Data cleaning is one of the most crucial steps to ensure data quality and database integrity. It efficiently allows managing data while determining reliability while making decisions. As the regulatory compliances are becoming more stringent and focused, ensuring high data quality is the need of the hour.
Given that organisations have a lot of data internally and externally, and that most of this data is not clean, it may result in errors while running programs that may lead to revenue loss and more. Data management best practices are, therefore, crucial for better analytics.
Some of the benefits of data cleaning are:
- It accelerates data governance while reducing time and cost of implementation to maximise ROI
- Accurately target customers and drive faster customer acquisition
- Consolidate applications and cost-saving
- Improves decision-making capabilities as it supports better analytics
- It saves valuable resources by removing duplicate and inaccurate data from databases, keeping valuable resources in terms of storage space and processing time
- It boosts productivity as it saves time in re-analysing work due to mistakes in data and saves from making incorrect decisions
Best Practises For Data Cleaning
Chalk Out A Plan
Talking about data cleaning, one of the first steps is to carry data profiling which helps in filtering out data and identifying outlier values or spot problems in data that was collected. Once the profiling is done, it normalises the field, de-duplicates it, removes obsolete information and more. While profiling is the first step, what follows next is asking these questions to carry out best practices.
- What are our goals and expectations
- How will the execution carried out
- What are the benefits in terms of ROI
- Where are the data sets captured from
- How to standardise data
- How to validate the data
- How to test and monitor data quality
- Are the expectation realistic
- What is the cost and more
Having responses to these questions will help in chalking out an overall plan and strategy to carry out data cleaning.
Uniform Data Standards Is The Way
For data cleaning, having a uniformed data standard can bring about better results. It helps in improving the initial data quality, thereby easing the steps further. It creates decent quality of data which is easier to clean than data which is low quality. Correction at the data entry point can be the most crucial steps in ensuring overall data cleaning. To ensure data standards, many companies believe in creating data entry standards documents which help in the long run.
Validating The Accuracy Of Data
The data that is collected and captured should be authentic to avoid errors in programs and avoid re-runs. Data should be able to meet the required standards, and the source should be accurate. While it is a crucial step, and can significantly improve the overall quality of data sets, the process can be complicated and challenging. Especially while dealing with large datasets. One of the effective ways is to develop a script or validate small data at a time. It also helps in removing duplicates, identifying obsolete records and other errors in the dataset.
Identifying & Adding The Missing Data
The next step after you have validated the data comes in the step of appending the data that is missing. Cross-referencing multiple data sources and combining known data into a final data set that is far more useful and valuable to you will help. This step is essential in order to provide complete information for business intelligence and analytics. Once the usability of the dataset is checked, the whole data cleaning process should be automated to avoid human error, saving significant time and money.
Monitoring the System
While setting up automation is crucial, monitoring the whole data cleansing process is highly essential. It checks the overall health and effectiveness of the system. It also checks if the data is meeting standards and that the procedures have been followed correctly. Implementing periodic checks will keep the situation in control.
In A Nutshell, Some Of The Steps That Will Help In The Long Run Are
- Sort data by different attributes such as negative numbers, strings, and other outliers
- For large datasets, breaking them into small datasets can work wonder to improve iteration speed
- Look at summary statistics such as mean, standard deviation, number of missing values, etc., for each column. These can be used to quickly solve the most common problems
- Creating a set of utility functions, tools, and scripts can help solve problems. Some of the ways are remapping values based on a CSV file or SQL database and more
- Keeping a track of cleaning operations is important so that changes can be carried out effectively. Verify data by re-checking sources is also crucial
- Use up-to-date and public sources to acquire contact which can be time-consuming but effective
- Conduct data cleansing frequently in a few months to ensure high quality and consistency
