We get very upset when we hear stories about water or air pollution.
Well, it’s natural, because we know there are scary consequences that could affect us in a harmful way.
Breathing difficulties, disease outbreak or risk of developing a life-long condition… The list goes on.
However, our business seems to have it another way. Too often, we ignore the elephant in the room – poor data quality.
And, we hate to break this to you, but for sure – your data is polluted – especially if you don’t have regular data-quality checks in place.
To clarify, SkyNews reported how almost half of all internet users (49%) are purposely falsifying the data they provide to companies when signing up to online services due to security fears.
We have probably all done it, at least once. I know I have.
If a business uses corrupted data for too long, repercussions are on the way. From campaign performance decrease to revenue loss and anything in between.
So, why do we dismiss data cleansing as a trivial task, like the one we plan to perform that sunny day when we have the budget or time?
Based on the facts we gathered so far, businesses mostly avoid talking about their data quality out of two reasons;
- We don’t notice how poor data is affecting our business
- The data cleaning process is a pain
So let’s talk about these points, one at a time.
Data Quality = Marketing Quality = Sales Quality = Business Revenue Quality
As we can often experience in horror movies – the scariest monsters are the ones we don’t see. However, once we do, we become more confident in our ability to survive the movie.
Generally, in a business environment, we ignore the bad data because we are unable to see or prove the harm it’s doing.
So to make the matter at hand clear, here are few facts you should know about the blood running through your business’s vessels.
There are three types of unclean data:
- Forgotten or neglected files
- Redundant duplicates
- Outdated or incomplete files
Each of these data sets can do damage to your business, as presented in the image below.
In the case of data quality – ignorance is not bliss. Poor data leads straight to wasted marketing spend.
To illustrate, on average it costs:
- $1 to prevent a duplicate
- $10 to correct a duplicate
- $100 to store a duplicate data asset if left untreated
But it’s not just about the dollars wasted. You also need a reliable data management process because your segmentation and targeting depend on it.
Otherwise, we can never achieve the desired level of personalisation or gain any value from the conversations we have with our prospects.
It’s the same on the prospect’s side – if they don’t get value from the interaction with your company – what does that say about your customer experience?
So now that we have met the monster and have seen what it can do, it’s time to arm up and face the beast.
Let the Data Cleaning Begin
The aim of our data cleaning efforts should be – taking data that comes into the enterprise and making it fit for its purpose.
Don’t get discouraged just yet. Remember – there is no such thing as perfect data.
Your data efforts will never be complete, and that is ok.
Where to start? First, consider three main aspects of data quality
Data consistency means employing your marketing automation to normalise the data at the entry point.
For example, there are many different ways for people to describe their current role at a given company.
However, if you eliminate the options for prospects to enter their data the way they see it, and offer a standardised list of occupations – you will make the data that comes into your system consistent with the data you have in your CRM or DMP.
Same goes for a location value – by normalising location values to a single value display (like zip code), your marketing campaigns will become way more efficient once the segmentation comes into play.
Action tip – list down the entry points your data comes from. From there find a way to eliminate different combinations by using your marketing automation platform.
Data completeness can be scary and extensive.
However, when it comes down to it – simplicity is key. The lack of integrity shows itself as the blanks you see in the critical data fields.
The question you need to ask is – out of all the info you need to know about your prospect, how much of it is essential to add to your database?
Consider breaking down title, industry, demographics, challenges and needs to find out what is crucial to continue to the next stage of their customer journey.
Action tip – Aim for the completeness to be up to 80% – 90% of all critical fields. To achieve it, you can add mandatory fields that need to be completed when a prospect signs up for a trial or downloads a white paper.
Data correctness shines a light on the changing world we live in.
Professionals advance in their careers, change roles and change companies more rapidly than ever.
Which means your database becomes out of date just by standing there, while time passes by.
Not to mention the point we had earlier in the article – potential customers deliberately give false data to businesses when they sign up for a trial or demo.
To address the correctness of data many companies start by identifying data sources. Sometimes the problem is with data migration, lead magnets or sometimes it’s about the lack of data updates.
Action tip – Set realistic data accuracy goals, assign a dedicated team to carry out necessary improvements or even consider hiring professional help.
Having these 3C’s in order when it comes to your data management will go a long way:
- For your business, it will help set the ground for steady data quality checks and subsequently, it will add up in marketing dollars spend over time.
- For marketers, it will help adequately segment and target their campaigns saving them time and money along the way.
- For sales, it will help build a more accurate picture of whom they are selling to and how they should be selling to them.
By laying the groundwork for data management, we have already found ourselves halfway to our data-improvement journey.
Keep on reading to achieve higher data quality and therefore, business quality.
Want to know more about Customer Data Platform Implementation?
Stress-free methods to improve your data quality
We need to make sure we always do the best we can to improve the data at hand.
In the interest of making it simple, we have broken down some easy steps to you can do today (or at least plan for them) to get you going.
Helpful tip – Keep track of your cleaning operations – as you go deeper into the process it will become useful to have a reminder of steps taken to repeat, modify or remove any activity.
Create a data quality plan
Set expectations in your business when it comes to capturing, entering and maintaining data quality levels. This step will include involving your sales and marketing team in the process.
For example – when a sales rep enters data which may be crucial to the overall marketing and business goals – it’s essential to keep it accurate and to avoid having it be guesswork.
Educate your marketing department on pressing data issues and ask the management team to get involved as well.
Identify the source of errors
Identifying the source of poor data is the step that will get you flying through the rest of the process.
Start with examining all the entry points, such as marketing automation powered data gateways, human entry errors, data migration operations or data errors due to siloed systems.
Also, consider the freshness of data, for how long are you holding on to old data sets, until you mark it as no longer valid?
Find the forgotten test cases in your database by simply searching for them. These create clutter, and in some cases, they might even contribute to false reporting.
By taking this simple step, you create a data-cleaning mentality with your employees and also setting the tone for future ventures.
Look at summary statistics
To zero in on the problem you may want to look at the summary statistics for each column.
Focus on the standard deviation or even the number of missing values.
Focus on key data points
Whether we are talking about collection or sanitation, your efforts would be best used if you focus on key data points for your business.
This means you will start your process by correcting the information you capture by following the order of how important it is.
Is the location of the prospect that plays an integral role in the purchase stage? Or the industry?
List down the key ingredients that make a prospect desirable to your business and tackle the data cleaning in that order.
Validate the data
Once you have completed your cleaning process, you may want to consider validation as a rule. This means having a system that cleans the data in real time.
To execute validation, try putting data type constraints, for example – data should be inserted as a fixed value such as numeric or date value.
You can also utilise unique constraints, they can help eliminate repetitive information in your system.
Additionally, mandatory fields can be beneficial when you have prospects leaving their details on your website.
It eliminates the white space and helps your marketing team to stitch the full image of the customer.
Deduplication is a common term we hear in the data world, especially when talking about data silos in the enterprise arena.
Duplicate records show up mostly during data collection or data migration.
You can either go in and find a system that works to identify them yourself or you can hire professional help to do so.
In any case, the next step listed is here to help you avoid the duplicates altogether.
Checking the data at the entry point helps immensely in this process, and of course, it reduces the risk of having duplicate records.
You can reverse engineer the data sets you currently have in your platform (CRM or DMP) by developing forms that will pre-offer the set of values in the same way you store them in your system.
Once you put this process to work, your database will reward you with clean data that is fit for purpose.
This method is focused on collecting the information needed to fill the white space or blank field you have in your database.
While your prospects interact with your company (or vice versa) make sure you have both the marketing and sales department ready to ask them questions.
However, keep in mind to ask about information you don’t have in your database.
The additional profiling will reduce the blank value fields and ultimately contribute to better marketing performance.
Utilise tools to optimise the data quality
There are various tools that will help you clean your dirty data, normalize the data formats or even enrich the prospect’s details.
These are great to use as a bonus step, along with other methods we listed above.
By applying such tools, you help your data rise to an acceptable quality level without sacrificing too much of your time.
The hard truth is – if you have avoided engaging in data cleaning so far, chances are – you are way behind your competition.
To get the most value out of these tips, make sure you implement them as a standardised process.
Understand data management is an ongoing process and data cleaning always needs to be running in the background.
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