When you are creating a data cleaning strategy plan, it is important to look at the big picture as well as your unique situation. What are your goals and expectations? What are your current struggles? How will you execute the plan?

An effective strategy will depend on your unique situation. However, we will walk through the general steps to begin with. The data cleansing strategy below is a great starting point but for more specific guidance, our data experts can help you.

1. Implement a Data Quality Strategy Plan

So what are the best practices for data cleaning? The first step is to create a data cleaning plan and strategy. This can sound overwhelming at first. However, start at the highest level. Ask your key stakeholders the following questions and let the answers illuminate the path forward:

Questions to Ask:

• What benefits could we see by using higher quality data?

• Can we calculate the ROI of investing in data quality improvements?

• What types of data do we capture on a regular basis?

• What types of data do we base important business decisions on?

• How are these data sets captured?

• Who captures this data?

• What standards for data capture do we currently use, if any?

• Do we catch errors and issues during data capture?

• How can we standardize the data that we capture so that it’s cleaner?

• Where do most of the errors in our data occur?

• How do we clean our data, overall?

• What methods do we use to validate our data?

• How do we append, or combine, our data from multiple sources?

• Are there opportunities to append, or combine, our data sets in unique ways that would empower better decisions?

• What automation do we currently use for data? What automation would greatly improve our data systems?

• How do we test and monitor our data quality?

• How do we assess the accuracy of our business decisions?

• Who is accountable for our data quality?

By asking these questions, you will start to see the current state of your processes. You will also start to see what can be improved. With these answers, you can put together an overall plan and strategy.

It is important to also identify your goals and objectives before you move forward. Are your expectations realistic? Is it worth the cost? Of all the data cleaning best practices, this step is probably the most critical.

2. Standardise Data at the Point of Entry

It is important to create uniform data standards at the point of data entry. In other words, create standards for how data is initially captured.

Screening data in this way can greatly improve its initial quality. It is far easier to clean data that is already of decent quality versus trying to clean data that is very low quality. Therefore, the highest ROI for data improvements can typically be found at the data entry point.

Implementing changes can be challenging for organisations that already have an embedded and highly active data entry process. However, effective communication and enforcing data standards can help achieve uniformity across the organisation.

For example, standardising contact data when it is initially captured can be accomplished by identifying errors at their first occurrence. Software makes this much easier. When any data is entered into a system, ensure that the data meets the required standards.

Data Entry Standards Document

One of the best practices for data cleansing is to create a Data Entry Standards Document (DES) and share it across the organization. Moreover, update new employee training to incorporate these standards and re-train existing employees as needed. In addition, implement software or other checks to ensure compliance with the DES.

At the point of data entry, the objective should be to identify inconsistencies, inaccuracies, and duplicate records. You can alert the operator or even implement software that resolves these issues automatically.

3. Validate the Accuracy of Data

Now that we have set standards for data that is being captured, the next step is to validate its accuracy. We need to validate the data to make sure that it meets the required standards. If it does not, we need to alert the operator or even fix it on the spot.

One purpose of data validation is to assess the accuracy and consistency of the data being captured. Accuracy and consistency can only be measured by comparing the data to another accurate source. This source needs to be correct, otherwise, we have no way to know that that the new data is also accurate.

By implementing data validation techniques on the front end when data is being initially captured, we can greatly improve the overall quality of our data sets. However, this can be complicated and challenging depending on the situation.

In addition, for large, messy datasets, reaching 100% validation is next to impossible. It is therefore important to have realistic goals. Moreover, you should consider a cost/benefit analysis when developing your goals for data validation.

Data validation can also take place after initial data is captured. This is a great strategy in situations where you cannot perform validation in real time. If you are dealing with a large data set, develop a script or approach that can validate a small data set at a time. This is much easier to scale up vs. trying to fix an entire data set at the same time. It can also allow batch processing.

Additionally, an effective validation strategy will include the ability to remove duplicates, identify errors and update obsolete records in data sets that are already captured.

4. Append Missing Data

After your data has been standardized and validated, you can append missing data. This simply means cross referencing multiple data sources and combining known data into a final data set that is far more useful and valuable to you.

This step is important to provide more complete information for business intelligence and analytics. It can put the different puzzle pieces together for your business.

5. Implement Automation

Once you have implemented data standards at the point of data entry, executed an effective data validation process, are appending data to increase the overall value and usability of your data sets, then it’s time to streamline the process even more. You can do this through automation.

Automation is one of the best ways to reduce human error. In addition, it can save a significant amount of time, saving you a lot of money. One example of automation would be automated database scrubbing. There are automation experts out that can guide you through the best way to do this based on your situation.

However, it is important to remember that automation should never be the first step. It’s critical to have a proven process in place before you try to automate everything.

6. Promote Data Quality Practices across your Organisation

To become a data-driven organisation, the whole organisation needs to buy in, regardless of job role. If possible, an organisation should train their workforce on the importance of clean data as well as the how the data processes work.

By sharing this information, employees will be better informed and more enthusiastic about helping the processes succeed. You may find that employees may even be able offer their own ideas on how to improve the system.

7. Monitor the Data Cleaning System.

Once automation has been achieved, it is important to monitor the entire process. Identify some key metrics to assess the health and effectiveness of the system.

Also identify ways to sample test data randomly to ensure that it is meeting the standards that have been established. Finally, you can also implement some test cases to see what decisions would be derived from various sample data sets to ensure that they are correct. Back testing is a great way to achieve this.

Data cleaning should be an endless loop. Consistent monitoring keeps this loop stabilized.

Implement periodic checks on your data cleaning process based on the situation. These can be weekly, monthly or even daily, depending on your needs and the availability of resources.

Finally, watch for changing situations in the process that require adjustments in processes or automation.

How to Measure the Success of a Data Cleaning System

Here are some ways to measure the success of a data cleaning system:

• Does the system detect/identify and remove or even correct major errors and inconsistencies?
• Does the system successfully use tools, scripts, and automation to reduce manual inspection of data?
• Is the system improving the overall quality of data?
• Are better decisions being made since the system was introduced?
• Is the system saving time and money, while improving data quality?

Conclusion

Data cleaning is vital to the success of any data-centric business activities. In this guide, we have discussed what data cleaning is, why it is important, and how to create a successful data cleaning strategy plan and system. We also discussed the best practices in data cleansing systems.

As you start to implement a data cleaning strategy, you may find that you require expert guidance to ensure successful implementation. Our data experts would love to help you and your business with your digital transformation efforts. Contact us today to discover the true power of your data.