Tired of Dirty Data? It’s Time to Implement a Data Scrubbing Initiative

Raw data coming in from various sources is often inherently dirty data, rife with factual errors, typos and inaccuracies. Left unattended, this data becomes a nightmare. Imagine having to pull a report only to realize it has duplicated data – not to mention half of them don’t even have valid phone numbers or addresses. Your boss is not going to be happy.

Correcting this faulty and inaccurate data, fixing typos, and punctuation issues, deleting duplicate information and bringing the data into a consistent format is necessary.

And that’s where you’d need to implement a data scrubbing process.

Actually, despite being an, ‘obvious,’ thing, most companies often end up neglecting the importance of clean data. In fact, data is not even considered part of a business process and is often wrongly assumed as an IT process. Most business users, managers and leaders are not even aware of the problems with their data until a key initiative fails.

It’s even more challenging for IT managers when they are tasked with the mundane responsibility of manually sorting through data, when they could easily have an automated data scrubbing solution. In an age of digital technology, AI, automation and what not, you’ll still find employees in large organizations using manual data matching and Excel filters to clean data.

If anything, this is counter-productive. Data today is so complex, it’s impossible to fix it manually on Excel sheets. Even for data experts, cleaning data manually is not a preferred task.

Apart from being hailed as a hero to fix your company’s data flaws, a data scrubbing solution can literally save your company hundreds of thousands of dollars and prevent them from making costly mistakes.

Something that would take IT teams or data experts days and months to accomplish, would take a software just a few minutes (hours?). Also, a software gives you better results, promises accuracy and lets you breathe a sigh of relief as it processes millions of rows of data.

Here are a few key benefits that you can use to convince your CEO, your CMO, your COO to invest in a powerful solution.

Bringing data quality at par is simply good business sense. More than 40% of companies state that their marketing, BI and CRM departments
name untidy data as their biggest challenge. Low-quality data is the cause of inaccurate analysis, leading to decisions that can prove costlier in the long run. More than that, a clean data can give you better business insight and reveal hidden opportunities that may not have been visible before (really, bad data makes it impossible to see sense!)

Imagine all the time you’d have if you performed tasks that are of value if you weren’t busy fixing data. This is the greatest benefit of using an automated solution – you, your team and your company can improve their processes which in turn will impact their efficiency. For example, your company could save hundreds of dollars on return mails if the contact information in your database has verified and validated addresses.

There is a crisis in your company. Sales are down. Customers are unhappy. Your company executives want reports from multiple departments in the company to determine a course of action. Guess what happens if you have bad data?

You’d have to first fix data then manually match it. But there are millions of records from disparate data sources. How do you do that? You run a common data matching algorithm to consolidate data, only to realize your data is complex and way too messed up.

Last course of action? You’d have to clean data first, make sure all errors are fixed before you can run an exact match. Well, luckily, most data scrubbing solutions come with data matching allowing you to clean data and quickly match then *without* increasing your stress meter.

With all these major benefits in place, some experts also recommend data scrubbing as an ongoing background function carried out at businesses which are largely data-driven, to avoid any dirty data related hiccup.

What’s next is deciding on the right data scrubbing solution. There are literally dozens of software solutions out there and depending on your business size, budget and requirements you can choose any that meets your criteria.

Whatever you choose though, make sure that the solution does the following:

So ideally, you want a solution that helps you maintain a standard – meaning, you don’t want your data to have the same mistakes over and over again. Once you understand the problems plaguing your data, you’d want to make sure they don’t keep occurring. An enterprise-level data scrubbing solution does more than just cleans data – it helps you sets standards, which you might not even have thought about before.

For example, you might not have known that your web form allows for the entering of phone numbers with punctuations (dashes or commas). When you want to match data, most of your phone numbers are not being matched simply because they have dashes or commas. When you start scrubbing this data to remove dashes and commas, you vow to yourself to implement a web parameter that STOPS the use of dashes.

Various algorithms have been devised to clean data which was previously done manually by the IT team, costing companies valuable time and resources; not to mention compromised accuracy as manual tasks are highly prone to human error. You wouldn’t want that on your hands!

Machine matching and cleaning is always better. Everything is automated and there is very little chance to miss a field or a list. Because a good data scrubbing software will be using multiple matching algorithms to match data and highlight duplicates. Data is so complex these days that it takes a darn smart machine to identify that Bill and Billy or Cat and Cath may be the same person with different nicks.

A standard data cleaning software will perform some basic tasks like standardization, transforming unclean data into good data by removing errors and typos and normalizing numbers between a set minimum and maximum limit, finding hidden patterns and filling in missing values, removing irrelevant and duplicate data and assembling relevant data under correct formats for easy access – you know all the boring, redundant work that you want to get rid of ASAP!

Often an Overlooked, Underrepresented Problem that Turns Into a Crisis

This needs to be said! Although companies are running after the data bandwagon and want to be considered as a “data-driven” organization, they are the most reluctant in buying the right tool or solution to fix their problems. They’d rather make the lives of IT experts and data experts miserable with redundant activities than invest in a solution that can resolve the matter once and for all.

Little do they realize that delaying the data cleaning job can result in a crisis in no time. Imagine what would happen if a segment of your customers accidentally receive an email that wasn’t meant for them. Or a data leak waiting to happen. Or a fine waiting to be imposed because your organization didn’t take its GDPR policies seriously. Gathering relevant data is simply not enough; it is the clean data that produces clear insights. It is an investment into the company, and eventually, in making sure the company doesn’t become a victim to a small typo error.