A guide to spring cleaning your data

For many, spring is the perfect time of year to brush the cobwebs away and get rid of clutter that’s built up over time. And it’s no different with data. Like most things in life, data needs a regular cleanse to keep it in tip-top condition and perform at its best. Stuart Watt, commercial director at Loqate, a GBG solution, shares his advice on spring cleaning your data and giving your database a new lease of life.

As digitalisation continues to grow, organisations face the challenge of managing more data, with volumes expected to reach 163 zettabytes globally by 2025. Whilst old channels of communication may decline, they don’t go away, and new channels are emerging rapidly, creating new data that is gathering at faster rates and greater volumes.

And despite business leaders recognising the value of data, 57% of companies fear that the volume of data is growing faster than their organisation’s ability to keep up.[1] It’s this fear that leads to businesses either putting off dealing with their data completely because it’s become too overwhelming or only cleansing it when they feel it’s crucial – such as just before a marketing campaign. But managing data in this way can cause major problems for businesses further down the line.

Why is data cleansing important?

Data is a vital asset to any business. But it’s an asset that constantly evolves. It’s subject to trends, it’s seasonal and it’s volatile. Over time, businesses accumulate significant quantities of information about customers and prospects. However, with data decaying at a rate of 20% per year, this information can become outdated quickly.

That’s why data managers must strive to keep data regularly cleansed and maintained, as well as collect accurate data in the first place. Regular and structured data cleansing can have wide-reaching benefits across an organisation.

The benefits of clean data

1. Avoid costly errors

Data cleansing is the best solution for avoiding costs that crop up when organisations are busy processing errors, correcting incorrect data or troubleshooting. For example, making sure deliveries are made to the correct address the first time and therefore not requiring costly redeliveries.

2. Build customer trust

Engaging with your customers and gaining their trust is the key to a long and profitable relationship. Targeted, relevant multi-channel communications based on an individual’s identity, likes, dislikes and preferred contact method can help gain customer trust, creating world-class interactions that foster long and profitable relationships.

3. Enhance customer acquisition

Organisations with well-maintained data are best placed on developing more accurate lists of prospects. As a result, they increase the efficiency of their acquisition and onboarding operations.

4. Ease the decision-making process

Nothing helps to support the straightforward decision-making process like clean data. Accurate data supports management information and other key analytics that provide organisations with the insights they require to make well-informed decisions.

5. Increase productivity of internal teams

Data cleansing is also important because it improves the data quality and therefore impacts increased productivity. When incorrect data is removed or updated, organisations are left with the highest quality information, meaning that teams don’t have to waste time wading through irrelevant and incorrect data.

How to carry out a data cleanse

Deal with missing data

Plugging any missing values in a data set is an important element of quality data management. Missing postcodes could mean undelivered goods, whilst missing forenames can lead to important communication being misdirected. Appending missing elements to create a complete dataset often relies on external parties.

Validate existing data

Reviewing existing data for accuracy has far-reaching benefits. And with Royal Mail reporting that they force address changes to over 40,000 UK addresses each year, all organisations should be aware of the need to update the location details they hold for customers and prospects.

Remove duplicate data

Identifying duplicate records keeps your database ship-shape accurate and makes your customer communication more efficient and cost-effective.

Handle structural errors

Redressing mistakes that arise from processing, such as measurement and transfer of data, illustrates the importance of data cleansing. Inconsistent punctuation, typos and mislabelled classes are the most common problems that need to be resolved.

How often should you spring clean your data?

The data cleansing process is usually done all at once and can take quite a while if the information has been piling up for years. That’s why it’s important to regularly perform data cleansing. How often organisations should cleanse depends on a variety of factors, not least the volume of data they hold.

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Organisations need to create a culture of data quality. Having good quality data and using it effectively are two very different things. The difference between businesses that handle their data well and those that don’t is that the former has at least the option of making their decisions based on complete and accurate information. 

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[1] https://www.businesswire.com/news/home/20200901005035/en/New-Industry-Research-Shows-the-Volume-and-Value-of-Data-Increasing-Exponentially-in-the-Data-Age

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