If you’ve been following our series so far, you will have learned all about why you should care about data cleansing. Did you know that bad data can cost companies up to a quarter of their revenue? So, cleaning bad data is paramount for good data practice. But for more reality checks, catch up on the first instalment: why should you care about data cleansing?
Cleaning bad data can be tricky
This article presents 5 essential steps for you to better understand your org, your data, and how to start the data cleansing process. By following these steps, you can make well-informed decisions for your company. You may even discover some quick wins along the way.
Follow through these 5 steps to begin your journey to cleaner data.
1. Ask yourself: why do I need this data?
Knowing what you want to get from your data is vital, so take a step back and look at your end goal. Are you collecting all of the information you need? Or maybe you’re collecting too much? After all, one less field you’re capturing is one less field you need to maintain!
Start by creating and establishing a plan outlining what clean data means for you and your org. You can’t do anything until you understand your org and users.
For some prompts, start here:
- What data do I need to achieve my business goals?
- Am I capturing everything I need to succeed?
- Am I collecting some data for the sake of collecting data?
2. Examine where your data is coming from
It’s time to identify all the different ways that data is entering your org. It’s likely that when you dig deeper, there will be more entry points than you think!
A few entry points to consider:
- Service agents
- Sales reps
- Partner users
- Web-to-lead forms
- Integrations
Once you’ve made your list and found the holes, you can do something about them!
3. Plug the holes to clean the data
I saw a great analogy on Reddit:
“I think of improving data quality as a sinking boat. If you are sinking, you need to plug the holes first (sources of bad data) and then start bailing out the water (getting rid of the bad data) second.”
(lziemke)
Simply put, if data enters your org clean, this saves you from battling with your records later on. This should be the initial focus. Thankfully there are lots of useful ways to make this a reality with minimal effort that don’t impede the end user experience.
Where possible, think of picklists, validation rules, or even consider managed packages available from the AppExchange.
4. Try the AppExchange!
Solutions on the AppExchange can be a great cost-effective, and time-efficient way to help you combat unclean data. Managed package providers are often experts in their field, so finding a ready-to-go data management solution will help you clean your data in no time!
So how do you choose the right app for you? This isn’t an exhaustive list, but we’ve included a data cleansing focused checklist to ask the managed package providers:
- Does the solution automatically resolve data quality issues?
- Does the solution require changes to the user experience?
- Does the solution clean all my data entry points?
- Does the solution clean up pre-existing data?
- How long does it take to implement the solution?
After all, don’t reinvent the wheel when it’s already turning – if you have an issue, it’s likely that hundreds of others do too!
5. Start building good data habits for cleaner data
So you’ve taken a good look at your business, identified where your data is coming from, and even potentially put some actions in place to begin cleaning up your org. Now it’s time to maintain this good practice as your org grows and share your wisdom.
Here are a few ways you can build good data habits in your organization:
- Get users involved and trained in what to look out for
- Identify entry points that are inputting bad data
- Schedule regular manual checks for your data
Operations Director, Beth, is our resident data cleaner and a strong believer in the power of good data practice:
“There is nothing more satisfying than having a good old spring clean in Salesforce! It’s great to identify trends and get to the root cause of how bad data is able to get in. Popping in a scheduled time every two weeks is the way I’ve found works best. Don’t let it build up and be a task you keep on putting off!”
Beth Clements, ProvenWorks
We live in a ‘need it done yesterday’ society so safeguarding your org against the perils of bad data is a sure way to give you back your time and let you focus on the things that matter.
Remember: the best day to start cleaning your data was yesterday. The second best day is today.
Life’s better after cleaning bad data from your org.
Stay tuned for the final instalment to our data blog series to learn about some of our favourite cleaning apps.
See you back here soon for the final instalment of the series!
One thought on “Data Cleaning 101: 5 steps to start cleaning your data”