What is Data Cleaning and Standardization?

All organizations operate on Data these days. All of that information is used by the leadership team to make decisions every day. The problem is that there is a problem in plain sight: Most of that data is dirtier than anyone would like to admit. Data Cleaning Software helps address these issues by identifying and correcting duplicate customer records, typos, inconsistent date formats, missing data, and outdated contact info that accumulate in the background of databases, costing real dollars.

Gartner estimates that $12.9 million of annual business expense is lost in all industries due to poor data quality. Some of the other studies are even more extensive. According to a study by MIT Sloan Management Review and Cork University Business School, loss of revenue attributable to poor data quality in businesses is between 15 and 25% per year. These are not rounding errors; these are big ones. They are an indication of lost sales, misguided marketing investments, and compliance fines, and are based on incorrect data. AML Software can help organizations maintain cleaner, more reliable data for compliance and financial crime monitoring.

That's why data cleaning and data standardization are now a must-have discipline for businesses that wish to be accurate and fast, whether they are dealing with AML software in India, thousands of material records, or millions of customers for a retailer looking to personalize offers. We'll explain what data cleaning and standardization are, how they are different, what should occur at each phase, what problems companies encounter, and the best practices to help you succeed in having high-quality data that helps you move past spreadsheets filled with data inconsistencies.

What is Data Cleaning?

Data cleansing (also known as data cleaning) is the act of fixing, correcting, and removing errors, inconsistencies, and inaccuracies in a data set. Increasingly, businesses are gathering huge quantities of information from a variety of sources, including CRMs, ERPs, web forms, and third-party vendors, making it difficult to manage the quantity of information and ensure it is accurate.

Data cleaning is basically a method of uncovering issues such as:

• Entries that are not valid, e.g., emails with an incorrect or missing character in them, or phone numbers that are not in the correct format.
• Duplicate records generated when the same customer and/or transaction is entered into the systems more than once
• Incompleteness of essential data item(s) for reporting or compliance
• An old address or an old person's contact who may have left a company
• Misnaming; e.g. same entity named differently in different locations

These problems more often than not begin with data profiling, a systematic study of data patterns and structures which indicates when anomalies have occurred in a data set, before they can propagate more deeply into business processes. Errors are then corrected, either manually by trained data specialists or automatically using data cleaning software that can work at scale and automatically correct large amounts of data.

One of the most popular and costly issues that businesses face is duplicate data. With two different systems, CRM and ERP, storing customer data separately, there are likely multiple versions of the same customer data. This duplication results in confusion within and outside the system. Duplicate data has been shown to cause issues in marketing, lost sales opportunities, lower scores on customer satisfaction, and decreased team productivity. An industry report revealed that over 60% of companies surveyed had a data health score of "not reliable" and about 28% of all emails sent were never received due to poor contact data.

Data cleaning validation is the final data cleaning step. After the erroneous information is checked off and the duplications are deleted, teams must ascertain that the information that must be retained is accurate, current, and usable. This is typically where dedicated data cleaning software will be most useful, if it can detect anomalies in huge data sets much quicker than manual data cleaning could. For organizations working with financial and compliance data, AML Software Vendors also provide solutions that help maintain accurate customer information and support data quality requirements.

What is Standardization of Data?

After the data is "cleaned," it is not necessarily available to all departments or systems for use. This is where data standardization comes into the picture. To standardize cleaned data means to put it in a consistent, common format that can be recognized, shared, and used reliably by all teams, tools, and platforms that interact with it.

Consider the way that a business could store a customer's residence. It could be spelled out in a number of different ways: “California,” “CA,” or “Calif.” All three are referring to the same thing, but if not standardized, systems and reports will view them as being different values entirely. The same issue occurs in date fields, currency symbols, units of measure, product codes, and many other fields.

There are normally three steps in standardization:

1. Data entry points: understanding them. You can't get a standard anywhere without knowing where your data comes from and how it is transferred into your systems.
2. Choosing data standards. Each type of information is formatted according to an agreement, which is referred to as a common data model (CDM), which the organizations agree on in the future. This may involve compliance with well-known standards like ISO 8000, ISO 22745, UNSPSC, or GS1 standards, in regulated or asset-intensive industries.
3. Data Mapping to matrices. When the standards are chosen and the data is already available, or new data is added, it is mapped and indexed to enable it to be retrieved and used in a consistent way in the future.

The advantages of doing it right are great. Standardized data eliminates duplicate data inputs, prevents missed emails and misrouted calls, enhances customer interactions, helps better qualify leads, and makes internal team and external business partner collaboration much simpler. It also provides a foundation for future analytics, as dashboards and models are as stable as the data that goes into them.

Data Cleaning Software vs. Data Standardization

Data cleaning and data standardization are often used synonymously, but they address different types of problems, and typically occur in order.

• Data cleaning software is aimed at the detection and correction of data. Checks for duplicate records, missing fields, incorrect formatting, and errors. Its role is to ensure the accuracy of information in your systems and also to remove clutter from the information.
• Data standardization is about standardization and structure. Its role is to ensure consistency: once it's accurate, it's formatted the same way everywhere it's found, which allows it to flow between systems, departments, and reports without losing meaning.

In reality, however, you must have both, and in that order. The only thing that happens when you standardize dirty data is that you get consistently formatted errors. If data is not standardized, it will result in the same accuracy, but the information cannot be reliably compared and combined with each other and reported on across systems. In industries such as banking and financial services, where AML software in India and around the world relies on clean and standardized customer and transaction data, as well as entity data, to accurately identify suspicious transactions, the importance of this becomes even more pronounced. Even the most advanced AML software can fail to detect links between accounts if a customer's name or ID number is entered differently on each account. Even the most advanced AML software can fail to link accounts if a customer's name or ID number is entered differently from one account to the next, and a bad actor is not as sophisticated as a human investigator.

What Do You Need to Do Specifically to Clean Your Data and Standardize It?

Typical steps for transforming disconnected, messy data sources to clean and standardized sources often include the following:

1. Review and analyze data. Know what data you have, its location, and the accuracy of the data at present. This step would serve as a reference point for the subsequent steps.
2. Establish data quality rules. Determine what the "correct" is for every field, such as the proper format of an email address, the proper number of digits in a cell phone number, or a list of approved country numbers, etc.
3. Deduplicate records. Find duplicate records and determine which is the most up-to-date and accurate, and then combine or eliminate other duplicate records.
4. Verify, correct, and enhance information. Correct inaccuracies and add missing information to records from accurate external sources, if appropriate.
5. Select and use a common data model. Choose the structure and format for each field, and integrate them with industry standards, as applicable.
6. Create a map and index standardized data. Standardize the newly organized data to be able to be accessed and applied across systems and departments.
7. Confirm the final set of data. Before re-commissioning the data, perform a final check for accuracy, completeness, and consistency.
8. Establish ongoing governance. Set up clear responsibility for data quality to prevent new data quality issues and inconsistencies from accumulating without anyone being aware.

Some companies try to do this completely with automated data cleaning software; automation is necessary to deal with volume, but it isn't all. While automated tools are great for identifying anomalies and consistently enforcing rules at scale, they can fall short of recognizing patterns that may seem unusual but are legitimate; context is key. Most successful solutions involve a combination of automated data cleaning software and a team, be it in-house or outsourced, that is familiar with the business context to make decisions that can't be made by the software.

Challenges of Data Cleansing and Data Standardization

Despite having the proper tools, some of the following problems inevitably get in the way for organizations.

• Data silos. If the information is distributed across multiple systems and applications like CRMs, ERPs, spreadsheets, and legacy systems that are not communicating, it's extremely hard to get a single clear view of any customer, product, or transaction. One of the most prevalent data quality issues that organizations experience is data quality being inconsistent across sources, according to Gartner.
• Volume and velocity. In this day and age, businesses produce more data than ever, and it continually comes in at an increasingly quick pace. At scale, of course, it is simply not possible to review individual records, and this leaves organizations with no other choice but to rely on automated data cleaning software, which will also require configuration, monitoring, and periodic re-training.
• Differences in data entry at the source. Near-duplicate and inconsistent records are often generated by free-text fields, where the user types in a product description or customer information according to their personal taste. If there's no format at the entry point, the problem continues to reproduce itself over and over again, even if you clean up existing data multiple times.
• Lack of ownership. Most organizations don't have a clearly defined data steward or governance process, so data quality is just a project to be done periodically, instead of being a discipline. Accountability is essential to good governance, and if it's missing, the same mistakes will reappear within months of a cleanup.
• Regulatory pressure. A variety of regulations, including privacy policies, AML, and financial regulations, are increasingly demanding consistent, accurate data, and incorrect data is no longer just an operational challenge; it's a compliance risk. Financial institutions are particularly at risk, as AML software relies on the standardization and accuracy of data to properly detect risk and suspicious activity.
• Resistance to change. Normalizing data can involve changing existing practices that teams have been used to doing when entering and handling data. It can be more difficult to enact this cultural change than the technical change.
• Striking the right balance between automation and accuracy. Automated data cleaning software can handle massive amounts of data in a relatively short time, but if you're doing a lot of data cleaning all at once without proper configuration, then you might end up with some errors or with more subtle problems that a trained data cleaner would have caught.

Data cleansing and standardization are the most effective practices for data.

• High-quality data practices are common in organizations that continually achieve high-quality data.
• Start with a thorough data audit. Spend time profiling your data and determining the sources and flows of your data and where the biggest gaps and inconsistencies are occurring before implementing any cleansing or standardization technique.
• Scale will be dealt with using automated data cleaning software. Manual review is no longer up to today's demands for data volumes. AI and machine learning-powered tools can automatically detect duplication, notice inconsistencies, and correct records, relieving human specialists of what can be a judgment-heavy task.

Best Practices for Data Cleansing and Standardization

Organizations that consistently have high-quality data will engage in the same set of practices.

• Do a comprehensive data audit. Take time to profile your data and understand its sources, flow within your systems, and where the largest gaps and inconsistencies lie before applying any cleansing or standardization technique.
• Automated Data Cleaning Software to clean data at scale. Manually reviewing data is not sufficient to handle the volume of data in today's world. AI and machine-learning-powered tools can recognize duplicate data and identify inconsistencies, as well as correct data according to pre-defined rules, freeing up the human specialists for more judgmental tasks.
• Incorporate governance in the process, not just the clean-up. Having a governance model that establishes roles, responsibilities, and processes for data quality over time is more appropriate than simply tackling it as a one-time project. Appointing specific data stewards establishes accountability and transparency for data stewards across the organization.
• Implement accepted data standards. If you can, make your common data model conform to existing models relevant to your field, such as ISO standards for materials data, or existing standards for customer or financial information. This enables seamless partnerships and vendor and regulator integration.
• Use uniform classification systems. Whether it's customer segments, product types, or service categories, the consistent classification and description of data provide greater accuracy in reporting and a more reliable analysis across departments.
• Prevent data entry quality issues. Better to stop bad data from entering your systems than to clean it up later! Many errors are prevented when they are created because they are prevented in the structured entry fields, through validation rules, and using defined naming conventions.
• Validate before deployment. Don't consider a cleansing or standardization project complete until the final set of data is accurate and complete. This is the reason why working without a plan is a bad idea: You run the risk of reopening the same issues you were trying to address.
• Continuously track the quality of data. Information is not clean by itself. Continuous monitoring and regular audits will detect any new faults and drift before they turn into larger faults.
• Be aware of when to seek additional help. A task that can be time-consuming without proportional benefit in terms of return on investment can benefit from dedicated staff, a proven track record from agencies, and specialized data cleaning software service providers. This is especially beneficial for companies that have to deal with sensitive data, such as financial institutions using AML software in India, as it helps their internal teams concentrate on their main tasks, providing the data required for regulatory requirements.

Why This Matters More Than Ever

Data cleansing and data standardization were once a back-office housekeeping task that was performed by IT without much fanfare, whilst the “show” was on the road to growth. This perspective is quickly being outweighed. The quality of the data has become a boardroom issue as more organizations rely on data to drive decisions with tools like analytics, automation and AI. Inaccurate data no longer just skews a particular report; it can subtly impact forecasting models, personalization engines, risk scoring systems, and strategic investments.

It's even more critical if you're in any industry that's closely monitored for its operations. In India and around the world, financial institutions rely on the reliable detection of suspicious activity and the ability to remain compliant with ever-changing regulations by leveraging clean and standardized data when implementing AML software. In that case, a duplicate customer record or an entry that isn't formatted properly is not only a nuisance, but it could also be a compliance issue.

The positive side of that is you can do all of this without a complete transformation of your data strategy overnight. It begins with an honest assessment of the state of your data, the mix of automated data cleaning solutions and human oversight, and a solid approach to data governance to prevent it from decaying again, as well as adopting a standard structure for data that's already proven and accepted. Those companies that do not see data cleaning and standardization as a "one-and-done" sort of thing, but rather as a way to maintain a trustworthy data set and make decisions upon it that do not fail, are the ones that end up with data they can rely on and decisions that will stand the test of time.

Also Read: What is Data Cleaning? A Comprehensive Overview

Conclusion

Data cleaning and standardization are essential for maintaining accurate, consistent, and reliable business data. As organizations continue to depend on data for analytics, automation, AI, and decision-making, poor-quality data can affect everything from customer interactions and reporting to compliance and risk management.

Using automated data cleaning software can help organizations identify duplicates, correct inconsistencies, and manage large volumes of data efficiently. However, technology alone is not enough. Combining automation with human oversight, clear data standards, and ongoing data governance helps prevent data quality issues from returning.

For organizations handling sensitive financial and compliance data, clean and standardized data is especially important for supporting accurate risk detection and AML processes. Treating data quality as an ongoing discipline rather than a one-time cleanup allows businesses to build a more reliable foundation for better decisions and long-term growth.

Ixsight provides Deduplication Software that ensures accurate data management. Alongside, Sanctions Screening Software and Data Cleaning Software are critical for compliance and risk management, while KYC Risk Scoring enhances data quality. Additionally, CKYCRR 2.0 Upload Software supports streamlined regulatory reporting and seamless compliance processes, making Ixsight a key player in the financial compliance industry.

FAQs