Data cleaning is the process of ensuring data is correct, consistent and usable. You can clean data by identifying errors or corruptions, correcting or deleting them, or manually processing data as needed to prevent the same errors from occurring.
Data cleaning is not simply about erasing information to make space for new data, but rather finding a way to maximize a data set’s accuracy without necessarily deleting information.
For one, data cleaning includes more actions than removing data, such as fixing spelling and syntax errors, standardizing data sets, and correcting mistakes such as empty fields, missing codes, and identifying duplicate data points. Data cleaning is considered a foundational element of the data science basics, as it plays an important role in the analytical process and uncovering reliable answers.
6 Steps to Clean the Data
1. Monitor errors
Keep a record of trends where most of your errors are coming from. This will make it a lot easier to identify and fix incorrect or corrupt data. Records are especially important if you are integrating other solutions with your fleet management software, so that your errors don’t clog up the work of other departments.
2. Standardize your process
Standardize the point of entry to help reduce the risk of duplication.
3. Validate data accuracy
Once you have cleaned your existing database, validate the accuracy of your data. Research and invest in data tools that allow you to clean your data in real-time. Some tools even use AI or machine learning to better test for accuracy.
4. Scrub for duplicate data
Identify duplicates to help save time when analyzing data. Repeated data can be avoided by researching and investing in different data cleaning tools that can analyze raw data in bulk and automate the process for you.
5. Analyze your data
After your data has been standardized, validated and scrubbed for duplicates, use third-party sources to append it. Reliable third-party sources can capture information directly from first-party sites, then clean and compile the data to provide more complete information for business intelligence and analytics.
6. Communicate with your team
Share the new standardized cleaning process with your team to promote adoption of the new protocol. Now that you’ve scrubbed down your data, it’s important to keep it clean. Keeping your team in the loop will help you develop and strengthen customer segmentation and send more targeted information to customers and prospects.
Different types of Data issue:
Duplicate data: There are 2 or more identical records. This may cause misrepresentation of inventory counts/duplication of marketing collateral or unnecessary billing activities.
Conflicting Data: When there are same records with different attributes, it means data is conflicting. For example, a company with different versions of addresses may cause delivery issues.
Incomplete Data: The data that has missing attributes. Payrolls of employees may not be processed due to their missing social security numbers in the database.
Invalid Data: Data attributes are not conforming to standardization. For example, 9 digit phone number records rather than 10 digits.
Causes of Data Issues:
Data issues arise due to technical problems such as:
Synchronization issues: When data is not appropriately shared between two systems, it may also cause a problem. For example, a banking sales system captures a new mortgage but fails to update the bank’s marketing system, then the customer may confuse if they get a message from the marketing department.
Software bugs in data processing applications: Applications can write data with mistakes or overwrite correct data due to various bugs.
Information Obfuscation by users: It is the concealment of data by purpose. People may give incomplete or incorrect data to safeguard their privacy.
Data Cleaning Techniques
As is the case with many other actions, ensuring the cleanliness of big data presents its own unique set of considerations. Subsequently, there are a number of techniques that have been developed to assist in cleaning big data:
1. Conversion tables: When certain data issues are already known (for example, that the names included in a dataset are written in several ways), it can be sorted by the relevant key and then lookups can be used in order to make the conversion.
2. Histograms: These allow for the identification of values that occur less frequently and may be invalid.
3. Tools: Every day major vendors are coming out with new and better tools to manage big data and the complexities that can accompany it.
4. Algorithms: Such as spell check or phonetic algorithms can be useful – but they can also make the wrong suggestion.
Removal of errors when multiple sources of data are at play.
Fewer errors make for happier clients and less-frustrated employees.
Ability to map the different functions and what your data is intended to do.
Monitoring errors and better reporting to see where errors are coming from, making it easier to fix incorrect or corrupt data for future applications.
Using tools for data cleaning will make for more efficient business practices and quicker decision-making.
The Tech Platform