If you’re investing in AI, real-time personalization, or omnichannel engagement, your success hinges on the quality of your customer data. That data quality can’t be an afterthought. It must start at the source – when it’s first ingested into your system.
Adopting a modern approach to data quality automates the structuring, formatting, and cleansing of incoming data before it ever enters your systems. You can be confident that it’s complete, accurate, timely, and fit to use anywhere, from day one.
Clean Data at the Start, Every Time
Automated data quality at ingestion isn’t a one-time fix. It needs to be a built-in, ongoing process, working continuously behind the scenes in your data pipelines, making sure that every piece of data – whether it’s headed to a marketing cloud, a CDP, or an ETL tool – is clean and reliable. It’s an essential element that helps you avoid negative downstream effects of bad data.
Your customers don’t stand still. They move, they marry, they change emails and phone numbers, and they even occasionally make a typo when entering information. Keeping up with them, especially from a data point of view, is a constant challenge.
For companies looking to eliminate inefficient legacy processes, reduce manual work, and build a strong, trustworthy data foundation across the tech stack, automated data quality at ingestion can be a game-changer.
Demand More from Data Ingestion
While automated data quality at ingestion sounds intuitive, many companies still rely on outdated technology and manual processes. Some apply different standards for data quality depending on where it came from or its intended use. Data is often pulled into a central processing hub, with formatting, cleaning and quality processes typically put off until the data is needed. Even then, customer data is often walled off from data inside marketing clouds like Salesforce or Adobe.
The result? Multiple versions of the same customer, scattered across systems: one customer profile that exists in Adobe or Salesforce, and another that exists in a data warehouse or data lake, with a lot of time, effort and manual work required to meld the two.
Today’s customers expect real-time, personalized experiences, and time-consuming workarounds, heavy coding and other DIY stopgaps won’t cut it. Taking it one step further, customer profiles manually adjusted and built on inconsistent data simply can’t be trusted to guide your decisions surrounding those customers.
Your Ingestion Tool Should Do More
A good ingestion tool doesn’t just bring data IN to your stack. It should also format and export clean, processed data out to every other system you have integrated. We tend to think of pipelines as one-way or bidirectional conduits, but to support a modern tech stack there should be as many off-ramps as there are systems that plug into and utilize or activate your customer data.
Pre-Built Pipelines = Less Work, More Trust
When data quality is automated from the start, you get consistent, secure integrations that meet your organization’s security, governance and compliance standards. Reducing manual prep work with streamlined data preparation in a no-code environment also saves money and time. With pre-built, flexible pipelines that match your workflows and connect via APIs, you can bring in and send out data without needing to cut a ticket for IT. That means fewer delays and more agility.
Ideally, your marketing stack and enterprise data strategy should work together seamlessly. A platform that offers automated data quality at ingestion and that connects to nearly any data source – whether a cloud or a legacy platform – means the end of manual interventions and inconsistent data quality standards.
Primed for Takeoff: Clean Data for Any Use Case
Data ingestion done right, as the front end of an automated data quality pipeline, sets the stage for a strong enterprise data strategy. Your customer data is clean, accurate, consistent, and ready to use wherever and whenever you need it. No coding. No extra work. Marketers should be able to trust their work without having to worry about the downstream impact of inconsistent or incorrect data that was previously fed into the system. That trust should come standard, whether they’re using it to drive acquisition, train AI models, build dynamic segments, or anything else.
Companies that modernize ingestion processes and embed automated data quality into the foundation of a data strategy will be best equipped to scale AI and unlock the full value of their customer data across the enterprise.
