Does your data need a good wash? Here's why it's important.

We all know that data is important. We use it to make informed decisions every day, both big and small. But what happens when that data is dirty? When it's not accurate, up-to-date, or complete?

Just like any other tool, data needs to be cleaned and maintained in order to be useful and intelligent. In this article, we will explore why having clean data is important and why the terms datawashing, data enrichment or data cleansing is commonly referred to by businesses that rely on data.

Why data washing is important

In the world of big data, data cleansing is more important than ever. Global Data's data washing services can help you keep your data clean and accurate. Here are three reasons why data cleansing is so important and why our clients do it:

1. Data quality is essential for decision-making.

When it comes to making decisions, data quality is essential. Poor data can lead to bad decision-making, which can have disastrous consequences. To avoid this, businesses need to ensure that their data is clean and accurate. Data washing is a process that can be used to improve the quality of data and its accuracy. This involves identifying and correcting errors in the data. By ensuring that data is of high quality, businesses can make better decisions that lead to better outcomes.

2. Data washing can improve the efficiency of your organization.

If your organization deals with data, you know how important it is to keep it clean and structured. Data washing can improve the efficiency of your organization by ensuring that your data is accurate and up to date. By regularly cleaning your data, you can avoid duplicates, prevent mistaking an incorrect contact, avoid errors in communication information. This process will ensure that your employees are working with the most accurate information possible and not carrying out unnecessary tasks with outdated data. Data washing can be time-consuming, but the benefits are fantastic and the costs are low.

3. Data washing can help you comply with regulations.

The Australian Communications and Media Authority (ACMA) requires all organisations that make telemarketing calls to have up-to-date records of customers who have opted out of receiving such calls - known as the do not call register.

Organisations that make telemarketing calls are required to 'data wash' their records against the do not call register at least every 30 days. This process involves comparing the organisation's customer records against the do not call register to identify any customers who have registered their number on the do not call register. In principle, you a performing a type of marketing datawash known in the industry and a “DNC Wash.”

If an organisation does not data wash its records, it may be in breach of ACMA's regulations and may be subject to penalties. This is just one example of where datawashing can be a crucial process requirement and there are many other examples of course. Data washing is an effortless process that can help organisations comply with ACMA's requirements and avoid potential penalties.

The benefits of data washing

There are many benefits of data washing or having clean data. Firstly, it helps to ensure the accuracy and completeness of the data and this will mean your organisation will achieve greater ROI or revenue from it. Secondly, it can help improve the quality of decision-making by providing complete and accurate information and allow you to understand your customers better. You will be making better decisions from the updated data. Thirdly, data washing can help reduce costs by avoiding duplication of effort and wasted resources. Finally, data washing can help build trust between organizations and their customers or clients by providing accurate information.

The challenges of data cleansing

In any kind of data analysis, data washing is one of the most important but difficult steps. Data cleansing, also known as “data washing,” is the process of identifying and correcting inaccuracies and inconsistencies in data. This can be a time-consuming and tedious task, especially if the data set is large.

There are several challenges that can make data cleansing more difficult. First, various sources of data may use different formats or standards, which can make it difficult to reconcile the data. Second, some errors may be subtle and hard to spot. Finally, human error can introduce new inaccuracies when data is manually entered into a system. A good example are date of birth format corrections, staff inadvertently placing contact information in unformatted or incorrect fields.

Despite these challenges, data cleansing is essential for producing accurate results. By taking the time to cleanse your data set, you can ensure that your analysis is based on accurate and reliable information. Imagine sending out hundreds or thousands of residential address mail items where the postcodes are missing or the street numbers are missing. The cost of postage and money spent would be dreadful.

How to get started with data washing

There are many ways to cleanse data, but the most effective way is to use a data washing service. These services are designed to reduce human error and provide high-quality results. Who you are looking for is a service provider that is data reputable, has safe and trusted data handling processes and transparent with pricing.

When it comes to datawashing, quality is key. Global Data has the best data washing services available to increase data accuracy, compliance, and business efficiency. If you're interested in using our data washing service, request a demonstration or call us today. We'll be happy to answer any questions you have to help get you started.


It is evident that data washing is an important process that should not be overlooked. By taking the time to cleanse your data, you can ensure that your decisions are accurate, and your choices are based on sound information. Additionally, data washing can help improve the efficiency of your operations and avoid potential problems with compliance and dreadful costs.

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