Data Cleaning: The Ultimate Guide For Businesses
In today’s fast-paced and data-driven world, businesses of all sizes rely on accurate and reliable information to make informed decisions. However, the amount of data available at our fingertips doesn’t come without challenges.
As businesses gather more and more data from their customers, the need for maintaining it’s quality becomes increasingly important.
Read on to find out about the importance of data cleaning and how it can help your business ensure high-quality data.
→ What is data cleaning?
→ Components of Data Quality
→ What are the best ways to clean business data?
→ How To Enhance Your Business Data
→ Real-life example of data cleaning
So, what is data cleaning?
‘Dirty data’ refers to any flawed, inaccurate, inconsistent or incomplete data. Data cleaning, also known as data cleansing or data scrubbing, is the process of identifying, editing and removing these issues to ensure accuracy within the database.
This process involves removing and correcting duplicate entries, filling in missing values, standardising data formats, and resolving discrepancies to ensure your data is ready for analysis.
Why is data cleaning important?
Data cleaning is a crucial step for ensuring complete data accuracy in a set of records. For businesses, data cleaning has never been more important; as one of the key steps of any data strategy, it ensures confidence in informed decisions and gives you an advantage over competitors.
Whilst it’s not always possible to achieve 100% total accuracy, especially in the business world where customer data can change at any moment, having an actionable plan to remove dirty data will place you in a much better position than you were before.
In May 2018, the General Data Protection Regulation (GDPR), a directive of the European Union, took effect for member states of the European Union on 25th May 2018. The GDPR gives EU citizens more control over how their personal data is collected and used. Furthermore, personal data must be safeguarded and processed in accordance with its provisions.
The UK GDPR incorporates many of the EU GDPR’s principles following the UK’s exit from the EU on 31st January 2020. In order to communicate electronically with individuals, you must have their explicit consent.
It is essential to comply with the UK GDPR. The penalty for not complying can be up to £17.5 million or 4% of global turnover, whichever is greater.
What happens if I don’t clean my data?
Ignoring, or simply refusing, to clean your data will result in inaccurate data analysis impacting important business decisions. Whether you’re not targeting a part of the market or
Even on a smaller scale, there are many data mistakes to avoid. Whether it’s keeping multiple records of the same customer on file or ignoring suppression files of moved-away or deceased customers, having them on your database can result in serious consequences on how customers perceive you.
By cleaning your dirty data, you can reach the right person at the correct address every time. For larger businesses, you should be cleaning your data every three to six months, while smaller businesses should be doing it at least once a year.
42% of business decision-makers state their organisation has seen the negative impact of incorrect data, with almost 20% claiming they have lost customers as a direct result.
Keep your customers happy by ensuring your data is up-to-date using Hopewiser Data Quality Services today.
Components of Data Quality
There are many ways to judge the quality of your data and should be done so based on what’s most important to your business. There are five main components of quality data:
- Accuracy: How close to the truth is your data? Customer records should be updated at least once a year – if you are a large organisation, it should be done every 3-6 months.
- Completeness: How much data is in the database? Are there any missing data fields that can be completed? This is impacted by both user decisions and data migration.
- Consistency: How consistent is your data? This might include some datasets including second address lines and others with missing contact details.
- Uniformity: What units of measurement are included? This includes conflicting measurements such as determining revenue by £ and another by $, or distance in miles and kilometres.
- Validity: How much your data is in line with business rules and constraints
What are the best ways to clean business data?
Manually cleaning data is a time-consuming and monotonous task that can often result in many human errors, especially when working with large datasets. There are many issues that come along with manually cleaning data including:
- Wasting time & money
- Inconsistent formatting
- Missed-out entries
- Potential bias
- Decreased morale
Data cleaning requires high attention to detail, which is why you need a reliable data strategy that can maximise the effectiveness and efficiency of your employees and business. An effective data strategy should include:
Removing Irrelevant Data
One of the first steps should be identifying and removing any data that is not relevant to your analysis. In a customer database, it’s common to keep data of former customers no longer associated with the business.
Removing these records ensures that analysis and marketing efforts can be focused on your current customer base. As they return to your business in the future, archiving these previous customers is the best way around this as you will have a log of any previous communications with them.
If the customer requests for their data to be removed, you must do so in line with GDPR’s right to erase.
Deduplicating Data
Your database should have a completely unique set of customer records – if it doesn’t, you run the risk of having duplicate records creating a lot of internal confusion. A ‘duplicate’ is when there are two or more records sharing identical, or near-identical, information such as names, addresses and contact details.
It’s important to note that not all records are duplicates, as you might have customers with the same name or living in the same household. However, contacts sharing the same contact details are likely to be duplicated.
More often than, duplicate entries are the result of human error or from merging multiple data sources. These create several issues in your database and lead to miscommunication between team members, skewed data analysis and reputation-damaging issues such as misdeliveries.
Removing, or merging them together, ensures everyone refers to the same customer record and that no data entry is counted multiple times.
Inputting missing data
A commonality in many datasets is missing fields such as address lines and contact details. This typically happens due to two reasons; the customer decides not to include certain information, or there has been an issue migrating data from one source to another.
- Customers excluding information: This can happen when a customer opts against including optional information such as last name or county of address. This also happens when customers become confused over what to include in address line 2; is it the street name? Is it the town? In most situations, it’s not needed and will lead to addresses becoming split across multiple lines of entry.
- Migrating data: This can happen when columns are not perfectly aligned in two or more datasets, resulting in data ending up in the wrong line.
By inputting missing values, you can ensure data integrity throughout your database and learn more about your customers.
Important to note: Under GDPR, you can only process personal data without the customer’s consent if it is for a legitimate purpose of your business and falls within the legal scope. This will mean certain missing values, such as last names or phone numbers, can only be captured with the consent of the customer.
How you can fix it
Enrich your data with domestic, international, gone away, deceased and preference data and ensure high data integrity standards throughout your organisation.
Try Our Data Enrichment Services Today →
Data Outliers
Data outliers standout in your database as a record with unusually high or low values compared to the rest.
Whilst some outliers, like interest from sudden geographic regions or repeat purchases, are worth investigating further, others, like unbelievable birthdays (1st January 1900), can have a significant impact on statistical analysis and understanding who your customers are.
Data Validation
The goal of a data strategy is to have clean data that is accurate, consistent and reliable. Validate your data by asking questions such as:
- Is the data easy to understand?
- Does the data follow a rules-based approach?
- Can the data be analysed using tools?
- Are there any trends in the data that require further analysis?
Data validation is crucial for maintaining data quality and integrity and ensuring the dataset is fit for its intended use. By checking against predefined rules and formats, you can spot any other errors, inconsistencies or anomalies that might be in your database.
Enhance Your Data Today
Address validation
With Hopewiser’s Address Validation services, you can capture and verify address data from 242 countries across the globe.
Enrich your data with access to PAF, Multiple Residence, International, ONS, and more datasets for accurate and engaged targeted marketing campaigns.
Plug into your e-commerce website to save customers valuable time at checkout and ensure the correct address is captured every time.
Try Our Address Validation Services Today →
Email Verification
With Hopewiser’s Email Verification services, you can cleanse your email database and remove and invalid addresses at the point of entry.
Ensure every email has the correct syntax and start building accurate email campaign lists for a greater insight into how your online comms are interacted with.
Try Our Email Verification Services Today →
Real-life example of data cleaning
In 2014, a major insurance provider partnered with Hopewiser for a significant IT infrastructure project, which involved migrating their systems to a cloud platform, including address validation for 10’s of millions of addresses (record figures yet to be confirmed).
Hopewiser’s solution? A cost-effective Bureau service, ensured a 99.5% match rate and added Unique Delivery Point Reference Numbers (UDPRN) and Unique Property Reference Numbers (UPRN) to addresses. This enriched data empowered the client to offer accurate insurance rates and enhance customer experience.
The cleansing process, combining Royal Mail’s Postcode Address File (PAF) and Ordnance Survey data, used rules-based logic for accuracy. A manual review by Hopewiser’s experts further refined the data, reducing non-matching records from 27,000 to 11,000.
The client had the following to say:
“Hopewiser’s Bureau team have exceeded our expectations throughout the data migration project. They achieved a match rate above our set sign-off level, and have supplied us with meaningful information throughout the data cleansing process. This has enabled us to proactively report back to the internal higher management team.”
, updated 16th November 2023.
Topic: DataData Cleansing
