Book a demo

Book a demo

Case study streamlined data from 160 weekly hours to just 5 and doubled return on ad spend
600+ data sources
"Great tool to automate data pipelines

By having so many connectors and easy to use UI, Adverity allows us to save time when building ETL pipelines."

Blog / 8 Data Quality Best Practices For Your Day-to-Day Marketing

8 Data Quality Best Practices For Your Day-to-Day Marketing

If you rely on marketing data to thrive (and frankly, who doesn't?), you've come to the right place. Welcome to your one-stop shop for understanding and implementing data quality best practices.

Data quality is vital to marketing analytics, fueling effective decision-making and outpacing the competition. Yet, if your data isn’t accurate, complete, consistent, timely, or relevant - it can lead to ineffective marketing campaign optimizations. So, let's delve into the top 8 actionable data quality practices to get you started.

1. Determine which data is relevant

The first step to improving data quality is knowing what's important. Not all data is created equal, and sifting through irrelevant information can make your life harder than it needs to be.

Your source data will have dozens of fields available - but that doesn’t mean they all need to be consolidated, transformed, and brought into a centralized database. 

The more moving parts you have, the more complex things can become and the higher the likelihood of encountering errors. 

To help determine which data is most useful, consider the business goals you want to achieve, the metrics important for measuring those goals, and the data most useful for decision-makers to work from. 

Delivering your marketing team the data they need to achieve their objectives and nothing more can help avoid a sense of data overwhelm.

Release the burden from your team by managing data effectively and allowing them to have stability and complete control over the data they extract, transform, and load.


2. Centralize marketing data with a data integration tool

Our next data quality best practice is to ditch manual integration. Rummaging through disparate data sources is time-consuming and confusing. Not to mention how manual data integration can leave you open to human errors, with the smallest mistake leading to significant optimization errors.

Why not bring all your data into one place with a data integration tool? Data integration tools can wade through your data silos and churn out beautifully consolidated datasets on the other end. They're real game-changers in the world of quality management and data processing!

Centralizing your marketing data improves team collaboration, efficiency, and decision-making. With data integration and automation through platforms like Adverity, you can send your integrated and unified data to various destinations. Let the data integration tools do the hard work to streamline your information flow, remove the likelihood of human error, and enable more effective data-driven strategies.


centralizing data

You can streamline data centralization by using integration tools.

3. Standardize your data to make it consistent and comparable

Now that you nailed down your important data and centralized it, it's time to tidy up. Think of your data as LEGO blocks — they need to be standard to fit together and form something meaningful. You can achieve this using data standardization techniques, helping you compare apples to apples. This way, your data is standardized, your data quality is improved, and it’s much easier to see which channels are producing the best results so you can allocate your budget accordingly.

Data standardization breathes consistency into your data. Data in raw formats becomes challenging with different terminologies, measurements, and units across platforms, bringing errors and complicating data comparisons for your team. This is where data transformation and standardization become important.

Data transformation is the process of transforming data from different sources into a single coherent database, and by standardizing it, you ensure data consistencyem and strengthen the quality of your data. Our platform’s advanced data transformation capabilities help you convert your data to uncover valuable insights that will enhance your marketing campaigns.

4. Add value to your data with enrichments

Remember the golden rule of data quality standards: good data can always improve. This is where data enrichment comes into play. Data enrichment involves supplementing existing information with additional, relevant data and enhancing its quality, accuracy, and value.  

Data enrichments can be applied at the transformation stage to add value to your data by joining two different fields together, creating new fields and values based on set criteria, or even converting the language. 

Adverity has powerful out-of-the-box data enrichments, including currency conversion, language conversion, and location data unification. And for more advanced data teams, there’s even the ability to code your own custom enrichments. For a richer understanding of your market and successful marketing campaigns, an enrichment in time saves nine!

5. Embrace real-time data

Timeliness is another crucial dimension of your data quality, with real-time data capturing instantaneous insights and revealing the current market scenario. Keeping tabs on live metrics and retrieving recent data allows you to quickly tweak and respond to marketing trends and adapt your marketing strategies accordingly.

Data should be as fresh as possible so that your marketing teams can make decisions on what happened today rather than what occurred last week. With a market-leading fetch time of 15 minutes, Adverity ensures that your team can optimize from real-time data.

The Performance Manager allows data teams to identify the time it takes to extract, transform, and load data from each different source and proactively address any issues that might be causing delays in data being loaded into the centralized database.

6. Regularly spot-check your data

Human oversight can lead to errors, so how do you ensure data integrity? Simple: spot-checks!

Those responsible for data management should regularly complete manual checks between the centralized database and the original data sources to ensure everything is accurate. Regular spot-checks are your most potent defense against data quality issues, so auditing, validating, and reconciling your data will keep errors in check and maintain your data quality in the long run.

Adverity-2023-Illustration_binary code 1

Regular spot checks are recommended to ensure data quality.

7. Implement tools to help spot anomalies

Manual spot-checks and human reviewing are important for maintaining data quality, but it isn’t viable for one person (or team) to do this for all your data sources.

Unlike troublesome data outliers, data quality tools are your friends! Implementing the right tools and technologies, such as automated anomaly detection, can help. Anomaly detection will alert your team if particular metrics fall beyond an expected range - so quick investigation and resolution can happen.

Leveraging these tools will measure the underlying health of your data and keep an eye out for deviations that fall outside your set criteria. This gives you control to be extra vigilant about inconsistencies, duplicates, and inaccuracies while enhancing your data quality checks.


8. Implement a data governance framework

Now that we've covered the tactical, let's switch gears to the strategic. Implementing a data governance framework isn't just best practice - it's fundamental for sustainable data integrity.

A solid data governance framework outlines your data policies, standards, practices, roles, and responsibilities. To ensure the smooth sailing of your data management processes, including the accuracy, consistency, and availability of your data assets.

Some key building blocks of good data governance include:

  • Data access and ownership – Setting the right access and privileges.
  • Unification and consolidation – Improving the quality of your data by having it all in one place.
  • Classification – Once unified, your data needs to be classified by splitting it into different data types.
  • Enrichment – Setting up naming conventions for a systemized approach or joining data together to break down data silos.
  • Reconciliation – For trustworthy data, automated anomaly monitoring helps detect discrepancies between platforms measuring the same metric.
  • Augmented analytics – Taking a proactive approach to delivering vital marketing intelligence and utilizing AI to reveal valuable insights and challenges within multi-channel data.

Data governance steps up quality management & data processing by offering a structured approach to improve data quality and support your marketing team.

How to ensure data quality with Adverity

There you have it: eight actionable steps toward data quality best practices. Remember, maintaining high data quality is a journey, and progress requires patience. It involves being stringent about what enters your database, how it's managed, and ensuring it isn't ridden with errors or discrepancies. We advise that you keep it simple, implement tools and technology, and take small steps.

As a data integration platform, Adverity is here for you every step of the way, offering an array of features to simplify the process of turning your data quality management challenges into assured success.

Upgrade your data quality today to effectively optimize your marketing campaigns and outpace your competition.

Make insights-driven decisions faster and easier!

Book a demo