You know data is central to everything we do because it’s the subject of a number of cliches. The first of which was probably “data is the new oil.”
The expectation was that data would change the way we create wealth and inspire new ways of doing business the way oil changed how we manufacture products, transport people and goods and create billionaires.
“The new oil” cliche was quickly followed by: “Garbage in, garbage out.” Data has the potential to be a game changer (but, alas, not fossil fuel). But only when it’s the right data and it’s accurate. When you feed a system bad data, you get a mess on the other end. You need to fill your stack with good quality data.
But what does good quality data look like?
In the context of martech tools like CRMs, CDPs and marketing automation platforms, data quality means the data in these systems is:
- Accurate: Information like contact details, purchase history and preferences are correct and up-to-date.
- Complete: All relevant fields contain meaningful data, minimizing missing entries.
- Consistent: Data formats (those pesky details around dates, names, etc.) are standardized across your platforms.
- Clean: Duplicates and irrelevant information should be identified and removed.
- Timely: Data should be entered and updated promptly to reflect real-time interactions.
Sounds great, right? Who doesn’t want quality data flowing through their martech systems, informing solid decisions and delivering great outcomes?
Now comes the hard part. How do you get there?
A step-by-step approach to improve existing data and ensure future quality
Data scientists and data-minded marketing pros go through a number of steps to reach this state of high-quality data.
The data assessment
The data assessment is your opportunity to take stock of the data that exists in the martech stack and where and how it gets consumed. There are two essential parts of the data assessment:
- Data profiling: This is where you analyze the data in each platform to understand the volume, types and current quality issues.
- Data mapping: Helps you identify how data flows between platforms and pinpoint inconsistencies.
Data cleaning and standardization
It’s not uncommon to find incomplete and inconsistent data during the data assessment. Inconsistent formatting, missing information and mismatched fields are par for the course at this stage.
There are three processes that will help you clean and standardize your marketing data:
- De-duplication: Uses matching algorithms to identify and merge duplicate records.
- Standardization: Establishes consistent formatting rules (e.g., date format, name titles) and automates data cleansing processes.
- Enrichment: Utilizes third-party data providers to fill in the missing details and gain deeper customer insights.
Data governance
Assessing, standardizing and cleaning data is great. But like a teenager’s bedroom, your data won’t stay in a pristine state for long after the cleaning is complete. Unless you want to go through the whole exercise again, you need to put in place policies to promote good, clean data habits.
Two tactics are instrumental in helping keep your data clean as you move forward:
- Data quality policies: Develop a company-wide policy outlining data ownership, access controls and quality standards.
- User training: Educate CRM and marketing/sales teams on proper data entry procedures and the importance of data quality.
Data monitoring and maintenance
Even with policies and training in place, you’ll need to check your data to ensure it’s of high quality from time to time. There are a couple of checks you can put in place to maintain the quality of the data in your martech stack:
- Schedule regular audits: Periodic checks will help you identify and address any emerging data quality issues.
- Implement data quality KPIs: You can track key metrics like data accuracy and completeness to measure your progress.
Dig deeper: How to categorize customer data for actionable insights
Data management tools and technology
You can’t go about manually assessing, cleaning and enriching thousands and thousands of records in your database, of course, which means you’ll need to bring in the right tools to do the job for you.
Among the tools you’ll want to consider:
- Data integration platforms (DIPs): These tools automate data movement between platforms, which reduces manual data entry errors and ensures consistency.
- Master data management (MDM) tools: MDM tools centralize customer data, creating a single source of truth that eliminates duplicates and inconsistencies.
- Data quality management tools: These platforms offer functionalities for data profiling, deduplication, cleansing and standardization.
- Data visualization tools: Visualizing data quality metrics helps identify trends and areas needing improvement.
Dig deeper: Unlocking the potential of synthetic data: A business game-changer
How data quality impacts marketing outcomes
What we’ve outlined so far is quite a bit of work and a significant investment in resources. You probably want to ask, “Is it worth it?” That’s a good question. And it’s one your leadership is going to ask you.
You’ll need to make the business case to get the resources you need to improve your data quality. You’ll also need to show the results of your efforts in order to keep the program going, even when resources are tight.
Let’s talk about outcomes.
Your data quality program should result in:
Increased campaign effectiveness
Your marketing team’s campaigns will be more effective thanks to the following improvements:
- Improved targeting: Cleaner, more accurate data allows the marketing team to segment audiences more precisely. This allows for targeted campaigns that resonate better with specific customer needs and pain points.
- Better personalization: Richer and more complete customer profiles enable personalized messaging across channels (email, social media, etc.), leading to higher engagement and conversion rates.
- Reduced campaign waste: Eliminating irrelevant or duplicate contacts helps campaigns reach the right audience, reducing wasted spend and improving ROI.
Enhanced lead generation and qualification
Your lead gen efforts should improve thanks to:
- Better lead scoring: Clean data allows for the development of accurate lead scoring models, helping to identify high-potential leads for sales teams to focus on.
- Improved lead nurturing: Marketing can tailor nurture campaigns based on specific customer journeys and demographics, leading to more qualified leads entering the sales funnel.
Stronger customer relationships and insights
Happy customers are the key to business success. Existing customers are less costly to acquire, provide upsell and cross-sell opportunities and will become advocates for your brand.
Improved data quality will help your organization develop
- Improved customer experiences: Accurate, personalized interactions across touchpoints foster stronger customer relationships and brand loyalty.
- Deeper customer understanding: Clean data enables better customer segmentation and analysis, revealing valuable insights you can use to inform future marketing strategies and even new product development.
Increased efficiency and productivity
Quality data allows your marketing team to “work smarter, not harder.” More efficient use of the team’s time means more work gets done. Here are a couple of areas where this pays off:
- Reduced manual work: Data cleansing and standardization tools free up marketing teams from tedious data entry tasks, which creates more time to focus on strategic initiatives.
- Improved collaboration: The availability of consistent and accessible data across platforms streamlines communication and collaboration within the team and between marketing and other departments.
Dig deeper: Building a future-ready marketing operations team
Quality data is not a project, it’s a business lifestyle
If you’ve tried to lose weight in the last 30 years, you’ve heard healthcare professionals talk less about diets and more about “lifestyle changes.” The problem with diets is they tend to end. Lifestyle changes, on the other hand, are a permanent strategy.
The same applies to data quality. You can’t assess, clean and monitor data for a year and expect the results to last much longer. Much like your weight gain when you go back to drinking soda, you’ll quickly lose the advantages of quality data when you stop your efforts.
Data quality requires a commitment from a large swatch of the organization, both in marketing and beyond. But the results are worth the work.
