Data Quality is everything. Good data oils the wheels of your business. Poor data grinds it to a halt.
So, is your data good, bad, or ugly??
- Good quality data: Your reports are accurate. You trust them. You’re confident in making critical business decisions because you have good data
- Bad data quality: You run your reports, but the data needs tweaking before running them again. It’s Thursday, you run a report and the data was last updated on Tuesday. There’s an important customer who’s bought again from you since then. They’re missing from your report. Most of the time you trust your data, but today you need to spend time double checking it
- Ugly data quality: You don’t run reports. You don’t trust your data. People give you the answers. This feels unstructured. You prefer structure. You can’t get the full picture without good data. You have niche systems that don’t talk to each other, so reports get created manually outside of the system. Clint wouldn’t be happy.
Starting with The Ugly Data League… addressing data quality
Some traits of ugly data:
- First names stored in the last name fields or vice versa. You cringe when you imagine that mail merged letter addressing the recipient as Mr Mark instead of Mr Pullar
- Last names containing brackets with that person’s maiden name…you’ve got another mail merge problem!
- Inaccurate phone numbers and email addresses. You know there will be a critical moment when someone needs those details…but they’re incorrect so they can’t easily contact that customer
- Missing or inaccurate dates of birth. You’ve got some customers aged 5 and some aged 105! Bang goes segmenting your data for marketing activities until this gets cleaned up
Promote yourself from the ugly data league to the bad data league.
- Train your team on the embarrassing issues that ugly data creates, such as addressing people wrongly
- Make sure your system uses mandatory fields so important data isn’t lost
- Make sure your system can check for badly inputted dates and incorrect email addresses
- Train your team again and continue training them, until they input data correctly the first-time
- Create a data champion(s) in your team with the authority to make people improve their data quality. It’s that important.
Moving on to The Bad Data League… addressing data timeliness
Examples of bad data:
- Contact details are not updated immediately when you’re informed of a change
- Opportunities, quotes, and enquiries are not updated regularly enough, meaning you don’t know what’s been discussed, the latest prices and the expected date to close the deal
The data that’s entered isn’t ugly – that’s a start! But your team ‘never have time’ to update data as part of their daily activity
Promote yourself from the bad data league to good data league:
- Data timeliness is crucial. Train your team on the impact of basing decisions using old data
- Use live system data in all relevant meetings to highlight its importance – sales meetings, account management meetings, customer service meetings and financial planning meetings. If an item is raised which isn’t in the system, then the meeting is an ideal time to highlight this
- Make it clear that the only reports used in the business are run directly from the system. Not reports tweaked in Excel. Not reports modified in Word. Reports run directly from the system – make the system and your data the single version of the truth
Finishing with The Good Data League…keep it up!
The name of the game here is consistency.
- Your people understand data quality. They know what data to enter into which fields
- New starters get trained in the importance of data quality and quickly adopt the culture
- System updates for changed contact details, company details, and data changes during your sales cycle happen immediately
- You frequently train your team that you’re only as good as your data – and your data forms a key part of all decision making across the business
Good data is what makes a good business stay good. A business which displays confidence in everything it does, knowing that it’s backed up with the solid foundations of good data management. Getting into the good data quality league takes discipline, consistency and focus across the whole team. Avoiding data relegation means you need to keep doing this, indefinitely. So keep practicing.
Want to know how to get to The Great Data League? Watch out for next week’s article.