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Blog / What is Data Consistency and How To Manage It

What is Data Consistency and How To Manage It

For modern marketing, having consistent data across platforms is essential for making reliable, data-driven decisions. In this blog, we explore the concept of data consistency—what it is, why it matters, and how you can ensure your data tells the same story across every channel and system.

Did you know that 34% of senior marketers don’t trust the data they’re working from? That’s a staggering number, with significant implications.

If you are trying to compare apples to pears, it becomes difficult to make confident marketing decisions. As such, ensuring consistent data is one the core principles of maintaining overall data quality. And yet, many marketing teams still face issues with inconsistent data sets that make it impossible to draw any meaningful or accurate insights from their data.

So, if you’re spending more time fixing reports than acting on insights, read on to understand how to keep your data consistent and ready for accurate reporting.

You can also learn more about maintaining overall data quality with our free eBook: What Is Data Quality? A Marketer’s Guide to Ensuring High-Quality Data.

What is data consistency

Data consistency is essential for trustworthy marketing insights. The internationally recognized standard for data quality, ISO/IEC 25012, calls consistency

The degree to which data has attributes that are free from contradiction and are coherent with other data in a specific context of use.

Similarly, DAMA-DMBOK defines it as “the degree to which data values are the same across different data sets, systems, or time frames.”

For marketers, this also means standardizing all your campaign metrics, customer data, or spend data so that it appears in the same format regardless of the data source. Consistent marketing data, in standardized formats, makes it easier to integrate, analyze, and draw insights from multiple data sources, providing marketers with better, more reliable insights.

What is an example of inconsistent data?

Imagine you’re looking to analyze data across multiple different marketing channels, that all use a different format for date. Some platforms use DD/MM/YYYY, others use MM/DD/YY and some go with DD-Month-YY. 

For campaign data from the 12th June 2023 for example, you’re going to have a variety of different values in your ‘Date’ column:

  • 12/06/2023
  • 06/12/23
  • 12-Jun-23 

If you consolidated this data in its raw format, it wouldn’t be easy (or accurate) to compare and gain valuable insights from it. 

If you were analyzing data based on the first date format, your performance data for channels with the second date format would be likely to fall into December - which would be really unhelpful if you were trying to review June! 

The format of these date values needs to be standardized in order for analysis to be accurate - and this is what we’re referring to as data consistency.

What causes issues in data consistency?

Data inconsistency can stem from multiple sources, making it difficult to spot and fix. Before trying to resolve your data consistency issues, it's a good idea to first assess how your data became inconsistent in the first place. Let’s take a look at some of the most common causes.

Lack of established data standards, guidelines, and processes

In an ideal world, every business would have a clear set of data schemas, standards, and processes that were embraced by teams across the entire organization. In reality, these guidelines often don’t exist or aren’t enforced rigorously across a business.

The result is a lack of uniformity and consistency in the data that is being used, and the way it’s being processed by different teams. This can cause issues with data consistency when data comes to be consolidated into a single source of truth

Errors in manual data integration

When data is manually integrated, the risk of human error always comes into play. Even the smallest of mistakes, such as using 06/12 rather than 12/06 for a date can have a significant impact on the reporting of marketing results - which can be detrimental to both the trust in business data and the accuracy of marketing decision making. 

Lack of data validation and quality checks

It might be tempting to consider things ‘job done’ when it comes to ensuring data consistency but without implementing quality control processes and data validation checks, it’s easy for inconsistent data to make its way into your database unnoticed. 

In some respects, doing all the right things without the correct quality control measures could be the worst-case scenario. Data inconsistencies can become a ‘silent threat’ - undermining the accuracy and reliability of your data-driven decision-making without anyone realizing, as they have complete trust in the standards and tools that are in place.

 

“If you know exactly what shape your data is supposed to be in, you know what data you're supposed to be receiving every single day.”
 

- Landon Perry, VP of Ad Measurement and Data Analytics at Green Line Digital

  Watch Season 2, Episode 10 of the Undiscovered Metric

How to ensure data consistency

1. Establish robust data governance

Explained simply, data governance refers to the systems, processes, and standards that you put into place for the management and control of your data. Having a robust approach to data governance is the first step to improving data consistency and overall data quality.

This includes defining what date format, what currency, and what naming conventions should be used across all your data. By defining what your data should look like, and how each set of values should be formatted within your database, you establish a framework to ensure that the data you manage is accurate, consistent, and reliable.

For clarity, data governance goes far beyond the topic of data consistency, covering topics like data access and ownership, unification, classification, enrichment, restructuring, and analysis. 

For a deeper dive into the building blocks of good data governance,  check out our detailed ebook on the subject. 

2. Automate, automate, automate

The simplest method to reduce the risk of human error is to automate as many processes as possible. The more you automate, the less likely that errors will creep in. And, there is an abundance of tools on the market (adverity included) that provide a host of features for ensuring consistency across your data.

This includes automated data integration that lets you regularly fetch data from all your data sources, automated transformations that clean, format, and standardize your data so it’s ready for analysis (like converting date formats or unifying campaign names), and even more advanced tools like data mapping features that let you define how data fields from different sources align with each other, so your metrics are consistent across platforms.

These tools not only help improve the quality and consistency of your data, but can also save businesses huge amounts of time and resources, better accommodate scalability, and enable you to collect data more regularly to make sure it is also up-to-date.

Data mapping: The process of defining how data fields from different sources align with each other, so your metrics are consistent across platforms.
(e.g., mapping "ad spend" from Google Ads and Meta to a single unified field)

Data transformation: The process of cleaning, formatting, and standardizing data so it’s ready for analysis, like converting date formats or unifying campaign names.
 (e.g., changing “12/06/23” and “06/12/23” to “2023-06-12”)

 

3. Quality monitoring

Ensuring consistent data, and indeed overall data quality in general, is not a set and forget job. As part of a robust data governance strategy, it’s important to implement regular monitoring and audits to ensure no inconsistencies have crept in.

The term ‘audit’ is unlikely to fill you with excitement, but data audits are critical for monitoring the consistency of your data in order to prevent any minor issues from snowballing into major problems.

Most importantly, faulty values must be detected as early as possible. Again, technology can be your friend here with a number of tools offering a variety of monitors that can check your data for quality issues. This means you can pre-emptively flag problems before they propagate to downstream systems.

4. Have a plan for fixing problems

When you find problems, don’t panic. Inconsistent values can happen. Thus, modern marketers must have a plan not only to detect incorrect values but also to deal with them.
Using the date format example, if you find inconsistent data formats, you might apply a transformation rule that standardizes all date fields into a single format.

In other cases, like missing or invalid entries (e.g., text in a numeric field or an empty ID), you could either replace them with a fallback value (NA for example) that can be easily identified and filtered out, or filter out the affected rows entirely (though be cautious — this may lead to data loss).

Bottom line: there’s no one-size-fits-all solution, but there is a wrong one; doing nothing. Pre-emptive detection and a case-by-case approach to fixing invalid data should be essential tools in every modern marketer’s toolkit.

Steps to take now

If you’re struggling with inconsistent data, don’t fret. There are some steps you can take now to fix the problem.

Step 1. Work out what’s going wrong

It’s that ugly word audit again but, if you’re going to fix the problem, you need to know what is actually broken. Audit your data to work out where any inconsistencies lie. Are there specific metrics (dates, currency, campaign names, etc.) that are noticeably inconsistent? Or is it specific data sources that are throwing your data out? If you don’t know what the cause of the problem is, it is only going to be that much harder to fix it.

Step 2. Get buy-in

Depending on how widespread your data consistency issues are, the solution may or may not involve multiple and/or cross-functional teams. If that is the case, it’s always a good idea to get people on board with what you are trying to do as early as possible. Gather your stakeholders, explain the problem, explain the importance of fixing it, the scope of the project, and get everyone on the same page before doing anything else.

Step 3. Establish data governance frameworks

Next, it is crucial to define how you want your data to look. Establish what currencies, date formats, and naming conventions are going to be used. Establish how each metric should be calculated. Establish rules around where data is coming from and how it should be transformed to ensure consistency. Lastly, and this is crucial, write this down in a place that everyone can access and reference. Rules are no good if they are only in your head!

Step 4. Explore what tools you need

Now you have an idea of what problems there are with your data and, more importantly, how you want it to be moving forward, you can start looking at what tools can help you achieve this goal. Again, automate as much as possible. At Adverity, we (and our customers) benefit from having automated data integration alongside tools such as automated transformations and data mapping features so that we can remove the heavy-lift of managing our data.

Step 5. Monitor

Establish regular audits and checks to ensure your data is staying consistent. As mentioned before, this is not a set and forget exercise. Adding new data sources, for example, is a sure-fire way to introduce inconsistencies, so always be alert to any issues in your data. Again, smart monitoring tools such as Adverity’s Data Quality Suite can make it much easier to keep a track of any problems in your data and deal with them preemptively before they snowball into larger problems.

 

"Adverity helped us improve our big data needs by providing cleaned and harmonized data in a very transparent way. This was a huge spark in our productivity."
 

- David O’Neill, Global Data Solutions Manager, Mediacom

  Read the case study

Conclusion

Data consistency is not a nice to have but essential if you want to get accurate insights from your data. It’s a crucial component of what we call having a solid data foundation upon which to make meaningful decisions that will have a positive impact on your marketing performance.

This is even more true in the age of AI-powered data analysis. To get accurate insights from an AI tool, you need to have quality data for it to work from. Ultimately, if your data is inconsistent, then the AI will only ever be able to give you inconsistent insights.

 

 

 

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