Category: Data Management

This is the third instalment of our Data Cleaning 101 mini-series. So far we’ve explored why clean data is essential in Salesforce, and introduced 5 steps to start you on your journey to cleaner data. Now it’s time to dive into the details! We’re going to examine the different problems you probably have with your data and take a closer look at some of our favorite ways to tackle them!

The problems

1. users entering bad data
2. unstandardized data and messy reports
3. duplicates and more duplicates
4. inaccurate old data

1. Users entering bad data 

It’s great if you prioritize clean data, but if your users can still input bad data with no barriers, we’re back to square one. 

For this problem, we’re looking for solutions that will clean data at the point of entry.

• Set up validation rules

Validate fields to meet set criteria before the data gets saved to your database. Design them carefully and with the end-user experience in mind.

• Use alternative data types

Do you need to have everything as a text field? Could picklists, radio buttons, or checkboxes be used instead for the data you’re storing?

• Search the AppExchange

If you’re looking at AppExchange solutions, use our five step guide from Part 2 to assess whether the solution resolves your specific problem.

💡 It’s important to consider everybody across your Salesforce organization and make changes that also consider the end-user experience.

2. Unstandardized data and messy reports

As basic as it sounds, ensuring that your data is uniform and standardized will save you time on all kinds of tasks.

For example, if you create a report to find accounts based in Mississippi, imagine if all your records show Mississippi instead of MS or Mississippi, and its various misspellings. No more bloated field filtering, just clean data with uniform reports! 

For this problem, we’re focusing on ways we can better organize our data.

• Use international standards

Use internationally-recognized standards to segment your data in the best way possible. They’re designed to be consistent and globally understood!

• Run standardization tasks

Consider exporting data to standardize it with your favorite tools then reimport it back into your organization, or look into ways you can automate it within Salesforce.

• We loved Salesforce Ben’s Guide to Standardization which provides a handy 5-step guide to help set up order amongst the data format chaos.

• Apps like IndustryComplete* will streamline industry categorization, SimpleImport* can speed up those importing tasks, and AddressTools* will start standardizing your address data immediately! (There are free versions available too for some of our tools* to get you up and running for no cost!)

3. Duplicates and more duplicates

If your role involves importing and entering data, duplicates are a constant battle. Dealing with duplicate information or “dupes” is a huge blackhole of time for admins and fundamentally for your business too.

Find a solution that stops or at least clearly alerts you to duplicated data. Have you implemented Duplicate Rules in Salesforce? Or is there a budget for a dedicated duplicate check tool? You have some great options out of the box and also on the AppExchange.

• If you’re importing data, Excel and Google Sheets have some great duplicate handling tools available. Try them out before importing your data into Salesforce.

• Duplicate rules in Salesforce are not to be sniffed at! They can be a great free feature if you’re on Professional, Enterprise, Performance, Unlimited, and Developer Editions.

• And of course, we love SimpleImport Premium’s* multi-field matching to help identify existing records easier, preventing duplicates from being inserted and saved.

4. Inaccurate old data

Up to 40% of user productivity is lost to incomplete, inconsistent data. Sorting bad data from good wastes 50% of users’ time. These stats alone show how existing bad data is bad for business.

Removing or validating existing data is essential for your users to remain focused and productive. If some data is now redundant, archive or delete it from your system altogether, but if it’s still in use, correct it by updating and validating it. There are plenty of applications that not only draw your attention to the incorrect data, but give you the option to update, change or delete it.

• Schedule regular spring cleans of your org – regularly diving into your data is the best way to ensure your data is up to date, and to spot any blackholes of outdated information! Checking through your data every two weeks is best as our Operations Director, Beth, recommends in our 5 step guide (Part 2).

• Tools that have access to international databases can be an invaluable addition to your org, i.e. looking up address data against postal authorities.

Summary

Now you have the tools and wisdom to win the war against bad data. Let’s recap the main 4 data problems that we’ve managed to tackle, they are:

• users entering bad data
• unstandardized and messy data
• the dreaded duplicates
• inaccurate old data 

Hopefully, with the facts and resources presented in this final instalment of our Data Cleaning 101 mini-series, you can take full control of your org and clean up your bad data!

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Data Cleaning 101: a round up

So now it’s the end of our little crusade against unclean data. But first let’s go over what we’ve added to your data cleaning arsenal.

Data Cleaning 101 has provided you with:

• a reality check about maintaining clean data in your CRM (blog one),
• 5 tips to remember before purchasing a solution to figure out what you want to get out of your data (blog two),
• 4 specific data problems you might have and how to solve them.

We’d love to hear your stories with bad data and the applications that saved your data. Are there any tips you think we should know about? Tell us!

Or, whether you’ve simply enjoyed learning about data and data cleaning let us know, we’d love to hear from you.

I want to start cleaning up my data!

Got issues with your data in your Salesforce org that you’re ready to tackle? Get in touch and see if one (or more) of our solutions can help clean the dirty data stinking up your CRM. 

• AddressTools*
• SimpleImport*
• IndustryComplete*

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 go forward and 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?

It may sound broad, but 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, there’s no point collecting data if you’re never going to use it. 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:

1. Does the solution automatically resolve data quality issues?
2. Does the solution require changes to the user experience?
3. Does the solution clean all my data entry points?
4. Does the solution clean up pre-existing data?
5. 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!

Unclean data stinks up your system. It can travel through your business, slowing you down, causing confusion and costing you money. Don’t believe us? Between 2019-2021, over 40% of sales reps did not have enough information about leads and accounts to make effective sales, and there are plenty more stats around data cleansing coming up.

This is the first in our three-part series taking you through the importance of data cleansing and some top tips for practising clean data habits… so make sure to stick around!

An introduction to data cleansing

Bad data… Does it really matter? How do I clean it? And what can I do about it in the future?

Don’t worry we have you covered with the ‘whats’, ‘whys’ and ‘hows’ of dirty data throughout this series. First off a few definitions…

Clean data: Data that is without error and in its entirety so it can be used effectively by everyone.

Data cleansing: The process of identifying incomplete, incorrect, inaccurate or irrelevant data and modifying, validating, deleting or replacing it.

So how does unclean data actually affect me?

Over the past 6 years Salesforce and Salesforce bloggers have noted the persistent and holistic impact that bad data has on companies of all sizes. Let’s take a look at some numbers:

• Salesforce believes that 70% of incomplete recorded data quickly becomes obsolete and within a year not useful to anyone.
• Blogs between 2015 and 2019 see Salesforce mention that over 90% of data recorded in CRMs is incomplete.
  The fact that this figure has remained at a high level should be ringing alarm bells!

As we’ve already discussed, between 2019-2021, over 40% of sales reps did not have enough information about leads and accounts to make effective sales, and therefore struggled to achieve monthly and annual sales targets. This inevitably affects generated revenue.

Why is data cleansing important?

1. Ensure consistency, validity and confidence in your data by cleansing it regularly. This foundation can yield better results and help reach goals quicker.

2. Reduce the time employees spend sorting through bad data and let users be more productive on the tasks that matter – like growing your business or organization!

3. Eradicate data privacy worries for customers and clients. It’s outright good practice; you wouldn’t want your private mail sent to an incorrect address!

4. Protect your company’s reputation. Saving bad or incomplete information affects all areas of your company, from sales and marketing to the senior management, stopping everyone from carrying out their job well and making beneficial decisions, which directly affects the reputation of your business and its revenue.

The good news

Not all is lost! The University of Texas has estimated that even if the data entered is 10% more accurate, then your revenue would be considerably boosted not only for bigger enterprises but also for B2B and B2C firms.

So now you know what data cleansing is, how do you practice it?

Watch out for the next post in Data Cleaning 101 for our five best tips for cleansing your Salesforce org. You don’t want to miss that one!

Want to learn more about the impact of poor data?

Check out our blog: What is the impact of poor quality data?