Data Quality

The Key to Successful Data Management

The quality of data-driven business decisions is only as good as the quality of the underlying data. Poor data can lead to costly mistakes and be counterproductive to a data-driven culture.

Opplane understands the financial impact of DQ issues and can enable organizations to extract maximum value from their customer data. In addition, a robust DQ environment can help organizations address regulatory challenges by meeting infosec and privacy requirements.

Implementing the Data Quality Process

Opplane uses a 3-step approach to implementing its data quality program:

Step 1:

Establish a DQ program that begins with data discovery and quantifying the financial and business impact.

We can help organizations begin their DQ program with a DQ health check. During this check, we uncover key DQ issues, quantify their impact, and create a risk matrix for prioritization.

  • Evaluate enterprise data platforms, SOR, site and analytical data environments, catalogs, and lineage.
  • Understand the organization’s data lifecycle and processes, including data classification, i.e., PII vs. Class 1/2/3, to assess the regulatory impact of the DQ issues.
  • Interview key stakeholders to identify and validate the DQ impact.
  • Quantify the business cost of DQ issues and the ROI of implementing a DQ program.
  • Complete a risk matrix for prioritization.
  • Enable executive buy-in for the program by helping to build an enterprise-level business case.

Step 2:

Implement DQ tools for a detailed gap analysis

We can help you select and set up DQ execution tools, leveraging our partnerships that best fit the organization’s data environment and business use cases.

  • Advise on the tool and partner selection process that best fits your organization and help with purchasing or building custom data quality tools as needed.
  • Provide complete implementations of Collibra DQ and BigID DQ with required professional services support.
  • Unify data management integrations with catalogs, glossaries, and custom data connectors.
  • Leverage Opplane’s AI, machine learning, and scanning capabilities to complement tool implementations.
  • Perform root cause analysis to assess the origin and extent of DQ defects throughout the data pipeline.

Step 3:

Remediation and Operationalization

The third and most critical phase of the DQ program is remediation. Opplane’s data and subject matter experts can enable your organization to operationalize DQ remediation to close the loop.

  • Leverage the risk matrix to prioritize remediation focus areas.
  • Select the appropriate remediation technique based on the nature and severity of the data quality issues: Imputation, data cleansing, and data enrichment are examples of remediation options.
  • Take advantage of machine learning and automation to implement DQ remediation, especially in the areas of cleansing and enrichment.
  • Cover both upstream and downstream environments to maintain data integrity throughout the pipeline.
  • Use business dashboards and analytics to monitor leading indicators of potential DQ issues and the associated business impact.

Data-driven decisions are only effective with solid data integrity.


say better insight into data could have helped improve the response to the pandemic.


of respondents believe that poor data quality has led to wasted resources and additional costs.


say that being data-driven helps them stay on top of consumer needs and market trends. Relying on data is essential to staying competitive.


of business leaders say they lack confidence in their data assets, which hinders their ability to be fully data-driven.

*source: Experian global data management research report

Why Opplane for Data Quality?

  • Leaders who built the original data warehouse at PayPal and the regional data management organizations that support businesses in North America, Europe, and Asia Pacific.
  • Big data engineering teams of world-class architects and full-stack engineers.
  • Data science and machine learning specialists who implement complex algorithms to enrich transactional data.
  • Certified engineers with the technical expertise to implement industry-leading DQ tools from our partners BigId and Collibra.
  • Ability to develop custom plug-ins and connectors for various databases and platforms.
  • Successfully implemented DQ programs that have delivered significant ROI at large and fast-growing banks and fintech firms in the U.S.

Collibra Data Quality Implementation

  • Full deployments, including standalone and cloud-native.
  • Backend data access knowledge via REST APIs for greater reporting flexibility. 
  • DQ rules and job creation expertise.
  • Getting started support for your stewards and DQ analysts.
  • Guidance on resource usage and optimization.

Let’s change the future together!

We are always looking for people to join our team who are as passionate as we are about helping organizations accelerate their customers’ digital transformation and data protection.

Free White Paper on Leveraging Data Remediation for Better Data Quality

Learn about Opplane’s six-step approach to effectively improve data quality through data remediation: 1) assess; 2) classify; 3) clean; 4) enrich; 5) validate; 6) monitor.

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