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.
When data isn’t trusted, marketing teams are reluctant to use it. And when data isn’t being used to its fullest potential, it means that marketing teams are missing out on opportunities to effectively optimize their campaigns.
That’s why it’s really important to measure and maintain data quality, to help ensure that marketing teams have the confidence to trust business data for insight, analysis, and decision-making.
Data quality is a broad topic covering the accuracy, completeness, validity, recency, and consistency of data.
In this post, we’re going to focus on the importance of consistency in data quality - and the steps you can take to manage it as part of your data integration process.
If you want to hear more about how to achieve high-quality data, check out our full guide here!
What is data consistency?
Data consistency means ensuring that data is uniformly formatted and synchronized across all databases and platforms.
For marketers, this might mean ensuring that all your campaign metrics or customer data or spend data appears in the same way across your different platforms and tools. Consistent marketing data makes it easier to integrate, analyze, and draw insights from multiple data sources, providing marketers with better, more reliable insights.
What are some examples of inconsistent data?
If that sounds a bit too theoretical, let’s illustrate it with some examples.
Date formats
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/YY, 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:
- 12/06/23
- 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 interrogating 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 consistent in order for analysis to be accurate - and this is what we’re referring to as data consistency in this article.
Location data
Or, imagine you’re managing multiple ad campaigns across different platforms, and each platform has its own way of categorizing customer locations. One platform might use state abbreviations (e.g., CA for California), another uses full state names (e.g., California), while a third categorizes by city (e.g., Los Angeles).
For a campaign targeting specific regions, consolidating this data in its raw format would result in inconsistent location labels, making it difficult to analyze performance accurately by region. If you were analyzing campaign performance for "California" but data from some platforms was labeled as "CA" and others by cities like "Los Angeles" or "San Francisco," you would miss critical insights about the effectiveness of your campaigns in that region.
To accurately assess the performance across locations, it’s crucial that all location data is consistent—either all in state abbreviations, full state names, or cities, depending on the intended analysis. This consistency is essential to ensure meaningful comparisons and actionable insights.
Types of Data Consistency
But, data consitency is not only about formatting. In fact, there are quite a few different types of consistency that marketers should be concerned about to ensure the overall accuracy of their data.
Format Consistency: As already mentioned, formating consitency ensures that fields like dates, names, currencies, and addresses follow the same format across systems. For example, using a consistent date format (MM/DD/YYYY) ensures that data from different tools is easily comparable and accurate.
Schema Consistency: This involves ensuring that data fields, such as campaign names or customer IDs, are named and structured consistently across all datasets and platforms. Without schema consistency, it can be difficult to merge or analyze data from different sources effectively.
Value Consistency: This means maintaining the same data values across systems. For example, product prices or customer statuses should be the same across your CRM, ad platforms, and reporting tools to prevent conflicting insights.
Timeliness Consistency: Involves synchronizing data updates across all tools and platforms so that marketers are always working with the latest data. This prevents outdated information from influencing campaign decisions or reporting.
Cross-Channel Consistency: Ensures that data from various marketing channels (e.g., email, social media, and ad platforms) is presented uniformly. This makes it easier to analyze performance across channels and draw reliable insights from integrated campaigns.
What causes issues in data consistency?
Now we know what data consistency is, but what causes data consistency issues? Let's take a look at some of the most common causes of data inconsistency.
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.
Technical integration challenges
We’ve talked about the benefits of ETL solutions and data integration platforms as potential solutions to improving the consistency and quality of your marketing data.
Yet, not all automated solutions are created equal.
So even if businesses have taken positive steps towards the automation of their data integration, if they’ve chosen a solution that has inadequate data mapping or transformation capabilities, or doesn’t support custom data enrichments - inconsistencies in the formatting of their data can still creep in.
Lack of data validation and quality checks
Let’s assume that you’ve addressed all the points above - you’ve established a set of standards for data, and you’ve moved away from manual data integration and towards an ETL solution that you trust. 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.
Best practices for maintaining data consistency
The good news?
With the following three-step approach, it’s possible to maintain a high level of data consistency and deliver confidence across your business in the quality of your data.
1. 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 data quality.
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, we’ve put together a detailed article on the subject.
2. Automated data integration with a trusted tool
ETL tools and data integration platforms reduce the risk of human error in manual data integration by automating the process of combining data from your various sources into a single unified view.
They handle all the complicated processes of mapping your data, transforming and enriching your data, and making sure it’s standardized so it’s ready for meaningful analysis.
Data integration tools can 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 provide more frequent data fetches to enable real-time insights and optimizations.
3. Regular data audits and quality control checks
As part of a robust data governance strategy, it’s important to implement a regular data audit schedule.
The term ‘audit’ is unlikely to fill you with excitement, but data audits are critical for monitoring the accuracy, completeness, and consistency of your data, and identifying any issues proactively so you can take the relevant steps to address them quickly.
Some leading data integration platforms have functionality and features that can help alert you to any data quality issues that may affect the accuracy and consistency of your database.
How Adverity can help maintain data consistency
If you’re struggling to keep your marketing data consistent, it’s worth considering a data platform that's designed specifically for the unique needs of marketers.
By equipping yourself with a standardized and centralized real-time view of marketing performance across all channels, you can help your marketing teams to make faster and better decisions, whilst improving your campaign performance and ROI.
Adverity has a number of features that can help improve data consistency and give you complete confidence in the quality of your data.
Data Quality Suite
The Data Quality Suite is a set of monitors designed to flag any data quality issues, including consistency issues, to the relevant people as soon as they occur and before they cause greater issues later on. Monitors can be standard universal monitors or fully customized to ensure that any internal consistency rules are followed uniformly across all your data sets.
Out-of-the-box transformation and enrichments
Data transformation and enrichment is the process that helps transform your disparate data with inconsistent formatting into a restructured, consistent database from which you can draw valuable insights.
With market-leading data transformation capabilities and advanced ‘out-of-the-box’ data enrichments, you can achieve data consistency, such as currency conversion, language translation, and location unification.
Smart Naming Conventions
Smart Naming Conventions help with data consistency by enforcing consistent naming conventions in your data.
It’s possibly best to illustrate the power of this functionality with an example - so let’s assume that, as part of your data governance strategy, you’ve agreed that the naming conventions of all your marketing campaigns should adhere to the following format:
- Country Code | Year | Product Name | Category| Manufacturer
So a list of campaign names might look like:
- UK | 2022 | Wicker table and chair set | Outdoor furniture | Garden Delights
- UK | 2022 | Marble dining table and chairs | Dining furniture | Dining Solutions
- FR | 2022 | Wicker table and chair set | Outdoor furniture | Succès du Jardin
- US | 2023 | Nature's Serenity Table and Chair Set| Outdoor furniture | ABC Gardens
Smart Naming Conventions will split the data by the agreed delimiter (in this example the pipe) and ensure that each variable in the campaign name matches the criteria that have been set in your agreed format.
Imagine there’s a new marketing team member who sets up a campaign in the following format:
- 2023 | UK | Outdoor Furniture | Rattan garden set | ABC Gardens
When breaking this campaign name into separate values by the pipe delimiter, Smart Naming Conventions would identify that the first value is not a country code and the second value is not a year - and either alert you to the inconsistency or withhold the upload until the error is fixed.
Match and Map
Another feature that can help you save time and help improve the consistency of both your data and your analysis is "Match and Map".
Explained simply, this enables you to find specific values in the data you extract and create new values based on criteria that you set.
Let’s illustrate with the same campaign names we used earlier:
- UK | 2022 | Wicker table and chair set | Outdoor furniture | Garden Delights
- UK | 2022 | Marble dining table and chairs | Dining furniture | Dining Solutions
- FR | 2022 | Wicker table and chair set | Outdoor furniture | Succès du Jardin
- US | 2023 | Nature's Serenity Table and Chair Set| Outdoor furniture | ABC Gardens
Marketing teams might want to be able to compare the performance metrics of European and North American campaigns.
This could be done manually by users at the analysis stage, by adding a filter by a selection of country codes.
But inconsistencies can creep in this way. For example, does the business define the UK as within Europe or not for purposes of reporting? What if one country code is missed in the filtering process?
With Match and Map, the business can set rules so that if the country codes match UK, FR, DE, or ES, the value of "Europe" is mapped to a new field. If the country codes match US and CA the value mapped is "North America".
This means there is no confusion or inconsistencies in the way that data is analyzed when it comes to campaign optimizations.
Ready to improve the quality and consistency of your data?
High-quality and consistent data is essential if you’re going to make accurate and effective business decisions.
If you’re still operating with manual data integration or ineffective ETL solutions, or don’t have the technology to help you proactively address data quality issues - it’s all too easy for inconsistencies to affect your data, which can undermine confidence in your database and lead to incorrect decision making.
Adverity can help ensure the quality, accuracy, and consistency of your data through a full suite of data transformation and data governance features.
