Your digital business data speaks for itself. Just one look at any company's analytics is enough to understand its situation. Some data reflects a solvent and successful company; other data reflects exactly the opposite.
The quality of your data plays a very important role in the state of your business. Its performance is entirely linked to how you manage and leverage your data. That is why knowing your data is key to understanding your business problems.
In this article we will explain the most common data problems in any digital business and how to identify them before it is too late. Having solid analytics is the foundation of a good business strategy.
Data does not only speak about a company's numerical situation or its key metrics. It also speaks about its strategy, its organization, and its vision of the digital world. Your business analytics go far beyond a figure here and there. They speak about your company culture.
How you collect, store, integrate, and analyze your data is a reflection of how you guide your business model. A good data strategy is an indicator of an agile, precise, fact-based business. A poor one indicates a poorly organized strategy.
That is why it is so important to stop and analyze your data. How you obtain it and how you use it. This way you can understand more significant problems for your business and all the opportunities you are letting slip by.
Every company is unique and in the world of data, no two cases are alike. But there are cases that repeat with enough frequency that, if not addressed in time, can have negative effects on your digital business. Here they are:
We all know the saying: the devil is in the details. And in the case of data, it is a reality. Data precision is, in the majority of cases, the main problem. Data with errors, incorrectly entered values, or figures that do not reflect reality are everyday occurrences in many companies.
Whether because the attribution model is failing, there is a disconnect between your data sources, or simply because there is an incorrect calculation, one thing is clear: inaccurate data ends up becoming failed strategic decisions and missed opportunities.
Let us use an example: Imagine a Korean cosmetics ecommerce. Its website is a hit, but the connection between its catalog and its inventory is incorrect. There are inconsistencies and the data is inaccurate. As a consequence, it ends up selling out-of-stock products.
Data makes sense when you look at it with perspective. Combining multiple data sources, different metrics, and different time periods gives us an overall picture of the situation and helps us understand what is happening (and what might happen). But what happens when a piece of that puzzle is missing?
The lack of key information at specific moments leaves important gaps in your business analyses. As a consequence, you will struggle to understand what is happening and make the right decisions — whether that means segmenting customers, personalizing experiences, or changing purchasing patterns.
Let us use an example: Your business is B2B and you operate across different regions of the same country. Your sales team is responsible for preparing personalized proposals for potential clients, but they realize something is missing: demographic data. A lost segmentation opportunity.
Having too much data can also be a problem. Especially if that data is duplicated. Incredible as it may seem, it is fairly common for many companies to store the same information multiple times in their systems, generating inconsistencies — especially if that information is obtained from different sources.
Duplicate data almost always translates into wasted time and imprecise, inconsistent decisions. Redundant data means confusing analyses and decisions based on incorrect data.
Let us use an example: Any business knows the story of duplicated users. Beyond causing errors in their experience, these kinds of duplications can become a strategic problem by generating databases that are not representative of reality.
Have you heard about data silos? They are one of the hottest topics right now. The isolation of information in systems or platforms — and even departments — that do not communicate with each other is a real problem for many companies.
If your data is not properly integrated, you may have a fragmented view of your business (and your results). This will make it harder to identify important opportunities or trends and act with sufficient speed.
Let us use an example: There are companies where each team is free to use the tools that best suit their needs. This results in teams like Marketing and Sales deciding to use different CRM tools. And that also results in data (such as conversion, for example) being stored and measured separately.
Data goes out of style. In fact, much faster than clothing. Information that is not updated regularly to reflect reality at all times eventually loses its relevance and becomes useless.
When your data is outdated, the analyses you produce will be obsolete even before they see the light of day. If they do not reflect the reality of your business or the market, how will you be able to identify trends or opportunities to keep growing and improving?
Let us use an example: The sale season is approaching and an online store has decided to go all-in on a single product to discount and boost sales. But when choosing the product, they only have the results from the campaign a year ago. Will it still be their bestseller?
The best way to get ahead of data problems is to sit down face to face with them and understand what the situation is in your business. Problems are more common than we think. In fact, there is no such thing as perfect analytics. There will always be something to improve.
If you want to avoid falling into these common problems, here is a brief guide or checklist with the key questions you need to ask yourself when auditing your data:
Is my data accurate and consistent? Evaluate whether it represents your business reality and does not contradict itself.
Are my records and platforms up to date? Check whether your data aligns with current reality and establish ways to update it regularly.
Are there duplicate or inconsistent entries in your databases? Make sure you do a thorough data cleanup.
Are all my platforms integrated and sharing information with each other? Review available integration options and ways to ensure everything aligns.
Am I collecting the data I need? A more strategic question you will need to answer to know whether your current data strategy truly meets your business needs.
Improve your analytics with Boost and turn your data problems into business opportunities
Data speaks. It speaks about the health of your business, about opportunities to keep growing, about new trends... The key is knowing how to listen to it correctly and avoiding the most common analytics pitfalls. Your business success depends not just on the data you have, but on how you use it. Improving its quality is not a luxury — it is a necessity.
The best way to start: entrust your data strategy to Boost. We will begin by evaluating the state of your digital business, identifying potential problems in your analytics, and proposing a new strategy that is reliable, precise, and useful for your business. Shall we begin?
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