Oftentimes the success or failure of your marketing endeavors hinges on just one variable, the quality—or lack thereof—of your data. Think about it, how many Forbes and Harvard Review articles chronicling the latest successes of the next great captain of industry have you read? Too many to count, right? Now think about how many of those stories share the pivotal moment when the hero considered the information he or she had and made a wise decision using that “data” that ultimately led to their becoming the envy of all who read about them. Every hero story has one thing in common, the hero has the right information at the right time and acts on it.
Becoming a Data Hero
Our goal today is to help you become the hero of your own story by ensuring your organization has the right information (a.k.a. high-quality data) all the time, so that you are ready for action at any moment. Ideally to accomplish the goal of getting and maintaining high-quality data you need to take a hybrid approach that makes the most of your people and technology. Below, we’ve outlined four strategies to elevate your data quality, and we’ve also listed four tools that can help you maintain it.
#1 — Implement Data Governance (a.k.a. Orchestration): Governing all of the data that pours into and out of your company each day is no small task. It requires careful consideration, and the planning and implementation of a variety of data processes. In order to properly manage data throughout your department and enterprise, you will need to develop a data governance framework that will:
- Allocate Data Responsibility – Note who is in control of different types of data tools, warehouses and silos in your department and others that you share data with, i.e., sales, customer services, etc. Make sure that everyone who has a piece of the data pie is included in meetings that concern its management and future.
- Establish Protocols for Data Collection, Storage, and Maintenance —Defining the steps on how data is to be collected, kept and shared across your martech ecosystems, as well as with other departments and their technology ecosystems is vitally important. This task will likely be the most time and labor intensive, but once it is complete, you will have built a solid foundation upon which the future of your data will grow and thrive.
#2 — Set Data Quality Standards: Put a plan in place to uphold data quality by setting standards and establishing measurements for your data’s accuracy, currency, conformity, completeness and validity. It’s important to note that the management of most of these standards can be automated with marketing technology, which we’ll talk more about when we discuss the four data tools you need.
- Accuracy — To ensure the data you are working with is correct, implement ongoing data improvement processes like: data profiling, cleansing, and validation. Metrics that are commonly used to measure accuracy include the Error Rate, which measures how much inaccurate data is in a dataset and the Validity Rate, which measures how much of your data meets the specific rules or constraints that have been set.
- Currency —Outdated data leads to incorrect decisions, missed opportunities, and lost revenue. To ensure your data is relevant, always update it in real-time before using it. Metrics used to measure timeliness include: Age of Data, which measures the length of time since your data was last updated and Frequency of Updates, which measures how often your data is updated.
- Conformity — For optimum results, your data needs to be uniform across all data sources, which means it has been standardized and normalized. Standardized data is consistent in its format and can be easily shared, compared and analyzed. Normalized data means there is only one unique record for each item, which helps improve accuracy and reduce redundancies. Metrics for measuring standardization include Consistency Rate, which measures how much of your data is consistent across sources. To measure data normalization, check your Duplicate Record Rate.
- Completeness — To ensure data completeness, most businesses need to enrich data by purchasing data from a reputable third-party source in order to fill in missing information. Metrics used to measure data completeness include the Completeness Rate and the Missing Values Rate. As their names imply, these metrics show you the percent of your records that are complete, as well as what fields are missing information.
- Validity — Setting data validation rules ensures your data meets certain standards right from its point of entry. These rules might verify if email addresses follow the correct format or if all mandatory fields have been filled out. Having these checks and balances in place helps enhance the correctness and completeness of your data from the outset.
#3 — Clean Data Regularly: Cleaning your data is like clearing out a corporate closet, only instead of junking old tech and logoed knickknacks, you get rid of any data you come across that is incorrect, wrongly formatted, or incomplete. Regular data cleansing, supported by data quality audits, helps keep your data accurate and current. As with all things, cleaning your data is a process that is made far easier by modern martech. There are typically five steps in the process, which include:
- Scan data currently in use and flag all potential issues.
- Review and fix data mistakes based on established standards.
- Highlight anomalies within datasets for review, taking action as needed.
- Use third-party data to fill in missing data fields and enrich data.
- Repeat A through D in real-time every time you use your data.
#4 — Centralize Data Access: Making your data accessible from a single source helps to improve its consistency, accuracy, quality and security. When you connect your data sources to a data dashboard a.k.a. marketing ops dashboard, you gain a centralized view of data throughout your enterprise. This enables you to see what’s going on with your data at any time and make improvements in real-time. It also equips you with the ability to control who has access to what data and establish a hierarchy that helps to prevent data theft, breaches, misuse and other embarrassing scenarios.
Use Technology to Enhance Data Quality
While well-planned strategies are critical to your ability to enhance and maintain exceptional data quality, technology makes is the magic fairy that makes this feat a thousand times easier and more effective. While Scott Brinker, God bless him, is committed to keeping track of the ever-increasing list of martech tools (currently 11,038), we’ve narrowed down your list of data quality tools to a more manageable and tasteful four, and they are:
#1. Data Profiling Tools
These tools analyze your data and unearth anomalies like missing values, duplicates, and inconsistencies. They also provide an overall view of your data quality, and are handy to both auditing and cleansing processes. Examples of data profiling tools include:
- Sureshot Data Management
- Informatica Data Quality
- Talend Open Studio
# 2. Data Validation Tools
Data validation tools scrutinize data at the point of entry against predefined rules. They ensure that your data meets the set standards, which enhances the quality of your data from the get-go. Examples of data validation tools include:
- Sureshot Data Management
#3. Data Cleansing Tools
Data cleansing tools automate the process of identifying and fixing data errors, making it easy to keep your data clean, complete and ready to use in real-time.
- Sureshot Data Management
#4. Data Integration Tools
Data integration tools consolidate data from multiple sources, which helps improve data quality and consistency throughout your enterprise. It also ensures customers experience a unified messaging experience with your brand. Examples of data integration tools include:
- Sureshot Journey Orchestration
- Oracle Cloud
- Informatica PowerCenter
The Importance of Training Your People in Data Quality Enhancement
While we’ve emphasized four data strategies and tools, it’s equally important to consider the human aspect of data quality enhancement. The people handling your data should be adequately cross-trained on all the data tools you use. This ensures you are not beholden to a single data guru in your department, and it also empowers people to learn and grow in their careers, which is always a win-win. Promoting a data-driven culture in your organization also significantly improves adoption of and adherence to the data quality practices and processes you implement.
In It to Win It
The pursuit of superior data quality is an ongoing endeavor that demands regular assessments and improvements. Although the strategies and tools we highlighted today are a great starting point, the journey does not end here. To learn more about how Sureshot can help you do more with your data, reach out. We love solving data problems and making martech work precisely the way you want it to.