5 Data Cleansing Techniques Every Business Owner Should Know

In terms of data, there are two types – clean data and dirty data. The latter lacks accuracy and commercial value. It accumulates, duplicates, and deteriorates. In order for data to be considered clean, it must be accurate, concise, and populated. Thankfully, an effective data cleansing hygiene process can be straightforward, which many businesses don’t realise. They are also often unaware when they have bad data.

data cleansing

CRMs and marketing tools are only as powerful as the data you populate them with.

But the importance of data cleansing goes beyond saving money and generating leads, it is also crucial for the company’s reputation and brand.

General Data Protection Regulation (GDPR) dictate that businesses holding any data containing personal contact information must comply with guidelines stipulated by it.

An individual has the right to be informed about the use of their personal data, to rectify data errors, and to have their personal data deleted. Any changes or deletions should be applied to all copies of the record.

If a company has dirty data, their contact information may be outdated. By sending emails and SMS marketing to the wrong people, your marketing messages could get buried in spam folders, blacklisted or worse you could be fined under the regulations.

By creating a solid data cleansing strategy, you will not only save time – but also ensure your customer data is compliant.

Following best data practices ensures any insights from data analytics are more accurate and useful.

More Than Words Marketing has put together this 5-step data cleaning process to help you understand what data cleaning is and how to achieve it.

Step One: Plan Your Data Quality Strategy

Make sure you establish and follow data quality key performance indicators (KPIs) for the whole team.

What steps should you and your staff take to reach these KPIs, monitor your data over time and ensure it stays healthy?

When you follow these best practices consistently, you can uncover the source of data quality problems, identify incorrect data, and identify where errors occur.

Step Two: Automate Contact Data Standardisation

When unhealthy data is entering your CRM, you cannot maintain good data hygiene.

It is imperative to check data at the point of entry – even before data cleaning takes place. It will be easier to detect duplicate or inaccurate data if all information is standardised when it enters your database.

The best way to ensure the accuracy of your data is to streamline your data entry with machine learning and automation software that meets your specific needs and objectives.

It’s often best for business owners to be involved in selecting this software since they have the most comprehensive view of business goals and processes.

Since many business leaders don’t understand how to review software, it may benefit them to consult or work with external experts and companies.

Step Three: Validate and Remove Duplicate Data

Duplicate records sap valuable resources instead of adding value.

Data cleaning should include the verification of postal addresses, phone numbers, and email addresses.

If you are cleansing an existing database and have a lot of data to work with, now is a good time to invest in a data cleansing tool or outsourced data cleansing services to bring your contact data up to date.

Step Four: Resolve Structural Errors

There may be structural errors which crop up during the data cleansing process or data transfer, often resulting from human error.

There are several areas one should keep an eye out for when fixing data structure, such as typographical errors, grammatical mistakes, and so forth. In most cases, the data structure concerns categorical data.

This is where we correct misspelled words and summarise too-long category headings so that they can be analysed more effectively.

For example, it would be necessary to standardise a title field if you’re analysing two different data sets which are for the same demographic (think ‘doctor’ and ‘GP’).

The same applies for dates, addresses, phone numbers, etc, they all need to be standardised, so they can be managed and imported into CRM or email platform without error.

Step Five: Append Data

At this point, you are likely to have a certain number of fields populated for each data record in your contact list.

Let’s say, as an example, you have a first name, a last name, an email address, and a business address.

However, it would be significantly more valuable to marketers to fill in missing values such as phone number, number of employees, and location for each contact as well.

A business can append existing records with an external data provider to populate missing fields of information resulting in a complete data record.

Additionally, by appending existing contact information, you can discover new information about an audience segment, and target those prospects with more precision.

As an example, say you have been primarily focused on telemarketing in the past, but you would like to expand into digital marketing. By adding missing data such as email addresses, social media and website addresses, you can target your current audience through digital channels.

You can refine your marketing efforts by narrowing your target audience. Let’s say you operate a business travel company, and your marketing strategy in the past focused on all businesses.

The audience is too broad – not all businesses put on business trips. Adding data about prospects who are looking into business travel, or already plan and put on trips and events will improve your ROI.

At More Than Words Marketing, data cleansing is a daily activity.

A commitment to data quality allows us to call ourselves a leading data cleansing service in the UK.

Our unique selling point is that we can deliver data cleansing using both existing compliant marketing data and telephone research, this results in the most comprehensive and accurate databases for clients.

We’re available on 0330 010 8300 or you can click here to email us.