What is Data Quality and why does it matter?

 

Data Quality refers to how fit your data is for serving its intended purpose. Good quality data should be reliable, accurate and accessible. 

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A data quality firewall is a key component of data management. It is a form of data quality monitoring, using software to prevent the ingestion of messy or bad data.

It’s a set of measures or processes to ensure the integrity, accuracy, and reliability of data within an organisation, and helps support data governance strategies. This could involve controls and checks to prevent the entry of inaccurate or incomplete data from data sources into data stores, as well as mechanisms to identify and rectify any data quality issues that arise. 

In its simplest form, a data quality firewall could be data stewards manually checking the data. However, this isn’t recommended, as it’s considerably inefficient and could cause inaccuracies.  Instead, a more effective approach is the use of automation.

An automated approach

Data quality metrics (e.g. completeness, duplication, validity etc.) can be generated automatically and are useful for identifying a data quality issue. At Datactics, with our expertise in AI-augmented data quality, we understand that the most value is derived from data quality rules that are highly specific to an organisation’s context. This includes rules focusing on Accuracy, Consistency, Duplication, and Validity. The ability to execute all the above rules should be a part of any data quality firewall. 

The above is perfectly suited to an API giving an on-demand view of the data’s health before ingestion into the warehouse. This real-time assessment ensures that only clean, high-quality data is stored, significantly reducing downstream errors and inefficiencies.

What Features are Required for a Data Quality Firewall? 

The ability to define Data Quality Requirements 

The ability to specify what data quality means for your organisation is key. For example, you may want to consider whether data should be processed in situ or passed through an API, depending on data volumes and other factors. Here are a couple of other questions worth considering when defining data quality requirements- 

• Which rules should be applied to the data?  It goes without saying that not all data is the same. Rules which are highly applicable to the specific business context will be more useful than a generic completeness rule, for example. This may involve checking data types, ranges, and formats, or validation against sources of truth. Reject data that doesn’t meet the specified criteria.
• What should be done with broken data? Strategies for dealing with broken data should be flexible. Options might include quarantining the entire dataset, isolating only the problematic records, passing all data with flagged issues, or immediately correcting issues, like removing duplicates or standardising formats. All the above should be options for the user of the API.  The point is, not every use case is the same and a one-size-fits-all solution won’t be sufficient. 

Key DQ Firewall Features:

Data Enrichment 

Data enrichment may involve adding identifiers and codes to the data entering the warehouse. This can help with usability and traceability. 

Logging and Auditing 

Robust logging and auditing mechanisms should be provided. Log all incoming and outgoing data, errors, and any data quality-related issues. This information can be valuable for troubleshooting and monitoring data quality over time. 

Error Handling 

A comprehensive error-handling strategy should be provided, with clearly defined error codes and messages to communicate issues with consumers of the API. Guidance on how to resolve or address data quality errors is provided. 

Reporting 

Regular reporting on data quality metrics and issues, including trend analysis, helps in keeping track of the data quality over time.

Documentation 

The API documentation should include information about data quality expectations, supported endpoints, request and response formats, and any specific data quality-related considerations. 

How Datactics can help 

You might have noticed that the concept of a Data Quality Firewall is not just limited to data entering an organisation. It’s equally valuable at any point in the data migration process, ensuring quality as data travels within an organisation. Wouldn’t it be nice to know the quality of your data is assured as it flows through your organisation?

Datactics can help with this. Our Augmented Data Quality (ADQ) solution uses AI and machine learning to streamline the process, providing advanced data profiling, outlier detection, and automated rule suggestions. Find out more about our ADQ platform here.

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Year after year, the volume of data being generated is increasing at an unparalleled pace. For businesses, data is critical to inform business strategy, facilitate decision-making, and create opportunities for competitive advantage.

However, leveraging this data is only as good as its quality, and traditional methods for measuring and improving data quality are struggling to scale.

This is where Augmented Data Quality comes in. The term describes an approach that leverages automation to enable systems to learn from data and continually improve processes. Augmented data quality has led to the recent emergence of automated tools for monitoring and improving data quality. In this post, we’ll explain what exactly is augmented data quality, where it can be applied, and its positive impact on data management. 

Why Are Traditional Approaches Struggling? 

First, let’s set the scene. With an ever-growing reliance on data-driven decision-making, businesses are looking for ways to gain accurate insights, deep business intelligence, and maintain data integrity in an increasingly complex business environment.

However, measuring data quality is challenging for enterprises, due to the high volume, variety, and velocity of data. Enterprises grapple with ensuring the reliability of data that has originated from multiple sources in different formats, which can often lead to inconsistencies and duplication within the data.

The complexity of data quality management procedures, which involve data cleansing, integration, validation, and remediation, further increases the challenge. Traditionally, these have been manual tasks carried out by data stewards, and/or using a deterministic-based approach, both of which are not scalable as the volume and veracity of data grows.  Now, enterprises are turning to highly automated solutions to effectively handle vast amounts of data and accelerate their data management journey and overall data management strategy.

What Is Augmented Data Quality? 

Augmented Data Quality is an approach that implements advanced algorithms, machine learning (ML), and artificial intelligence (AI) to automate data quality management. The goal is to correct data, learn from this, and automatically adapt and improve its quality over time, making data assets more valuable. 

Augmented data quality promotes self-service data quality management, empowering business users to execute tasks without requiring deep technical expertise. Moreover, it offers many benefits, from improved data accuracy to increased efficiency, and reduced costs, making it a valuable asset for enterprises dealing with large volumes of data. 

Although AI/ML solutions can speed up routine DQ tasks, they cannot fully automate the whole process. In other words, augmented data quality does not eliminate the need for human oversight, decision-making, and intervention; instead, it complements it by leveraging human-in-the-loop technology, where human expertise is combined with advanced algorithms to ensure the highest levels of data accuracy and quality.

“Modern data quality solutions offer augmented data quality capabilities to disrupt how we solve data quality issues. This disruption – fueled by metadata, artificial intelligence/machine learning (Al/ML) and knowledge graphs – is progressing and bringing new practices through automation to simplify data quality processes.”

-Gartner^®, ‘The State of Data Quality Solutions: Augment, Automate and Simplify; By Melody Chien, Ankush Jain, 15 March 2022.

GARTNER is a registered trademark and service mark of Gartner, Inc. and/or its affiliates in the U.S. and internationally and is used herein with permission. All rights reserved.

How Can Augmented Data Quality Help A Data Quality Process?

Routine data quality tasks, such as profiling and rule building, can be time-consuming and error-prone. Fortunately, the emergence of augmented data quality has revolutionized the way routine data quality tasks as performed, reducing manual effort and saving time for users. Below are some examples of where automation can add value as part of a data quality process:

Data profiling and monitoring

ML algorithms excel at recognizing patterns. For example, ML can enhance a system’s capability to manage data quality proactively, by identifying and learning patterns in data errors and corrections. Using these learnings, ML can be applied to automate routine tasks like data cleaning, validation, and deduplication.

Data Deduplication

ML can be used to identify and remove duplicate entities. Rather than simply looking for exact matches, ML algorithms such as  natural language processing can identify duplicates even with minor variations, such as spelling mistakes or different formats.

Automated Validation

ML can be used to automate the data validation process. For a feature such as  automated rule suggestion, the system applies ML to understand underlying data and match relevant rules to this data. The process can be further enhanced by automatically deploying suggested rules using a human-in-the-loop approach, making the process faster and more efficient. 

Why Enterprises Are Embracing Augmented Data Quality

Augmented data quality is useful for any organization wanting to streamline its data quality management. Whether it’s for digital transformation or risk management, augmented data quality holds immense value. Here are a few examples of where our clients are seeing the value of augmented data quality:

Regulation and Compliance: Industries like healthcare and financial services are confronted with increasing regulatory changes. Yet, organizations often struggle to meet the demands of these regulations and must adapt quickly. By leveraging AI/ML methods to help identify data errors and ensure compliance with regulatory requirements, enterprises can efficiently minimize the potential risks associated with poor data quality. 
Use Cases: Single Customer View, Sanctions matching.

Business analytics: With complete, and consistent data, organizations can leverage analytics to generate accurate insights and gain a competitive edge in the market. Through AI/ML, data quality processes can be automated to quickly produce analytics and predict future trends within the data.
 Use Cases: Data preparation & Enrichment, Data & Analytics Governance.

Modern Data Strategy: Data quality is a foundational component of any modern data strategy, as data sources and business use cases expand. By leveraging augmented data quality within a modern data strategy, organizations can experience greater automation of manual processes, such as rule building and data profiling. 
Use Cases: Data Quality Monitoring & Remediation, Data Observability

Digital Transformation: Enterprise-wide digital transformation is taking place across all industries to generate more value from data assets. Automation plays a crucial role in enabling scalability, reducing costs, and optimizing efficiencies. 
Use Cases: Data Harmonization, Data Quality Firewall

Adopting augmented data quality within an organization represents a transformative step towards establishing a data-driven culture, where data becomes a trusted asset that drives innovation, growth, and success. The automation of process workflows reduces dependence on manual intervention, saving time and resources while enhancing efficiency and productivity. Moreover, augmented data quality increases accuracy, reliability, and compliance, enhancing customer experiences and improving an organization’s competitive advantage.

In conclusion, the seamless integration of augmented data quality within essential business areas offers significant benefits to organizations seeking to maximize the value of their data.

Find out more about Datactics Augmented Data Quality platform in the latest news from A-Team Data Management Insight.

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