to get a complete picture of reality. If a specific piece of data is missing, the entire structure of our data can collapse and leave us without the ability to draw solid conclusions.
It could be a demographic data point, data about your campaigns or simply data about your customers' visits. Any gap can cause a stumbling block when it comes to identifying trends and opportunities for your business. That is why it is important to keep a close eye on your data.
→ Often, the problem of incomplete data is not solved by looking at the data you have in front of you, but by taking stock as a digital business. What information do I really need to make decisions? With that question answered, you will be able to identify the gaps in your data.
What if you have too much data? Yes, this can also be a problem in your analytics. In fact, when data accumulates and overlaps, that is when doubts start to arise in any digital business.
When data is duplicated (either because it is collected twice or there are 2 data sources analysing the same thing) the big question arises: which one should I trust? The main problem with duplicated data is that, in most cases, the two copies do not match and put you between a rock and a hard place.
→ To eliminate duplications, follow a simple process: first, cross-reference your different sources and databases to identify data entries that are identical or too similar. Then choose the version that best fits your data structure and discard the rest. This is a process you need to repeat regularly.
Multiple data sources, different digital tools, marketing campaigns across different channels... Any digital business faces a lot of information at the same time coming from completely different places. And this is very likely to result in inconsistent data.
This problem consists mainly of different data points not sharing the same characteristics (date format, for example) or calculating certain values differently (number of visits or campaign reach). Result: your data does not fit together and everything becomes a complete mess.
→ The problem of data inconsistency is an integration problem. While you can fix it by reviewing each data point, the best solution is to have a tool that integrates the different data sources and unifies the various formats, metrics and so on.
And what about data we do not even know how to standardise? Yes, the inconsistency problem expands to the point where we face something bigger: data with no particular structure.
Some examples: qualitative interview results, third-party information or market studies. This type of data, while useful for any business, is difficult to structure and format in a way that can fit in with the rest of the analytics and be useful.
→ Again, integration is the answer. Poorly structured data must go through a cleaning and organisation process that adapts it to your current data system and allows it to be integrated with the rest of your data. Remember: the effective combination of different data points is the key to obtaining interesting insights.
Finally, we need to talk about all those data points that fly under the radar for digital businesses and that nobody knows how to make enough use of. Known as Dark data, these refer to data that lives in the shadows waiting to see the light.
According to IBM, almost 80% of current data could be considered Dark data. This not only wastes the company's economic resources but also means that many opportunities are being ignored. And that is in nobody's interest.
→ To address Dark data and decide what to do with it (delete it or bring it to light), it is important to carry out a proper data audit and identify where this data is and why it is not being used.
These are some of the low-quality data points you should have on your radar. You now know that to solve a problem the first step is to fully understand that same problem. Now that you have some of the key concepts of Poor data in mind, it is time to design a strategy to implement solutions.
From Boost we help you design an audit to identify all of these common data quality problems. And once we have the improvement areas clear, we move on to designing analytics tailored to your digital business.
Sounds good, right? Write to us and let's get to work.
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