TL;DR

This article dives into the world of data quality to highlight some of the most value-adding tools available. It details the foundational principles underlying data quality and explains the advantages and disadvantages each data quality tool has to offer. It also explains the current landscape for data quality tools with a view to the top challenges and their solutions and evaluates the potential for future innovation.

Introduction

Businesses of all types hope to drive their operations by data-driven decision-making, but for that to work, the data they rely upon must tell the right story. The data must be recent enough to correctly capture the relevant business scenario, must align with the format put forth by the organisation and satisfy a host of other requirements for the findings it yields to be taken seriously. Taken together, those requirements are known as data quality and they're measured by data quality tools.

A data management term referring to the accuracy, completeness, reliability and timeliness of an organisation's data assets, data quality is fundamental not only to decision-making, but to efficiency, innovation and profitability as well.

All other things being equal, a business with inferior data quality will be less able to leverage its digital assets to yield maximum productivity than one with higher data quality standards, so the difference in this single parameter can be the difference between failure and success. Data quality tools elevate this standard, helping organisations make the most of their data — and the rest of their operations as a result.

In this article, we'll take a deep dive into the core principles underlying data quality and evaluate the top tools for their operation. We'll show you what to look for in a data quality tool and detail some best practices for implementation and tool assessment. We'll also examine the future of data quality tools and what Zendata can offer. 

Key Takeaways

1. What is data quality? Data quality is defined as the degree to which your organisation's data is trustworthy and reliable. It is an essential component of data management and governance practices.
2. The key metrics used to quantify your data quality are completeness, integrity, validity, timeliness, consistency and uniqueness.
3. While a wide number of data quality tools exist, some of the most prominent are Talend, IBM InfoSphere, Great Expectations, and Informatica — though the exact tool you select will vary based on multiple factors. 
4. To select and implement their data quality tool of choice, organisations must assess their needs and available resources, decide which functionalities are most essential to their operations and pilot the tool with a test run to see if it meets their requirements. 
5. Artificial Intelligence (AI) and Machine Learning (ML) will likely drive future data quality improvements, though regulations are likely to be implemented as AI innovations continue.

The Principles Behind Data Quality

For a more precise description, Gartner defines data quality tools as: 

" ... the processes and technologies for identifying, understanding and correcting flaws in data that support effective information governance across operational business processes and decision making. The packaged tools available include a range of critical functions, such as profiling, parsing, standardisation, cleansing, matching, enrichment and monitoring." 

These processes and technologies help improve the veracity, currency and subsequent usefulness of an organisation's data, and many other business operations as a result. To see how these tools can elevate a company's data quality, it helps to first understand the fundamental principles that comprise data quality. 

While organisations may evaluate the status of their data quality differently, one of the leading standards is the UN's Data Quality Assessment Framework (DQAF). It employs six metrics to assess an organisation's data quality. They are:

• Integrity: A company's data is of little value if it doesn't accurately reflect its object's status. Integrity refers to how factual your data is and is therefore closely tied to its accuracy — and it's an essential mark of data quality.
• Validity: From visualisation to reporting, incorrectly formatted data will often be rejected as invalid. Validity is a metric of how well your data adheres to the required organisational and compliance formats, making it more useful for presentations and adhering to regulatory standards.
• Completeness: Your data has to tell the whole story. Completeness measures how much of your data is missing from a total dataset, ensuring that important gaps aren't left unfilled. 
• Consistency: If your data conflicts with itself or with other datasets, it might not be accurate. The Consistency metric shows how well your data matches up with other records and provides a good insight into how trustworthy your data is. It also creates multiple insights from analytics.
• Timeliness: Outdated data is bad data and it can prove highly unreliable. Timeliness refers to how current your data is and serves as a kind of expiration date to keep your data from going stale. 
• Uniqueness: Massive datasets may sometimes contain duplicates or omitted fields. Uniqueness measures how much of your data consists of duplicate data, preventing errors from creeping in and skewing the numbers.

Many of these data quality pillars overlap, as each can impact the other. For example, if a company's data is obsolete, it may no longer reflect the true value of its object, which in turn diminishes its integrity. 

This particular entanglement is especially important, as the speed of change in the data world has never been faster. The result is that a company must maintain the most current data for its analysis. Otherwise, it will make decisions based on outdated scenarios and fail to maintain its competitive edge. 

The Importance of Data Quality

From completeness to integrity, each component of an organisation's data quality can impact the rest of its operations. That makes data quality essential across nearly all business processes. In addition to the above examples, some other ways that data quality can impact business operations include:

• When an organisation's data lacks validity, it will fail to adhere to formatting requirements. This will make it less discernible to executives and stakeholders, making any reports or recommendations founded upon it less credible. The insights it yields will be less likely to be acted upon even if the data is accurate and valuable insights could be lost. 
• If an organisation's data has inferior uniqueness, then some of it must have been duplicated, resulting in skewed, inaccurate findings. Analysts who make recommendations based on such faulty data may end up encouraging incorrect business actions or missing opportunities for growth. 
• An organisation that has data lacking completeness may be subject to inadvertent bias and could end up tarnishing its brand image. 

In a climate where businesses attempt to apply data-driven decision-making to nearly every phase of operations, inferior data quality can have a ubiquitous impact. From manufacturers pivoting their production at strategic moments to marketing campaigns founded upon insights derived from their social media data, low-quality data can cause companies to lose productivity, miss opportunities for profit, and fall behind competitors who possess higher-quality data.

What Are Data Quality Tools?

Before you select a data quality tool for your stack, you need to know what capabilities your solution may have. As Gartner's definition shows, data quality tools may support a range of functions, such as:

• Data profiling, or running a scan to identify any problems with your data set
• Data parsing, or converting one form of data or type of text into another
• Data standardisation, or ascribing a uniform set of labels and symbols to your data, making it more legible for all
• Data cleansing, or correcting any errors located in your data and processes
• Data matching, or comparing multiple datasets to ensure they mirror each other, thereby improving uniqueness and consistency
• Data enrichment, or combining data from a primary source with datasets from other sources, to gain deeper insights for analysis
• Data monitoring, or periodically reviewing your data, to ensure its quality is maintained

Other important procedures related to data quality are data mapping (connecting data sets), data integration (unifying datasets into a single system), and data validation (double-checking to ensure maximum quality).

Also, while it's more to analytics and business intelligence (BI) than quality, data visualisation also enables executives and non-technical stakeholders to discern the results of your datasets. With so many capabilities available, the question is which ones best align with your data operations, as well as your broader business mission and scope.

Understanding the Role of Metadata Management in Data Quality

Effective data management requires more than just cleaning and organising your data. Metadata Management plays a critical role by providing detailed information about your data's origin, structure and usage. This not only enhances your data's reliability but also streamlines data governance practices.

By implementing Metadata Management, businesses can ensure their teams can easily find the data they need and understand its context, significantly improving decision-making processes. Remember, well-managed metadata is a cornerstone of high-quality data, leading to more informed business strategies.

4 Leading Data Quality Tools

Once you're aware of the functionalities your data quality tool should have, you can begin searching the marketplace for the best data quality tool for your organisation. 

There is no shortage of tools to choose from and some will possess capabilities that make them better suited for your application than others. This breakdown will consider what functionalities these tools are best used for and examine the key features and drawbacks of each.

1. Talend: Good for Versatility

Talend employs a host of visualisation methods such as charts and toolbars to let practitioners glean insights from its findings with ease. It also possesses data cleaning, standardisation and profiling functionalities, making it highly diverse — which is why it is frequently found near the top of many reviewer lists. Some other features include: 

• Employs ML algorithms to make recommendations on how to improve the quality of your data, taking the guesswork out of the process
• Implements a "Trust Score" system to compare the quality of multiple datasets, thereby improving consistency
• Displays a user-friendly interface that's intuitive for both technical and non-technical personnel

Despite its many features, some users have cited a slow runtime as one of Talend's drawbacks, as other solutions seem to be able to complete their tasks faster.

2. IBM InfoSphere: Good for Scalability/Flexibility

Developed by some of the leading tech industry giants, the IBM InfoSphere Information Server lets users cleanse, validate, monitor and better understand their data. 

Available both on-prem and in the cloud, IBM InfoSphere features an Extract, Transform, Load (ETL) platform that enables organisations to:

• Integrate their data across multiple systems
• Standardise their approach to improving their data quality, as well as the rest of their IT and business processes
• Employ Massively Parallel Processing (MPP) to execute their data quality management processes at scale

IBM InfoSphere is designed primarily for real-time use cases such as application migration, data warehousing, and corporate intelligence. It scored lowest in usability in some reviews, indicating a steeper learning curve than other data quality tools.

3. Great Expectations: Good for Data Validation

One of the most common data quality software solutions, Great Expectations (GX) is a notoriously data-centric quality tool. Rather than focusing on the source code, GX emphasises testing the actual data, since "that's where the complexity lives," as their developers say. GX boasts a wide number of data quality capabilities, including: 

• An "Expectations" list that displays the anticipated status of incoming data
• A "Data Contracts" section that implements automatic data quality checks and compiles them into the list, for easy accessibility and presentation 
• Direct connection with metadata aggregators, data catalogs and orchestration engines, as well as databases and data warehouses 
• Integration with many other data quality tools, including Amazon S3, Microsoft Azure, Jupyter, Databricks, Google Cloud and more

4. Informatica: Good for Data Profiling

Informatica comes in two forms: Informatica Data Quality (IDQ) and Informatica Big Data Quality (IBDQ). It leverages ML technology to identify and remediate errors or inconsistencies within an organisation's metadata and enables data stewards to automate a wide number of tests to catch data quality problems earlier. 

For all its capabilities, the downside of Informatica is that its interface is less user-friendly than some, as some users have reported difficulty with creating the desired rules and procedures. Informatica also lacks compatibility with other common data quality tools, though this issue is being addressed via the release of new versions and updates. 

How To Select a Data Quality Tool

Once a company knows what data quality tools are out there, it must think through the question of which one best suits its needs. Data quality tools possess a wide range of capabilities, interfaces, and price tags, so businesses must carefully evaluate how each one aligns with its operations. The exact considerations will likely vary by industry, but a solid step-by-step outline might be:

1. Identify your current data shortfalls: A company with inferior data quality due to poor completeness may need different tools than one whose data lacks adequate viability. The first step in finding the right data quality tool is assessing where your current quality is falling behind so that you can address the most pressing issue. 
2. Understand your tools: A data quality tool specialising in data cleansing may be most beneficial for improving uniqueness and consistency, while data mapping could help remove obsolete data to improve timeliness and integrity. Once you've identified where your data quality is falling behind, look for a tool with the functionalities needed to bring that particular shortcoming up to speed. 
3. Shop around: The above four solutions are only a small sampling of the many data quality tools that organisations can choose from. Search through multiple established reviews to see which ones rank the highest for your requirements and check out the customer reviews for an honest opinion on each one's pros and cons. 
4. Take it for a test run: A solid data quality solution will let you test its performance with a subset of your existing data, giving you a preview of its performance in real-time. 
5. Check the price tag: Small-to-midsized businesses (SMBs) will have a different budget for their data quality solutions than enterprise businesses and even the most profitable businesses must keep their vendor overhead to a minimum. Some data quality tools price out their solutions on a subscription basis while others can be purchased at a lump sum, so you'll want to make a decision based on both mechanism and cost. 

Another important parameter to consider as a business selects a data quality tool is the amount of customer support they will need. For example, enterprises may require a dedicated support team to facilitate their ongoing data quality maintenance, while those with fewer data assets may only need occasional support. 

Best Practices for Implementing a Data Quality Tool

After choosing a data quality tool, the next step is integrating it into your stack.  Following these best practices can help you implement your data quality tool:

• Decide on a measurement system: The DQAF is one standard that organizations may use to assess their data quality status, but other frameworks such as the Data Quality Maturity Model (DQMM) and Data Quality Scorecard (DQS) exist as well. The measurement system you choose will affect how you implement your data quality tool, so select one that aligns with the rest of your data operations and goals. 
• Establish an investigation process: Hiccups are bound to arise both in your data quality tool's implementation and your data management processes overall. Create a workflow to help teams identify and remediate any data quality issues so that you can keep downtime to a minimum. 
• Enlist a specialist: Data stewards are responsible for ensuring that your data assets are properly stored and managed and that all policies and procedures in your data governance strategy are followed. They might operate on their own or work in another department (most often IT), but make sure someone is responsible for overseeing your data quality — especially your tool implementation. 
• Foster a culture of data quality: All hands must be on deck to keep your data quality processes running effectively. Each team member who handles your data will need to be trained on how best to utilise your data quality tool, so provide plenty of education and check compliance early and often. 

Another key component of ensuring that your data quality process works as planned is to implement a data governance framework. Providing a comprehensive set of guidelines to help direct your data management systems, a data governance framework will help you establish the people, policies and processes needed not only to launch your tools but to develop a stronger data infrastructure overall.

Challenges and Solutions

Even after implementing best practices, deploying your data quality tool can still present some challenges. Some of the most common challenges you're likely to face are: 

• Too many data silos, resulting in duplicate data and poor uniqueness
• Alterations in data schema, leading to poor consistency and validity
• High data volume and velocity that become difficult to manage

Thankfully, many of the challenges associated with maintaining an effective data quality process can be resolved using the right data quality tool. Some solutions to these challenges are

• Transitioning from data silos to data lakes, creating easier access and lessening the need for duplication 
• Utilising a data cleansing tool to correct formatting errors, thereby improving validity
• Automating your data quality processes, making high data volume easier to handle

Adhering to your data governance framework should remediate many of these implementation challenges, so be sure to anticipate as many obstacles as possible as you craft your governance policies. 

Leveraging Master Data Management for Enhanced Data Consistency

Master Data Management (MDM) is an essential strategy for businesses looking to ensure consistency and accuracy across their core data. MDM involves creating a single, authoritative source of truth for your company's most critical data, such as customer, product and employee information.

By consolidating this information in one place, MDM helps eliminate inconsistencies and duplicates that can lead to poor data quality and decision-making errors. Investing in MDM can significantly enhance your operational efficiency, competitive edge and data quality.

The Future of Data Quality Tools

The capabilities of data quality tools have expanded in recent years. As AI and ML technologies continue to improve, look for data quality tools that leverage these techniques to better anticipate errors and take steps to remediate them sooner. 

AI and ML can also be used to power greater automation capabilities, supporting quality management and reducing remediation times in the process. And since data practitioners spend up to 80% of their time on data cleaning and wrangling, these AI-powered advancements can free up your team from time-consuming tasks. 

Conclusion

Data quality is a subset of the broader, often interchangeable, fields of data governance and data management, and it assesses the usefulness and reliability of a company's data assets. The quality of an organisation's data is determined by its accuracy, completeness, reliability and timeliness, with multiple frameworks existing to assess each parameter.

Once organisations have evaluated the initial state of their data, they can consider how best to improve it and a plethora of data quality tools exist to help them do the job.

Used in conjunction with data quality tools, Zendata's platform adds a privacy-focused component to your data operations. Specialising in data and privacy observability practices, our platform can help elevate your data quality standards while enhancing your privacy practices by discovering and classifying PII within your IT environment. We support data discovery, data profiling and data validation by providing context to the data your organisation collects and uses, facilitating data quality management.

If you'd like to improve your data and privacy observability and enhance your data quality in the process, contact us today to see how we can help.  

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