Data cleansing done right

In today’s digital-first world, we rely on data daily, it’s the beating heart of your business whether it’s the integrity of your customers contact details, right down to the financial and product portfolios kept on file.

Every business now depends on data to merely function, let alone offer a competitive advantage. Yet surprisingly, ensuring that data is accurate and actionable is one of the biggest challenges faced by businesses today.

IBM estimates that the amount of data organisations collect will double every year, and this challenge is only growing.

According to Forbes, approximately 27 per cent of business leaders aren’t sure how much of their data is accurate, making data cleansing a worthwhile activity for most organisations. Simply put, if data is of low quality, then the decisions organisations make, based on that data, will be ineffective, that’s why cleansing is critical to ensure an acceptable level of data integrity.
So, what is data cleansing?

Quite simply, data cleansing (also known as data scrubbing) involves a review of all the data within a database, to either remove or update information that is incomplete, incorrect, improperly formatted, duplicated, or irrelevant. Data cleansing objectives are typically broken down into the following:

1. Maintenance of information for existing customers to enable relevant communications.
2. Maintaining the information that supports business function like collecting payments and making deliveries.

Data cleansing supports the compliance requirements of many industries including data protection legislation such as GDPR (General Data Protection Regulation).

Why is data cleansing important?

Excellent quality data = great long-term customer relationships.

Data quality is the foundation of any customer management strategy. Analytics, campaign management, customer experience, and reporting are only possible with good quality data, get it right and it results in a positive impact on your business efficiency and profit margins.
Ultimately, you should think of data as the glue that holds processes together to deliver a superior customer experience, gain a competitive advantage, and move your business forward.
Try setting expectations for your data. Create data quality key performance indicators (KPIs). What are they and how will you meet them? How will you track the health of your data? How will you maintain data hygiene on an ongoing basis?

What defines high-quality data?

In short, accurate, consistent, valid, complete, and uniform. These foundation factors make up great data, but let’s break it down:

Accuracy

Data accuracy is one of the components of data quality. It refers to whether the data values stored for an object are the correct values. To be correct, a data values must be the right value and must be represented in a consistent and unambiguous form.
How do we know if the data is accurate? Accuracy is more challenging to remedy than data completeness and consistency. Accurate data is often the result of trained employees, however, there is still room for human error. To reduce the likelihood of inaccuracies, it is vital to implement extra measures like adding additional note fields, GPS location and time stamps to record further details and serve as a reminder.

Consistency

Data consistency means that there is consistency in measurement of variables throughout the datasets. This becomes a concern especially when data is aggregated from multiple sources. Discrepancies in data meanings between data sources can create inaccurate, unreliable datasets.
This can be remedied by using the drop-down menus in a data collection application, which will result in data that is consistently collected in the expected format. Instead of free form writing, there are predetermined numbers of options of which to choose from. There will be consistency across the board and allow for complete search results.

Validation

Data validation means checking the accuracy and quality of source data before using, importing or otherwise processing data. Different types of validation can be performed depending on destination constraints or objectives.
Incompleteness is a factor that data cleansing cannot fix. Reviewing existing data for consistency and accuracy will have far-reaching benefits from maintaining your communication channels to ensuring that your customers are able to pay and furthermore you will be able to fulfil any legal obligations.

Uniformed

The more uniformed data is, the easier it becomes to execute.
What standard units were used when capturing the data? It’s important to ensure that all values are in the same units. If you do not know what units were used, it can be challenging to clean data after the fact.

Completeness

Is the data complete or are there missing elements?
What standard units were used when capturing the data? Incompleteness is a factor that data cleansing cannot fix. You cannot add facts that are unknown. However, you can implement ways to retrieve that data from other sources if it is missing.

Benefits to data cleansing

Before you think this is just a housekeeping job which needs to be done to comply with governing laws, you’d be mistaken. Look at some of these benefits data cleansing brings to organisations of all sizes:

Increase customer acquisition

Businesses can significantly boost their customer acquisition efforts by ensuring they have high-quality data. Organisations that maintain their databases in shape can develop lists of prospects using accurate and updated data. As a result, they increase the efficiency of their customer acquisition and reduce its cost.

Improves decision-making capabilities

This one is a no brainer and we have already discussed it in this article. It is one of the biggest benefits of data cleansing. Data that is cleaned and that is of high quality can support better analytics and business intelligence. Consequently, this can ensure better decision making and execution towards objectives.

Avoid costly errors and save time

Data cleansing is the single best solution for steering clear of the costs that crop up when organisations are busy processing errors, correcting incorrect data, or troubleshooting.

Removing duplicated and inaccurate data from databases can help businesses save valuable resources. These resources include both storage space and processing time. Duplicated and inaccurate data can significantly drain an organisation’s resources, especially if the organisation is highly data-centric.

Enhances productivity

Clean and well-maintained databases ensure high productivity of employees who can take advantage of that information in a broad range of areas, starting from customer acquisition to resource planning.

Drives revenue

Spending a lot of time cleansing data can be very expensive. Businesses that work on improving the quality of their data through an effective data cleansing strategy can drastically improve their response rates to customers. Consequently, this leads to more productivity, happier customers, and much better decisions.

Businesses that take proper care of their databases are rewarded with these and many more benefits. Organisations that keep business-critical information at a high-quality gain a significant competitive advantage in their markets because they’re able to adjust their operations to the changing circumstances quickly.