Dirty data is a costly hidden tax that businesses are paying. One of the latest surveys conducted by Wakefield Research reveals that more than 25% of organizational revenue is affected by dirty or erroneous data. Inaccurate, obsolete, or missing details in datasets lead to companies chasing ineffective leads, overspending on marketing campaigns, losing potential sales, and more. Dirty data not only hampers an organization’s financial growth but also its staff’s productivity across all departments (sales, marketing, finance, IT, etc.). The best practices mentioned in the data cleansing framework presented below.
According to the same survey, it takes companies more than half a day to find errors in their data and then another 4 hours to fix them. Therefore, to save resources, improve operational efficiency, and grow better, it is crucial for organizations to efficiently clean their dirty data. How? By following the best practices mentioned in the data cleansing framework presented below.
Best Practices For Data Cleansing – Data Quality Management Framework To Follow
Assess Your Business Requirements
There is no one-size-fits-all data cleansing solution for businesses. To understand which dataset matters the most for your organization and should be cleansed on priority, it is crucial to first understand your business requirements. For example, customer contact information may hold higher significance than product inventory data when your business objective is to focus on marketing. Now, depending on how complex and big your customer data is, you can devise a data cleansing strategy, including the resources, tools, and techniques to utilize.
Set Quality Standards For Your Data
How will you assess the quality of the clean data? To do that, you need to define the data quality key performance indicators (KPIs) and metrics that align with your business requirements. KPIs are specific, measurable, achievable, and time-bound goals. Metrics are quantitative measures of your progress toward your KPIs.
For example, a data quality KPI for a customer relationship management (CRM) system might be to have 99% of customer contact information accurate and complete. A metric to track this KPI could be the percentage of customer contact information that is updated within 24 hours of a change being made.
Once you have defined your data quality KPIs and metrics, you must identify the key attributes that must be accurate, complete, and relevant in your cleansed data.
Define Rules For Data Cleansing
To improve the quality of your data & ensure it meets your business standards, you need to define clear rules for data cleansing. This includes deciding on the following:
Data Quality Requirements
What levels of accuracy, completeness, and consistency do you want to achieve for each data field?
Error Identification And Correction
How will you identify and correct errors? Will you use algorithms to automate this process, or will you have human experts manually review the data?
Handling Of Duplicate Records And Missing Values
How will you handle duplicate details and missing values? Will you remove them, impute them, or flag them for further review?
By defining clear rules for data formatting, enrichment, and validation, you can ensure that your data is cleaned consistently and effectively.
Conduct Data Profiling To Identify Errors
To identify errors and outliers in data, it is essential to first understand and analyze it. This is what data profiling does. It is a process of assessing data to understand its quality, use cases, and properties. By understanding the characteristics and quality of their data, businesses can better identify and correct errors and inconsistencies, leading to more accurate and reliable data.
Data profiling tools can automate this process and help businesses to:
- Identify missing values, duplicates, and inconsistencies in data
- Understand the different data formats and types present in a dataset
- Analyze the distribution of data and identify outliers
- Discover relationships between different data variables
Standardize And Validate The Data
Standardizing data formats focuses on converting the structure of different datasets into one common format for better consistency. If the data is organized, identification & rectification of missing values, inconsistencies, and outliers during data cleansing will be easier. Thus, following predefined data cleansing rules/guidelines, you should standardize data and integrate validation checks to ensure accuracy, completeness, and relevance in your database.
Provide Regular Training To Your Staff
For efficient & seamless data cleansing, your in-house staff should have the necessary expertise and knowledge about the data cleansing framework. To ensure that they are up to date on the latest data cleansing techniques and best practices, it is crucial to provide them with regular training on data management.
Conducting instructional workshops on data cleansing is crucial for organizations because maintaining data quality is an ongoing process. New data is constantly being added to databases, and existing data can become outdated or inaccurate over time. When your staff is well-versed with the data quality standards and guidelines, they can work better to improve the quality of your data to maintain its integrity.
Conduct Regular Data Audits To Maintain The Quality Of The Dataset
Even if an organization has a data cleansing framework in place, conducting regular data audits is still important for maintaining the quality of datasets. Data cleansing frameworks are designed to identify and correct errors and inconsistencies in datasets, but they are not perfect. Data quality issues can still arise due to factors such as human error, changes to data sources, and new data requirements.
As new data is added to datasets, it is important to validate its quality to ensure that it meets the organization’s data standards. Regular data audits can be conducted to validate the quality of new data and identify any potential issues before they cause problems downstream. At the same time, it also helps to ensure that the existing data cleansing framework is working effectively and that any remaining data quality issues are identified and corrected.
Employ Automation Where Possible
Data cleansing is a tedious and demanding process, especially when you have to perform it for large datasets. Leveraging automated data cleansing tools or algorithms at several possible stages can make the process more efficient and effortless for businesses.
Automated data scrubbing tools can scan large datasets for common errors and inconsistencies, such as missing values, duplicate records, and invalid entries. Once the tools have identified these errors, human experts can review and fix them. Alternatively, experts can train these automated tools on datasets that contain the defined ranges or values for various data fields (e.g., employee salaries or product codes) to validate the data against.
Apart from this, these tools can also be used to standardize data formats (date, time, email addresses, etc.,) in large datasets. This can make it easier to analyze and compare data coming from different sources.
Bonus Tip: Outsource Data Support Services To Experts
By following the data cleansing framework and implementing the above-mentioned best practices, businesses can ensure that their resources are utilized efficiently. However, since data cleansing is an ongoing process that requires expertise and time, not all businesses can afford to invest in it. This is where outsourcing data cleansing services to a third-party provider can help businesses streamline their operations and work more efficiently.
Outsourcing companies have dedicated teams of data experts with the required expertise and experience to clean and manage large datasets according to organizational needs. By delegating the labor and time-intensive data cleansing tasks to them, businesses can save resources and easily focus on other core tasks that help them grow better.