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7 Things Your Competitors Know About Data Quality (and You Should Too)

15 Jan, 2021  |  By Marketing Team   |  Published in Articles,

7 Things Your Competitors Know About Data Quality

Interested in learning more about data quality? Read PeerNova’s latest whitepaper and watch the data quality webinar on demand now!

Whether you are a large financial firm or a smaller enterprise, you may wonder if data quality is something you need to worry about. The answer is yes. Your competitors are already developing a top-of-the-line data quality strategy and implementing turnkey data quality tools to help them manage and govern their data more effectively. By prioritizing continuous data quality across the organization, your firm can quickly and positively improve its operational efficiency, regulatory compliance, and business decision-making.

Whether or not you are a data quality expert, there are seven key things you should know (that your competitors already do).

1. Data quality must be a critical piece of your overall data governance and data management strategies.

Data quality is not a one-off project, but instead, an iterative process that must be constantly and proactively improved. If you recall, data governance creates the rules, policies, capabilities, and processes that govern data. Data management keeps and manages that data in a safe and usable manner. Both of these practices should be designed with the goals of data quality in mind.

2. A single error in data can cause significant distress to your firm as a whole.

The smallest error or discrepancy from a system storing, enriching, or transforming low-quality data can trigger a chain reaction within your other processes and workflows. It becomes extremely challenging to trace and reconcile the source of the error. As a result, the root cause continues to sabotage various areas of your business, including operational efficiency, regulatory reporting, and decision-making. Your business must implement an effective data quality tool to catch low-quality data early.

3. You cannot achieve continuous data quality without understanding its current challenges.

Before you can accomplish continuous data quality, it is important to both understand and address the current challenges.

The enterprise landscape is extremely fragmented and siloed.
Data stored across your enterprise in disparate locations becomes difficult to manage. These silos result in scattered, outdated, and duplicate records, leading to availability and accessibility issues. Piecing the data together requires extensive resources, which can be costly, time-consuming, and error-prone.

Firms struggle with complex business processes and the heterogeneous nature of IT applications.
Effectively governing and managing large volumes of data while maintaining security, integrity, and quality across disparate, multifaceted workflows becomes extremely difficult.

Many firms use passive data management, data governance, and data quality tools.
These solutions often only fix the incorrect data, instead of resolving the foundational root cause that produced the error. As a result, the error may reappear in the future.

Additionally, firms must manually reconcile inconsistencies within each individual silo, instead of automatically across the enterprise. Data quality checks are performed sporadically or as a last step in the pipeline, when they should be applied continuously throughout the data’s lifecycle.

The majority of current solutions are strictly bilateral and batch-oriented.
For firms to perform continuous data quality checks on complete business flows, they must perform multiple bilateral data quality checks, often performed in batches. Other challenges with ensuring data quality include a lack of standardization, interoperability, and knowledge of a golden source.

4. With high-quality data, your firm’s operational efficiency skyrockets while your operational costs plummet.

Data quality issues often require extensive resources to address and fix, quickly reducing your firm’s operational efficiency and increasing operational costs. Delegating personnel to manually identify and reconcile errors while piecing together the fragmented enterprise landscape is costly. However, through continuous data quality checks and a solid data quality strategy, your firm not only reduces errors but also prevents manual investigations and duplicate efforts in the future.

5. Inaccurate data submitted through regulatory reports can result in hefty fines and compliance concerns.

When submitting regulatory reports, low-quality data can create significant problems for both you and regulators. Incorrect reporting results in unnecessary audits, hefty penalties, and higher capital reserve requirements. By having the right data quality controls, firms can reduce their risk and confidently report to regulators.

6. Faulty data leads to poor decision-making, it’s as simple as that.

Business leaders rely on having high-quality data to power their analytics and help them make decisions. However, low-quality business data produces inaccurate insights, resulting in business leaders making misinformed decisions. This can lead to incorrect knowledge about spending power, behavior, and interests. With continuous data quality, your firm can make informed decisions, while monetizing your data for direct top-line growth and increased profits.

7. Not all data quality platforms are created equal.

Today, many specialized data quality solutions require deep expertise for successful implementation. These tools can be fairly complex and require in-depth training to be used effectively. Additionally, these static tools only address the incorrect data, instead of fixing the error at the source to prevent it from happening again.

Your firm must take a holistic, active, enterprise-wide approach by implementing an effective data quality tool, like PeerNova’s Cuneiform Platform.

With the right data quality tool in place, like the PeerNova® Cuneiform® Platform, your firm can proactively monitor, identify, address, and resolve the root causes of any data quality issues before they enter the organization’s pipeline. The solution provides zero-code end-to-end (E2E) automation for business users that enables them to rapidly define and deploy data integrity and process correctness controls across a firm’s workflows, applications, or business processes.

Data quality is not just an IT priority, but a holistic, iterative process that involves every department within an organization. As a zero-code solution, business users can easily alter interfaces to enable new features and design workflows by connecting pre-built reusable modules. Instead of waiting for IT teams to implement changes, business users can independently create and implement new tools or features that fit the needs of their clients.

With the Cuneiform Platform, business users have access to high-quality and well-governed data that greatly improves your firm’s operational efficiency, regulatory compliance, and business decision-making.

By implementing an effective data quality strategy, your firm can reap the benefits of having data that is correct, consistent, complete, and timely. You can stay ahead of agile competitors, recalibrate quickly, identify digital transformation opportunities early, and, ultimately, increase your profitability.

Whether your organization is big or small, data quality must be your top priority. For more information about how to achieve continuous data quality and how to create an effective data quality strategy, read PeerNova’s latest whitepaper and watch the data quality webinar on demand now!

Achieving Continuous Data Quality in Financial Institutions Webinar




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