Want to see our platform in action?    

← Back to Resources

What is the Best Data Quality Monitoring Tool and Why Do You Need it?

Reading time: 8 min   |  By Sonia Chopra   |  Published in Articles,

Today, the most valuable asset a modern business holds is its data. Business leaders recognize this and have quickly shifted their mindset from viewing data as a commodity to prioritizing and ensuring its quality. If not, firms may make decisions based on incorrect data and insights, leading to missed opportunities, client dissatisfaction, and hefty regulatory fines. Using low-quality data also adversely affects a firm’s operational efficiency, leading to increased resources. Therefore, every firm needs an effective data quality monitoring tool to ensure that their data is continuously correct, consistent, complete, and timely throughout their infrastructure.

What is Data Quality?

Data quality is a set of characteristics that measure the degree to which data is fit for purpose and business use. Specifically, there are four key dimensions; correctness, completeness, consistency, and timeliness.

Correctness measures data’s validity and accuracy.

Completeness is the degree to which all required records and attributes exist.

Consistency is achieved if identical attribute values have the same representation in all occurrences of that value.

Timeliness refers to the availability and accessibility of information within an acceptable timeframe for fulfilling operational requirements, business planning, or decision-making.

Data Quality Dimensions

What are the Benefits of a Data Quality Monitoring Tool?

Poor data quality can have a domino effect, causing serious issues when it comes to productivity, risk, and planning for the future. Therefore, implementing a data quality monitoring tool is extremely important.

High-quality data drives the following aspects of a successful business:

High-Quality Insights and Informed Decisions
High-quality data drives high-quality, timely insights for making better decisions. Ingesting mass amounts of data can easily lead to errors, omissions, and inconsistencies due to either human or system factors. This results in incomplete, inaccurate, or delayed data, degrading its value in operations, planning, and decision-making. Low-quality data can also cause bottlenecks in business-critical workflows to result in adverse business outcomes.

Competitiveness and New Technologies
High-quality data is required to adopt new technologies successfully: The age of digital transformation has produced evolving technologies such as artificial intelligence (AI), machine learning (ML), internet of things (IoT), etc. For these new technologies to be successfully adopted and fully utilized, they require high-quality data.

Operational Efficiency
Firms do not have to manually comb through and verify large volumes of data, which is resource-intensive, laborious, and can often lead to false or duplicate investigations. Fewer mistakes and less time is needed to fix inconsistencies.

Regulatory Compliance
With an effective data quality monitoring tool, firms can submit regulatory reports confidently with data that is correct, consistent, complete, and timely. Implementing a consistent set of data formats combined with transparent business rules reduces risk and prevents penalties.

Firms can scale more quickly once they have a strategic and effective data quality tool in place.

Client Trust
Clients and partners are confident that their data is being handled with the proper controls.

Ultimately, implementing an effective data quality monitoring tool reduces regulatory penalties, legal liabilities, and reputational risk, all of which can adversely affect both top-line growth and bottom-line profitability.

Key Challenges with Current Data Quality Monitoring Tools

While there are several data quality monitoring tools on the market, they come with significant challenges.

No End-to-End Data Quality Insights
Many current solutions check individual attributes or fields of a dataset; however, they ignore business flows. For example, exceptions have a material business impact on revenues. Without end-to-end data quality insights, the material business impact due to exceptions is impossible to calculate accurately. If the business impact of exceptions is not known, firms have no way of prioritizing resolution.

Batch-Oriented and Bilateral Tools
Data quality monitoring tools often perform in batches at specific points for individual data sources (for example, at the end of the day), instead of providing metrics across the landscape. This results in a lack of scalability, high false positives, and duplicate issues. As a result, it takes significant cost and effort to build out new rules and manage existing ones, resulting in lengthy build-out times. Additionally, due to their bilateral nature, additional effort is required to compose an end-to-end “business transactional” view of data quality.

Fragmented Data and Lack of Visibility
Many tools fail to unify the enterprise landscape. This data and process fragmentation results in scattered, outdated, and duplicate records, leading to availability and accessibility issues. Additionally, firms must use extensive resources to investigate and resolve errors, resulting in time-consuming root-cause analysis and exception management.

Difficult and Time-Consuming Exception Root-Cause Analysis
To perform root-cause analysis of all exceptions for a transaction that spans multiple records from multiple data sources, multiple exception reports must be manually collated and analyzed. As the number of bilateral reconciliations increases, the number of these exception reports correspondingly increases, making it overly complex, laborious, and time-consuming to construct an end-to-end transactional view of exceptions.

Long Data Quality Cycles Leading to Higher Risk
Reconciliation tools cannot handle the scale of enterprise data, which can consist of billions of transactions. As the volume of data and the number of rules grow, the execution time for data quality batches increases. If there are multiple batches, this causes a cumulative delay that can result in data quality runs taking too long to complete, leaving insufficient time to identify and resolve data quality issues.

The Solution: PeerNova’s Cuneiform Platform
End-to-End Data Quality Monitoring with Business Impacted Value

Every firm needs an effective end-to-end data quality monitoring tool that also provides business impacted value. Cuneiform is PeerNova’s zero-code platform that provides data quality monitoring and exception resolution across internal and external data sources. This solution is the only one in the market that provides complete and end-to-end, continuous data quality metrics for business workflows.

Impacted Value Insights

The Cuneiform Platform provides the following benefits and features:

Continuous Data Quality Monitoring
The platform continuously monitors your data quality using four important dimensions: completeness, correctness, consistency, and timeliness across your workflows, datasets, transactions, and events.

Business Value Impact Scorecard
Cuneiform prioritizes data quality exceptions based on their business impact. See where you stand in real time and resolve the most important errors first.

Business Contextual Rules
The solution provides business contextual rules that are executed across the entire business flow, shortening build-out times for new rules and achieving significant operational cost savings.

Business Event Lineage
The Cuneiform Platform offers a complete lineage of data from inception to completion. This lineage is created in real time as business events are ingested into the platform.

The platform offers continuous data quality, which means that data quality is measured for every version of every event.

Self-Serve, Zero-Code Rules Builder
With Cuneiform’s zero-code capabilities, business users can easily use the platform’s self-serve interface to dynamically create, configure, and execute applications and rules without coding knowledge. This results in faster build-out times for new data quality rules and for maintaining existing rules with minimal IT effort.

With the Cuneiform Platform’s end-to-end continuous data quality monitoring, firms quickly streamline their operations, meet regulatory compliance, and improve decision-making. Through high-quality data and real-time, actionable insights, businesses can exceed client satisfaction and outshine the competition.

Ready to experience what our solution can do for your data quality? Request a demo today!

By Sonia Chopra

Sonia Chopra is PeerNova's Product Marketing Manager for the Valuation Risk product line. She has nearly a decade of marketing experience and has been with PeerNova for eight years. She specializes in crafting content and campaigns that address the complexities of product and valuation control, such as market volatility, asset pricing discrepancies, and regulatory compliance issues. Her ability to articulate the intricacies of these challenges, enables her to develop highly effective product marketing strategies that meet the evolving needs of the industry.

Want to see our platform in action?

By leveraging the Cuneiform Platform, you can obtain and use more accurate, data-driven insights through effective data quality monitoring. Learn more about how we can help you with your important tasks.