Businesses rely on data in 2025 for product sales, customer service, research and development, and market analysis (to mention just a few applications). But that doesn’t mean that they are getting their approach to managing information right. In fact, many companies fall short.
Worse still, the impact of poor data quality is tremendous. It can lead to lost sales, missed opportunities, breakdown in customer relationships, and poor marketing drives.
So, what should you do when your business data is shockingly bad? Let’s take a look.
Stay Calm
The first step is to stay calm. When you realise how terrible things are in your company, there’s always a temptation to panic.
The first step is to identify the scope of the issue. You want to know how many departments are affected, and how long the problem has been going on for. Even if all your data is awful across every division, and always has been, that doesn’t mean you can’t recover. All you need to do is go through it looking for:
- Duplicates
- Errors
- Missing values
- Outdated entries
You can also use data profiling tools that do a lot of this work for you. These can go through the data and highlight issues, providing you with guidance on what to do next.
Trace The Source Of The Problem
The next step is to trace the source of the problem. Why is your data so terrible in the first place?
Usually, poor data results from incorrect appending. Attaching new data to existing files doesn’t work unless the underlying software (and the person doing it) is smart.
Adding or appending new data, or merging datasets, leads to the most severe problems if it goes wrong. Data lines up with the wrong column, and every entry has the potential to be incorrect.
Another issue is human error. People might just be entering data incorrectly.
You can mitigate this issue by using machine learning. These days, AI can transcribe data from virtually any format into another, more manageable format, including database entries. All you need to do is find a solution that works and partner with organisations that can help you implement these systems.
Finally, you’ll want to look at the data itself. Does it make sense?
If your data is garbage because it’s made up or incorrect, it doesn’t really matter how you append it or how much you use machine learning–it will always be wrong. Therefore, take a look at it manually and see if it lines up with what you expect. Sometimes, you will discover that the way you’re recording data is leading to misleading statistics.
Implement Better Data Governance
Another approach is to implement better data governance. If you can get this step right, your data policies will be clearer.
You should decide:
- Who owns it
- Who can access it
- Where it will be stored
- How you will maintain it
- How you will collect it
- When you will conduct audits to check it
Knowing these basics gives you a structure you can follow whenever data issues arise. You know who is responsible at each step of the chain, and what they should be doing.
Invest In Better Tools And Training
While you’re at it, it’s also a good idea to invest in better tools and training. The more adept your business becomes at managing data, the better.
For example, you should upgrade your data management tools if they are out of date. New tools and AI integrations can improve their operation substantially.
You can also instruct your team on proper data management practices and instil in them the correct processes. If they understand how to handle information and make better use of it, that helps them prepare for disaster.
Turn Crisis Into Growth Opportunities
Finally, you can use your shockingly bad data to turn a crisis into a growth opportunity. Now you’ve identified the problem, you know what to do next.
For example, you can leverage insights from fixing bad data. Working out what went wrong allows you to better manage data in the future.
You can also use the opportunity to audit your information so you can make better decisions. It often contains nuggets of wisdom you can leverage.
Conclusion
Ultimately, your organisation will reach the point where it has some bad data. The trick here is to learn from the experience and implement new systems that avoid the same from happening in the future. If you act quickly, you can often repair your data, even if it seems shockingly bad right now.