Data has become the life of the corporate world. Not only this, it’s positively impacting almost every niche or domain. Businesses are actually leveraging the benefits of datasets.
But, how trustworthy such records are?
In a survey, IBM has projected a loss of around $3.1 trillion to the US economy because of bad data every year. Bad data often feed bad decisions. In addition, organizations lose money, which is sometimes more than that of expectation.
So, what can enterprises do to actually come across this major problem?
Well, it’s typically related to data cleansing. It helps to fix issues and also administer some technical and organizational issues.
How to Carry Out Data Cleansing Process
Here is an ideal way to manage data and run a data cleansing process.
- Must-Have Data Governance
Cleaning your files or records once in a blue moon won’t be helpful in the long run. You need to focus on its clean and comprehensive structuring. If there are any issues with it, try to sort them out quickly. Hire or deploy an experienced data professional or team to fix quality issues within the aligned department.
Aligning these responsibilities to a non-technical professional can end up in bad data. It is simply because he won’t have the expertise and skills to introduce accuracy. Nor would he know how to carefully process documents or files without compromising their quality.
Some organizations that are really concerned about this problem focus on data governance. They allocate experienced data scientists and MIS experts to deal with fixing, which can be related to enrichment, de-duplication, normalization, standardization, etc. The senior matter expert holds the authority to manage the data quality team effectively within their particular departments and areas. Together, it consistently cleans the data.
This data governance team is set up to improve the quality of records. With seamless files, it’s easy to trust those record-based findings and discoveries. This is easier if you don’t have any duplicates in your database and its all-time availability over the server. The quality team can set up goals related to data quality, such as having valid email IDs or, similar benchmarks.
- What Quality Issues
Quality issues can occur due to manual data entry errors and technical faults. Mainly, mistakes happen when you don’t focus on profiling the entire database. Once you do, integrating data cleansing solutions for your business become like a walkover.
The main reasons of quality issues are avoiding data as the asset of your company, ignoring data quality monitoring, and not devising quality best practices. These issues can be resolved if you focus on the causes.
- Sort Out Quality Issues
This step is dedicated to assessing the quality of your files and records. Here, technology appears in a major role. There are multiple cleaning tools, which enable quick profiling. Mostly, organisations start with scanning and finish with summarizing the statistics of the entire database.
Profiling makes it easier. Experts can examine its structure and use basic statistical analysis. It will highlight incomplete data fields, duplicates, null values, and anomalies. Now, the need is to complete and do the fixing by filling in the right fit value, such as postal codes, social security or nationality number, etc. It will help you to identify values that don’t fit the profile format.
Using a data cleansing tool certainly helps in cleaning the entire database in a few minutes. There are smart algorithms that comb through the preset records and find out the problems. Let’s say, your database has “Bob Sawyer” and “Bom Sawyer” with the same email IDs. The AI-driven cleansing tools are smart enough to discover this duplicate entry and correct it with an accurate record in its internal libraries or master data. In the nutshell, these tools are able to validate data entry in your libraries or records by looking up regularly refreshed databases.
There are some smart algorithms that support data quality measuring tools. With them, they hardly take a few seconds to filter typos or any missing entries. These algorithms have the capacity to comb through multiple fields of a relational database and find associated records. It helps in assigning a probability of such records. There are some exceptions when manual intervention is a must-have. The scope is always there to set up some algorithms that can cover match probability to a great extent without needing any humans.
Simply put, these tools are an amazing discovery that can narrow down results in the blink of an eye.
- Set Data-based Rules for Business
Once you’re done with data handling, move ahead and set some standards or rules with which the data should comply with. Let’s say, setting a format of lead collection, which has names, email IDs, and inquiries can make the data cleaning process way easier.
Focus on these metrics when you set rules for data quality mapping.
- Accuracy. It’s related to the correctness of the data as if it’s accurate.
- Completeness. Find out if your database has all attributes that are actually required for reference to other data entries.
- Consistency. Ensure that all data elements are ideally identical even if they are stored across different systems and warehouses of your organisation.
- Timeliness. Determine if your records are up-to-date and accessible to the actual users when required.
- Conformity. Find out if the data comply with your organization’s master database, which specifies values and specifications, including standards on data type, size, and format.
- Integrity. Discover if all relational datasets are connected across different databases of your enterprise. And ensure that they would be discoverable and reliable.
- Uniqueness. Find if all records are genuine or if there are any duplicate values inside your database.
It also helps in determining data sources and related discrepancies, like duplicates and missing records. Master data help algorithms to move smoothly ahead and find anomalies or errors accordingly, even if they are scattered across many systems. So, it’s really important to have excellent quality data.
- Strategise Process & Reporting
Now that you have developed the quality and set rules, it’s important to strategise how to go on further. This is vital because you cannot compromise the quality. Nor can you clean data entries now and then. With feasible strategies in place, you can have quality at source for a long time. For example, the antivirus software automatically detects malicious attacks for a long time unless any new type of cyber attack is evolved (which is not in its database). It scans through the data patterns and identifies malicious attempts accordingly.
Like the process, reporting is also important. You should set up reporting systems that can help you find flaws in the quality quickly. The report proves an incredible source of determining exactly where the data quality declined. So, you have an easy way to discover why it is so. Later, fixing that error and its root cause becomes easier because of strategising all things.
There are certain ideal ways to carry out the data cleansing process. These ways can be defining governance of data, identifying errors and their causes, settings rules to map quality issues, strategizing quality measuring processes, and setting reports in a comprehensive manner.