McKinsey Global Institute has found that data-driven companies are 23x more likely to acquire customers and 19x more likely to be profitable than companies that don’t use data to drive their decisions. Yet, 69% of executives report that their companies aren’t data-driven.
Most leaders struggle to launch data initiatives because they don’t know where to start.
The first step is implementing a data quality improvement strategy that takes all of your existing data (from your CRM, spreadsheets, and other sources) and transforms into a form that can be used to drive intelligent business decisions.
We’ve broken the strategy down into five actionable steps to improve the quality of your existing data and set your company up to achieve sustained data accuracy and integrity.
1) Consolidate All Related Data into a Single Database
If your team is using multiple systems to store information or individuals are tracking their progress in spreadsheets, you need to consolidate everything into a single database. It’s the only way for you to get a full picture of where you’re at, what’s working, and what metrics need to be improved.
Depending on your systems, there are multiple ways you can do this:
- Integrate your systems and work with your vendors to make sure all data syncs correctly.
- If your systems are redundant, migrate all of the data into your preferred software.
- Have your employees clean all of their spreadsheets and upload them to your database.
If you have multiple teams with different data spread across a variety of locations, repeat these steps with each of them to consolidate data across your organization. For example, you may have one database for marketing and sales and a separate one for supply chain and product development.
- Consolidate all related information into single databases.
2) Thoroughly Clean Your Data
This is the most tedious and important step of your data quality improvement strategy. Having a database that’s accurate and complete lets you discover powerful insights that you’d never find if your data was incomplete or spread throughout multiple systems.
There are three steps for cleaning your data:
- Determine what information you need for each object to run meaningful reports and analyze trends. Meet with other stakeholders in your organization to decide what information needs to be required. Choose these requirements carefully since they set the foundation for your data improvement strategy.
- Go through all of your objects and fill in missing and outdated records that you’ve decided every object must have. This is the most time part of improving data quality but, it’s necessary to give you a complete picture of the information you’ve deemed vital to your organization’s success.
- Consolidate or delete all duplicate objects. Duplicate data can severely skew your reports so a key part in cleaning your database is getting rid of it. Some databases let you use logic to automate this process so check with your system admin before you have someone start manually eliminating duplicate objects.
Depending on the size of your database, cleaning data can be a very time-consuming process. To speed it up, you can outsource it to a virtual assistant company that will consolidate all of your data into a single database, clean it, and ensure all of the data stays updated moving forward.
For many companies, outsourcing data quality management is more cost-effective than implementing this strategy in-house since data maintenance occurs faster and doesn’t pull employees away from their core responsibilities. To learn more, contact us for a free consultation.
- Determine what information is critical for your database.
- Eliminate all duplicate information.
- Fill in missing records.
3) Create a System for Accurately Inputting Data
Once you’ve cleaned your database, you need to put systems in place to ensure the data quality remains high. Getting this system right the first time prevents you having to go back and re-clean your data to meet new requirements.
To make it easy to adopt, your system should be an easy-to-access internal resource page that includes:
- Video and written tutorials about how to use the database and best practices to quickly find specific types of information.
- Definitions of all of your objects and fields and examples of the appropriate information to put in each one. Make your definitions as specific as possible so that your team knows how to input data using consistently terminology and syntax.
- List all of the required fields for each object. To run accurate reports, certain fields need to be filled in for every single object. List those out.
- State how frequently various parts of the database need to be updated. For example, you may require your sales team to update a prospect’s sales stage immediately after every single meeting and require your customer success team to verify customer phone numbers and mailing addresses once a year. This section is critical because it lets people know what’s expected of them.
- Include any other information about how you want your team to use the database. You can't assume your employees know how to use it effectively so you must explain even basic steps you want them to take.
The most challenging part of keeping your databases up-to-date will be gaining buy-in from your team. Most people don’t like changing their routines and, depending on how many required fields you have, data entry and maintenance can be a very tedious process.
There are two ways to get employees on-board with your new data entry processes:
- Share how data quality improvements directly benefits them. For example, it allows them to create reports that speed up their sales process, it provides insights that improve customer retention, it makes it easier for them to create marketing materials etc. When employees see the value of having access to consistent data, they’ll be much more willing to invest the effort in creating it.
- Outsource your team’s data entry. If you’re leading a team of senior and/or top-performing employees, convincing them to input data accurately isn’t worth your effort or their time. It’s more cost effective to hire a couple of virtual assistants who will monitor your team’s activities and update your database for them.
- Create a resource page for your employees to reference when they’re adding information to your databases.
- Give your employees examples of how taking the time to input all required data correctly enables them to be more successful.
4) Leverage Data in Your Daily Decisions
93% of executives in a 2019 survey said that creating strategic changes to their processes and culture is the biggest obstacle hindering their organizations from being data-driven. Unlike the prior steps that follow objective processes to improve your data quality infrastructure, getting people to use the data in a meaningful way requires a much more nuanced-approach that a lot of leaders struggle with.
Shifting your team’s mindset from relying solely on their expertise to making data-informed decisions requires you to change how they approach their work.
Here are a few strategies to improve your team’s approach to data:
- Teach your employees how to effectively leverage data. Employees often resist data quality improvement initiatives because they don’t understand why data matters or how to use it. Providing training gives your employees the knowledge they need to adopt your new processes.
- Hold-everyone accountable to data-driven metrics. This ties employee success to data and encourages them to stay on top of the numbers that affect their performance.
- Set an example by making data-driven decisions. Whenever you make decisions for your team, share the data you used. This reinforces the importance of relying on data and increases transparency.
- Encourage employees to solve their challenges with data. When employees are struggling to reach their goals, show them how they can use data to find solutions and/or complete their work faster.
Equipping your team with best practices will help your data quality improvement strategy drive long-term benefits.
- Train your team how to leverage the data resources at your company and hold employees
- When you’re rolling out new projects and changes to your team, mention the data that informed your decisions.
5) Conduct a Quarterly Data Audit
Even with strategies in place to sustain data quality, errors are bound to occur. Conducting quarterly data audits lets you catch them before they severely skew your decision-making and identify the root causes of errors before they become systematic issues.
Here’s how to conduct your audits:
- Randomly select a sample of your data. It should be large enough to capture points from throughout your database.
- Schedule a meeting with key stakeholders who can identify if the data is accurate.
- Review all of the data points to see if they are accurate, up-to-date, contain all required information, and the data is consistent across everything.
- When you catch errors, fix them and take a deeper look at similar data and data inputted by the same person to see if the errors are individual mistakes or habitual issues.
If you discover systematic issues, you need to retrain your team on how to properly use your database or outsource database management.
For many high-performing teams, it is far more cost-effective to outsource all of your data entry and management than it is to force your highly-paid employees to waste time doing it. Our virtual assistants are trained to clean and maintain CRMs and wide variety of other databases. To learn more about how we can support your data quality improvement efforts, contact us for a free consultation.
About the Author: Emily formerly led Prialto's content production and distribution team with a special passion for helping people realize success. Her work and collaborations have appeared in Entrepreneur, Inc. and the Observer among others.