Data Quality

Key to Successful Data Management

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

Opplane recognizes the financial impact of DQ issues and can enable enterprises to extract maximum value from their customer’s data. Additionally, a robust DQ environment can help organizations address Regulatory challenges by being infosec and privacy compliant.

Getting Started with the Data Quality Process

Opplane uses a 3-step approach for implementing its Data Quality program:

Step 1:

Establish a DQ program starting with Discovery & quantifying financial and business impact

We can help organizations kick-start their DQ program through a DQ Health check. During this check, we uncover key DQ issues, quantify their impact, and build a risk matrix for prioritization. 

  • Assessment of enterprise data platforms, SOR, site & analytical data environments, catalogs & lineages  
  • Understanding of the organization’s data lifecycle and data processes, including Data classification, i.e., PII vs. Class 1/2/3, to assess the regulatory implications of the DQ issues 
  • Interviews with key stakeholders to ascertain and validate DQ impact  
  • Quantifying the business cost of DQ issues and ROI of implementing a DQ Program.  
  • Risk matrix completion for prioritization 
  • Enable executive buy-in for the program by helping create an enterprise-level business case 

Step 2:

Implement DQ tools for a detailed gap analysis

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

  • Advise on the tool & partner selection process that best suits your organization & help with purchasing or building custom data quality tools, if needed 
  • Provide Collibra DQ and BigID DQ full implementations with necessary professional services support 
  • Unified data management integrations with data catalogs, glossaries, and custom data connectors
  • Leverage Opplane’s AI, machine learning, and scanning capabilities to complement tool implementations 
  • Root cause analysis to assess the origin and extent of DQ flaws across the data pipeline 

Step 3:

Remediation & Operationalization

The third and perhaps most crucial stage 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 focus areas to kick-start remediation  
  • Choose the proper remediation technique, depending on the kind & extent of data quality issues: Imputing, Data Cleansing, and Data Enrichment are examples of remediation options 
  • Use of Machine learning and Automation to implement DQ remediation, especially in the areas of cleansing and enrichment  
  • Cover both upstream and downstream environments to ensure the integrity of data is maintained across the pipeline 
  • Business dashboard and Analytics to monitor leading indicators of potential DQ issues and the associated business impact 

Data driven decisions are ineffective without strong data integrity


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


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


said being data driven helps them stay on top of onsumer needs and market trends. Reliance on data is essential to staying competitive.


of business leaders say they lack trust in their data assets, hurting their ability to be fully data driven.

*source: Experian global data management research report

Why Opplane for Data Quality?

  • Leaders who have built the initial data warehouse at PayPal and the regional data management organizations to power businesses in North America, Europe, and Asia Pacific
  • Big data engineering teams comprising best-in-class architects and full-stack engineers
  • Data Science and Machine Learning specialists implementing complex algorithms to enrich transactional data
  • Certified engineers with the technical know-how to implement industry-leading DQ tools of our partners: BigId and Collibra
  • Ability to build custom plug-ins and connectors to cover a wide array of databases and platforms
  • Successfully deployed DQ programs, generating significant ROI at large and fast-growing banks and fintech firms in the US.

Collibra Data Quality Implementation

  • Full installations including standalone, and cloud-native 
  • Backend data access knowledge via REST APIs for greater reporting flexibility 
  • DQ Rule and job creation expertise
  • Getting started help for your stewards and DQ analysts
  • Resource consumption and optimization guidance 

Let’s change the future together!

We’re always looking for people to join our team who are as excited as we are to help companies accelerate the digital transformation of customers and data protection.