In a data-driven world, organizations heavily rely on the ELT (Extract, Load, Transform) process to integrate and analyze vast amounts of data.
Data validation, a critical component of the ELT process, plays an important role in ensuring the accuracy, integrity, and reliability of the data being transformed and loaded. By examining and fixing errors or inconsistencies, data validation empowers organizations to make informed decisions based on reliable information.
In this blog we’ll take a look at what data validation means, and why it’s such an important process for marketing teams.
What is the ELT process?
Before looking at the data validation let’s first explain the ELT process. The process starts first with data being extracted (ELT) from different sources such as databases, files, or applications. Then data is loaded (ELT) directly into the designed destination, a data warehouse for example. In the end, transformations (ELT) will take place in the destination environment where the end-user has more flexibility and scalability for these operations.
Now that we have explained the ELT process, the next step is to understand how the data validation part is reflected in it.
What is data validation in the ELT process?
Data validation is a set of checks that compares your data against the preset rules with the aim of identifying if the received data is complete and correct.
What does that mean?
Dealing with a high number of data sources means you’re also dealing with lots of different formats — and therefore, higher potential for data inaccuracy. Data validation plays a vital role in maintaining the reliability and quality of data within the ELT process. By validating the completeness and accuracy of data at each stage of transformation and loading, organizations can minimize the risk of making decisions based on inaccurate or faulty information.
The key aspects that data validation helps to keep under control include:
- Data completeness: making sure that all the fields are filled in correctly based on the expected values.
- Format accuracy: ensuring that fields with a specific format such as country codes, naming conventions, email addresses, or dates (eg. DD/MM/YY vs. YYYY/MM/DD) are consistent. Data validation will check the fields against these specific formats and confirm their accuracy.
- Data uniqueness: making sure that there aren’t any duplicated data entries in there by mistake. Duplicated data will compromise the data quality, so it’s important to get rid of any duplicates and maintain data uniqueness.
Why does data validation in the ELT process matter in marketing?
Data validation ensures that your data is in a clean, accurate, and usable state. But what does this mean for a marketer?
Having accurate and reliable data to hand provides a significant competitive advantage for marketers. Data validation provides a reliable data foundation for campaign analysis that will drive quick and informed reporting and optimizations. In other words, a solid data validation strategy means campaign analysis becomes much more reliable and effective.
Depending on the data granularity, marketers can compare the performance of specific segments, and ramp up or dial down spend depending on which formats and segments are performing best. Valid and accurate data also opens the door for more highly personalized content, which could lead to higher customer satisfaction and higher conversion rates.
So, to summarize reasons why data validation is a competitive advantage for marketers include:
- Informed decision-making based on trustworthy insights.
- Optimization of campaign strategies through analysis of successful formats, channels, and segments.
- Effective resource allocation to better-performing areas, improving overall campaign effectiveness and ROI.
- Personalized content creation tailored to individual customer preferences, leading to higher customer satisfaction and conversion rates.
Common challenges in data validation and how to deal with them:
When it comes to data validation in the ELT process, marketers often face various challenges that can impact the accuracy and reliability of their data. Being aware of these challenges and implementing strategies to overcome them is crucial for maintaining data integrity. Here are some common challenges and advice on how to deal with them effectively:
1. Data inconsistency
Different data sources may have varying formats, naming conventions, or data structures.
Solution:
Develop a data mapping strategy: Create a data dictionary, and build out a data mapping workflow that defines how data from different sources should be transformed and standardized to ensure consistency. Use data transformation tools or scripts to convert data into a standardized format before validation.
2. Data completeness
Missing or incomplete data can compromise the accuracy and reliability of the transformed data.
Solution:
Perform data profiling to identify missing or incomplete data and develop strategies to address these gaps. Define specific rules and thresholds for acceptable data quality, and flag records that do not meet the defined criteria for further investigation. Employ data cleansing tools or algorithms to fill in missing values or remove records with insufficient information.
Incomplete data can lead to poor decisions down the line.
3. Data duplication
Duplicates can negatively impact data quality and lead to inaccurate insights and analysis.
Solution:
Identify and remove duplicate records during the data transformation process. You can use data-matching algorithms to identify potential duplicates based on common fields or unique identifiers. Conduct periodic audits of data sources to identify and resolve any underlying issues that contribute to data duplication.
4. Evolving data sources and formats
API updates can throw a spanner in the works — for example if Google Ads were to change the name of one of its fields. The dynamic nature of data sources and formats can introduce challenges in maintaining consistent validation processes.
Solution:
Stay informed about changes in data sources and formats, and update validation rules accordingly to ensure accurate validation. Maintain open lines of communication with data providers to stay updated on changes and ensure smooth data integration. It also helps to design validation routines that can adapt to changing data sources and formats, allowing for seamless integration of new data streams.
Data validation processes require maintenance due to API updates
Conclusion
Data validation in the ELT process is vital for marketing as it enables accurate customer profiling, reliable campaign analysis, effective customer segmentation, enhanced personalization, improved data quality, and compliance with data protection regulations.
The field of data validation is continuously evolving, driven by emerging trends and technological advancements. New technologies such as artificial intelligence (AI), machine learning, and data analytics are transforming data validation processes, enabling marketers to improve the accuracy and efficiency of their data validation efforts. These trends offer promising opportunities for enhancing data validation in the future.
By validating data, marketers can utilize the power of accurate and reliable information to drive successful marketing campaigns, optimize customer experiences, and achieve their business goals.
