Picture this: you are an avid puzzler and finally get your hands on that 5,000-piece puzzle you have been looking forward to for weeks. You enthusiastically set to work, but halfway through you find that some pieces are damaged, others are missing, and to make matters worse, there are also pieces from a completely different puzzle in the box.
This is exactly what happens when you try to make software integrations work with bad data. Clean, high-quality data is a must for your integrations. Without high-quality data, you are guaranteed to run into problems. It’s not for nothing that every data guru proclaims: Garbage in, garbage out.
Your integrations can be as technically advanced as they come, but if the data they carry isn’t correct, you’ll never get the results you hope for. You run the risk of bogging down processes, making the wrong choices and incurring unnecessary costs. In this blog, we’ll take a closer look at why data quality is so important, what can go wrong without clean data, and how to make sure your data is always of high quality so you can always work with your data with confidence.
What do we mean by data quality and purity?
Working with good data may seem like an obvious thing to do, but nothing could be further from the truth. Companies often work with different systems that collect and store data in a variety of ways. Data quality is all about how consistent, complete, accurate and relevant this data is.
Purity means that your data sets are free of errors, duplicates or irrelevant information. It ensures that your information remains reliable and that you can make appropriate analyses, decisions and reports.
An average corporate gets its data from as many as 33(!) different sources (Source: Tikean). Therefore, it’s necessary to ensure that this data remains pure and can be processed in a way that works across all sources. When these data sources do not have corresponding communication, data quality issues arise. By using good data integration methods, such as ETL (Extract, Transform, Load) and data warehouse solutions, you will see that you ensure your quality and get better insights from your data.
Let’s take a concrete example: a customer is named “John Johnson” in your CRM system and “J. Johnson” in your ERP system, with a different customer number and address. When linking these data, confusion arises: is this one customer or two after all? As a result, unnecessary duplicate accounts can be created, customers can be confused, or important information can be lost.
High-quality data ensures that your integrations provide a complete and accurate picture of your organization or customers. This is necessary for efficient business operations.
Why is data quality so important for integrations?
You can build the most sophisticated software integrations, but if the data you put in isn’t right, neither will the results. Compare it to cooking: you can be the best chef in the world, but if your ingredients are of poor quality, the end result will never taste as you would hope. The same goes for software integrations.
In fact, research shows that on average, organizations lose about $12.9 million each year due to poor quality data (Source: Gartner; Clover DX). By not examining your data regularly, you run the risk of working with bad data. This puts you at high risk for errors through misinterpretation, faulty analysis and ultimately poor decisions, which can cost you money. If you follow this through, you’re even likely to suffer reputational damage.
So, it’s a given that high-quality data makes a big impact on efficiency within a company. Consistent and accurate data ensure that costly mistakes can be avoided. Reliable data ensure that you dare to trust your analytics, which in fact allows you to make better strategic decisions and provide better customer experiences (Source: Qlik).
Smarter use of data and data quality is also becoming increasingly important with the rise of AI. After all, AI learns based on the data you feed it. If you continually teach a child that 2+2 is 5, it will never be able to calculate properly, the same is true for AI. When you give it the wrong data, it will not do exactly what you want it to do.
The consequences of bad data
Specifically, what happens when data is not up to standard?
1. Operational inefficiencies:
Working with bad data is like trying to put together a puzzle when half the pieces are missing or don’t fit. Employees spend an unnecessary amount of time checking, correcting and completing incorrect data.
Think of an employee who has to spend hours reviewing customer data because systems have created duplicate records or addresses don’t sync properly. Or a project manager who can’t find important documents because of inconsistent storage and labeling. These inefficiencies not only cost time but also lead to frustration, which in turn comes at the expense of productivity and morale.
2. Unreliable insights:
Data is the fuel for any modern organization, but what if that fuel is contaminated? Bad data leads directly to incorrect analysis and reporting. For example: you see in a report that a certain product is selling well, but an error in the recording causes sales to be double-counted. The consequence? You decide to invest more in that product, when demand is actually disappointing.
These unreliable insights can extend beyond sales figures. How about workforce planning that assumes incorrect work schedules? Or operational costs misallocated in financial reports? One mistake can set off a chain reaction, resulting in wrong decisions and waste.
3. Poor customer experience:
Bad data also makes direct interactions with customers problematic. Imagine: a customer receives an invoice with the wrong amount because previous transactions were not properly processed in the system. Or they receive a discount offer even though they have already purchased that product. These types of errors may seem harmless, but they make a bad impression and can strain customer relationships. No one wants to work with an organization that doesn’t have its records in order.
4. Distrust in your systems and processes
Perhaps the most damaging consequence of bad data is a slow loss of trust in your own systems. When employees encounter errors again and again, they start to drop out. They begin to manually track data in Excel lists, create their own processes outside the system, and ignore internal protocols.
This kind of distrust acts like a creeping poison within an organization. It leads to chaos, lack of cooperation and, ultimately, even more bad data. You create a vicious circle, so to speak, in which the situation gets worse and worse.
How do you ensure quality data in integration processes?
Fortunately, there are concrete steps you can take to ensure the quality of your data. Managing your data quality involves a variety of processes that include detecting errors, inconsistencies and other ambiguities. It is a process of cleaning, structuring and managing, and should be part of any data management system (Source: Qlik).
1. Start with a data audit
Before you start integrating, it is essential to know what you have in place. A data audit helps you identify errors, inconsistencies and gaps in your data. This process may seem time-consuming, but it lays a solid foundation for future processes. Without this step, you run the risk of small problems growing into big ones, with all the consequences that come with it.
2. Cleaning up your data
An audit identifies the problems, but cleaning up your data is where the real work begins. This involves correcting errors, removing duplicate records and merging overlapping data sets. Cleaning up ensures that your integrations work with reliable, consistent data.
Fortunately, you don’t have to do this manually. There are specialized tools that automate processes such as deduplication and error detection. These tools minimize human error and significantly speed up the cleanup process. With clean data, you lay a solid foundation for effective integrations and better business decisions.
3. Adhere to a unified internal data policy; data governance
Data governance provides the basis for quality data in integration processes. It provides a structural approach to ensure that data remains consistent, reliable and usable. This starts with clear agreements on how data is collected, stored and disposed of. Without such a policy, you run the risk of duplicate data, errors and inefficiencies that can wreak havoc on your integrations.
In addition, governance ensures uniformity. Consider standard formats for addresses or customer names so that integrations run smoothly without manual adjustments. Documentation plays an important role here: document why settings were made and how processes work. This prevents misunderstandings and makes it easier for new colleagues to get in.
Extra tip: Follow strict naming rules
Following simple but strict naming of your data and data fields in all your systems ensures consistency and clarity. Strict naming prevents misunderstandings, such as duplicate data or incorrect links in integrations. It also makes data easier to find, which is essential when multiple departments or teams use the same data.
4. Automate where possible
You can largely automate data maintenance with AI tools or data management software. These solutions keep data up-to-date, correct errors and identify inconsistencies in real time. This prevents old problems from recurring. In most cases, you can also set it up so that you are notified when problems do occur.
Quality over quantity also applies to data
Being able to trust the data with which you work is a comforting feeling, and very important for delivering quality output. Whether it concerns integrations with external systems or streamlining internal processes, bad data can disrupt the entire chain. Don’t think the problem will go away on its own, and make sure you invest the very beginning in good data governance and cleaning up your data. This will result in more reliable decisions and a strong foundation for growth.
My advice? Start small, but think big. Commit to data quality and purity from the beginning of your integration process, and you’ll find that a little extra attention now makes a world of difference in the long run.
