Data can help calculate your return on investment. Ensuring that it is correct, consistent, and useable is known as data cleaning.
Data quality is vital no matter what form of data you work with, whether it’s telematics or not. Therefore, consider making data cleaning a regular part of your routine.
What does data cleaning mean?
The process of making sure that your data is consistent, correct, and useable is known as data cleaning. You can accomplish this by looking for errors or corruptions and correcting or deleting those errors. In addition, you can manually process data to avoid repeating mistakes.
Software solutions can help with most data cleaning. However, some tasks require manual work. It’s true that data cleaning can be a daunting task. However, it is an important part of managing company data.
What are the advantages of cleaning data?
There are many advantages:
- It eliminates severe inaccuracies that are unavoidable when many sources of data come together into a single dataset.
- Using data cleaning tools will make everyone on your team more productive. This is because you’ll be able to rapidly extract what you need from the data you have.
- Customers will be happier and you annoy fewer employees if there are fewer faults.
- Data cleaning enables you to map various data functions. This, in turn, allows you to better understand what your data does and where it comes from.
Cleaning Data in Six Simple Steps
Before beginning a data cleaning process, it’s important to take a step back and consider the big picture. For instance, what are your objectives and expectations? Furthermore, how do you plan to achieve them?
Next, you must devise a data cleanup approach in order to fulfill your objectives. Focusing on your top KPIs is a good rule of thumb.
Here are some questions to consider.
- How is your data going to help you achieve your most important metric?
- What is your company’s overall objective?
- What do you want each member to get out of it?
Getting the main stakeholders together and brainstorming is an excellent place to start. When it comes to creating a data cleansing process, here are the six solid steps.
1. Keep an eye on the data errors.
Keep track of the patterns that lead to the majority of your errors. This will make finding and fixing faulty data much easier. If you’re integrating other software with your fleet management software, record-keeping is going to be extremely vital. That way, your mistakes don’t clutter up the work of other departments.
2. Standardize your data cleaning procedure.
In order to help lower duplication issues, the point of entry must be standardized.
3. Verify the accuracy of the data.
Validate the accuracy of your data after you’ve done the cleaning up of your existing database. In addition, you will want to invest in data-cleaning mechanisms that facilitate real-time use. Further, some solutions even employ AI or machine learning to improve accuracy testing.
4. Look for duplicate data.
Looking for duplicates when in the process of examining your data saves time. Furthermore, it’s important to understand what kind of solutions you need. Then, you can intelligently purchase any alternative cleaning solutions. They should be solutions able to review raw data. Furthermore, they must be able to use automation in the process in order to avoid repetition.
5. Examine the information you have.
Use good sources to augment your data after you standardize, vet, and clean it for duplicates. Reliable data sources should be able to retrieve the necessary data. After that, they can clean it up, getting it ready for business analytics.
6. Keep in touch with your coworkers about the data.
To encourage acceptance of the new technique, share the new standard cleaning process with your staff. It’s critical to keep your data clean. Maintaining communication with your team will aid in developing and strengthening customer segmentation. In addition, it will help with the sending of more targeted information to consumers and prospects.
Finally, keep an eye on data and examine it on a frequent basis to discover irregularities. Data can help you calculate your return on investment.
Conclusions
If you’re in charge of data management, don’t forget to clean it up. Keeping track of regular and correct inputs is a vital chore that must be done on a daily basis.
However, the procedures above should make creating a daily regimen easier. In addition, after you finish your process, you can confidently use the data for deep operational insights.
You can do this knowing that it’s now accurate and dependable. Clean and correct data is crucial for any business operation using data to drive marketing plans.
