What is Data Remediation and Why is it important?
Businesses need to identify and correct errors, inconsistencies and inaccuracies in data to ensure quality and accuracy.
What is Data Remediation?
Data remediation refers to the process of identifying and correcting errors, inconsistencies, and inaccuracies in data. This can include tasks such as removing duplicate records, standardising format and data types, and filling in missing values.
What causes these problems in data?
There are many factors that can cause data breaks or errors. Some common causes include:
- Human error: Data entry or manual data processing can lead to mistakes, such as typos or transposition errors.
- Systematic errors: These can occur due to issues with the systems or processes used to collect or store data, such as data loss or data corruption when being transmitted between systems.
- Data format issues: Data may not be in a format that can be easily understood or processed by a computer, leading to errors or inconsistencies. For example, data captured as free text, or where no clear consistency in formatting can be easily recognised.
- Inconsistencies in data collection: Data collected from different sources or at different times may not be consistent, leading to inconsistencies and errors. One system might predicate that names be stored in one cell in a table, but another might have them stored in separate cells, and a further system might separate them with commas to indicate they are different data elements. These inconsistencies are difficult to overcome.
- Data duplication: Data can be duplicated within a dataset, resulting in multiple records for the same data point. A person might feature twice in a system due to having more than one financial product, but with differing address data recorded against their name. Or a company might be misspelled in a system, creating the illusion of two different companies when they are in fact the same entity. This issue is sometimes leveraged by fraudsters trying to outwit computer systems.
- Missing data: Data may be missing or incomplete, which can lead to inaccuracies. It might have been overlooked at input, deliberately withheld, or accidentally omitted when being sent from one party to another.
- Data validation: Inadequate or no data validation can lead to errors or inaccuracies in data. Many firms capturing data lack the capability to validate that the data is true, complete and accurate when it’s being recorded. For example, a customer could input an invalid post or ZIP code, and without validation against a trusted source of address data, the error can lie undetected in a data system, compromising many business processes.
Why is remediating the data important?
It is important because inaccurate or inconsistent data can lead to incorrect conclusions or decisions, and can also affect the performance of machine learning models. Ensuring the quality and accuracy of data is crucial for organizations that rely on data-driven insights to inform their operations and strategy.
Additionally, firms will need to rely on a consistent and valid dataset to be able to conduct activities that counteract financial crime, money laundering and provide sound risk management. One of the reasons why the financial crisis of 2008 was so impactful on the population at large was the inability to detect the various entities that were part of a larger enterprise, linked together by the same owners or assets.
The impact of sanctions listings, where firms are expressly forbidden from doing business with individuals or entities from specific countries, is another area where bad data hampers efforts to combat crime. The Russian invasion of Ukraine in 2022 triggered international sanctions designed to limit Russian individuals and companies from doing business, but again the data needed to enforce these sanctions has to be good enough to be relied upon and avoid the penalties that would otherwise ensue.
How can a business user make use of Datactics Augmented Data Quality (ADQ) to remediate broken, inaccurate or inconsistent data?
Datactics ADQ is a software solution that can be used by businesses to improve the quality and accuracy of their data. Business users can make use of Datactics to fix broken data in several ways:
- Data Profiling: Users can profile their data and identify errors, inconsistencies, and inaccuracies. This can help them understand the nature of their data breaks.
- Data Cleansing: Business users can use Datactics to cleanse their data by removing duplicates, standardising format and data types, and filling in missing values. The built-in data remediation features in Datactics ADQ ensure data stewards and users can repair, update and standardise data.
- Data Matching: Using our platform, users can match data from different sources and ensure consistency across their data sets.
- Data Validation: Users can use Datactics to validate their data by implementing validation rules and checks to ensure that the data adheres to certain standards.
- Data Enrichment: Business users can use Datactics to enrich their data by supplementing it with additional information from external sources.
By using Datactics, business users can improve the quality and accuracy of their data, which in turn can help them make better decisions and improve the overall performance of their organisation. The platform empowers business users to demonstrate return on investment through improved data quality and enhanced decision-making.
