03 01

In today’s environment, entrepreneurs and business leaders are focused on accelerating time-to-value with data quality management to drive profitability and competitiveness from actionable insights.

The need for speed puts pressure to fast-track your data journey from source to value to rev access to innovation and help your organization move faster, giving customers quicker, more secure, and timely support and services.

Still, the big question is: How to empower your business to accelerate time-to-value while meeting the security, compliance, reliability, and availability challenges?

One way to dramatically improve time-to-value with data quality is by deploying new technologies and practices to make the data lifecycle more efficient and realize business value from data and analytics sooner.

As a result, organizations are heavily investing in data quality solutions – because virtually all of them understand that insufficient data quality, consistency, and completeness, as well as poor knowledge about the data’s lineage, can slow down the realization of value!


High-quality data drives trusted decisions and plays a critical role in the business! 

That is why Gartner predicts that by 2025, 60% of data quality processes will be autonomously embedded and integrated into essential business workflows.

But consistently delivering high-quality data is a challenge – as it is not a one time-activity and requires getting everyone on board to ensure a successful data quality adoption!

Furthermore, the processes that used to work for small organizations no longer suffice as those companies grow. IT teams and business analysts face a growing challenge with application proliferation and distributed data, making it more difficult to know what information the organization has, where that information resides, and its context.

So, a different approach is needed to make data quality initiatives successful.

You cannot get value from data to your business if you are not working with high-quality data for more accurate insights. And without those insights, data goes underutilized for analytics and initiatives such as gaining a 360-degree customer view.

So, only with the best practices in mind can organizations seamlessly implement a data quality strategy that ensures the use of reliable data across the entire enterprise.

As Collibra Data Governance Specialists, Murdio Data Quality and Observability tools allow you to trace the entire lifetime of data and give you the ability to prove that high-quality data yields high-quality results! 

Here are the key benefits of data quality adoption:

  • Improve your TCO ROI on data: 484% 3-year ROI and 34% improvement in staff time to address data errors;
  • Eliminate silos: With deep, granular data about workload performance, you can consolidate and optimize infrastructure to eliminate data silos;
  • Increase employee productivity: Eliminate up to 60% of manual data quality workloads with autonomous data quality rules;
  • Leverage technology innovation: Real-world data helps determine how technology innovations can improve performance;
  • Reduce regulatory & compliance risks: Avoid seven-figure fines for non-compliance with BCBS 239, CCAR, HIPAA, GDPR, and other regulations;
  • Cut costs: Accelerate cloud data migrations, as one company saved 2000 hours of effort with rule-based data integrity validation;
  • Fill your business with trust in data: quickly capitalize on data as a crucial business asset to be far more innovative, competitive, and cost-efficient.


Finally, these are our best-recommended practices to help you achieve a successful data quality adoption and ensure success. Follow along to discover 6 ways to accelerate time-to-value with data quality management:

  1. Make a strong use case specific to your pain points
    Compare your current state with the future state you are targeting by asking specific questions to your organization: How are you leveraging data quality? How can you get everyone on board? What are the barriers to updating your data quality vision? What state of data quality are you targeting? What are your strategic initiatives and their expected outcomes?
  2. Identify and implement the right solution that fulfills your requirements
    Choose a complete solution that not only automates technical rules but also business rules for building trust in your data. And if you are already using any data tools, begin by questioning if they are helping you achieve end-to-end quality, as most tools provide only partial automation and limited scalability. Instead, opt for a solution that leverages a predictive approach to quality and scales effortlessly!
  3. Add observability to your data quality stack
    Typical data quality dimensions fail to consider the business impact crucial for any organization. Therefore, Collibra focuses on the dimensions of data quality that have a meaningful business impact – one of them is Behavior which allows monitoring if the data behaves or looks different than before.
    And this is where data observability comes into the picture! It monitors data as it moves through the enterprise systems and secures quality across the entire data journey. Data Observability takes a more comprehensive view of data, including its lineage, context, performance, and business impact. It proactively tracks the health of enterprise data systems, so you are aware of potential issues in advance, and gives you rapid access to trusted data.
  4. Move towards a unified data management approach
    By enabling business users to identify and assign quality issues, you ensure data quality efforts are not limited to a small team but embraced by the entire enterprise. And leveraging metadata extends this approach by working with data quality, observability, catalog, governance, and lineage together, which provides the right context to the quality issues for impact assessment. Thus, allowing you to get the best out of your data and analytics investments.
  5. Keep stakeholders informed and the team members accountable
    Adopt an end-to-end collaborative approach to accelerate time-to-value with data quality by following these easy steps:
    5.1   Data profiling: By profiling data, observe all the aspects of data sources you need to be aware of and classify the observed issues.
    5.2  Data quality assessment: Set up the assessment criteria for data quality, identifying the current rules you need to enforce or the new rules you need to write.
    5.3  Data quality cleansing prep: All cleansing and standardization activities depend on your domain and the depth of your rule library.
    5.4  Data quality monitoring: Perform regular quality checks by executing profiling and data quality rules. You also need to set up monitoring and how best to handle the data when quality issues arise.
    5.5  Data issue management: Assign new data quality issues to the respective data owners and follow up regularly to fix them at the source.
    5.6  Data issue remediation: Investigate deep-rooted data issues which may span multiple data sets and owners.
    5.7  Data quality performance and impact reporting: Report your efforts with performance metrics and the impact of the data quality issues.
  6. Measure, report and keep improving continuously
    Data quality is an ongoing process, and its continuous measurement enables finding areas for improvement. Hence, it’s essential to share the findings with all stakeholders as a practice to keep everyone informed and enable them to contribute to the refinements. Define your expectations around cost savings, productivity gains, and improving data compliance efforts across the organization.


Accelerating time-to-value with data quality is imperative in today’s environment. Business leaders can make smarter, more informed decisions to accelerate time-to-value (or time-to-market if we’re talking about new products) by using modern tools to access high-quality data from their workloads.

Collibra Data Quality and Observability tools provide a fast and elegant way to manage your data sets by learning through observation rather than human input. And also apply the latest advancements in Data Science and Machine Learning to the problem of Data Quality – surfacing data issues in minutes, not months!

Besides, this pluggable and complete Data Quality framework allows you to use either native CDQ components or integrate the 3rd party components of your choice.

So, to learn more about how you can utilize Collibra’s modern approaches to leverage quality data to the point where it is ready for insight, book a discovery call with Murdio team.

We can help you solve your toughest data challenges and accelerate time-to-value with data quality in your organization!

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