Data Quality Approach

Approach

Traditional Data Management practices have failed.

Organizations have spent extensive time and money on disciplines in the realm of Data Governance, Data Cataloging, and Metadata yet these efforts have not scaled or met expectations without a focused approach towards Data Quality.

Current trends show that data is generated faster than data can be consumed and, in this climate, as a business you need a change in thinking from traditional data management practices towards a “Data Quality First” approach.

What is “Data Quality First” approach?

As organizations grow, you must be aware of where your internal practices land within the data maturity life cycle as this can drastically enhance or hamper the overall growth of your organization. DQLabs defines this data maturity life cycle in four stages:

Data Quality First Approach
Beginner

No data management practices implemented and more people-dependent processes using offline tools and methodologies

Low

Have used or tried individual or departmental efforts either using custom solutions or off the shelf solutions with no centralized focus

Medium

Have implemented a Data Quality, Data Catalog or Data Governance solution and are engaged in an effort towards centralized management practices, however, they are still struggling to streamline processes and have not seen real ROI.

High

Have a centralized data governance team and implemented technology and processes for Data Governance, Data Catalog, Data Lineage, Data Quality, DataOps, and Data Science but many are still struggling to have a modern collaborative data workspace with a focus on Data Quality.

Here’s the bad news - irrespective of which category you belong to, many organizations have no understanding of what percentage of bad data exists. This is because most of the efforts taken towards Data Quality are in conjunction with other disciplines of Data Governance, Data Catalog, or Data Lineage but there is not a primary focus or an actionable effort towards Data Quality.

That is why we preach a Data Quality first approach – an approach that focuses on understanding data from a business context via automated processes like semantic discovery and classification. This is further fueled by automation using proven machine learning technology that helps organizations measure, monitor, and remediate data quality issues in a more practical way and derive immediate value. If you are in the high maturity cycle, the good news is that a Data Quality First approach still takes into consideration your current learnings of business glossaries, governance practices and seamlessly integrates to automate an actionable data quality process.

Using this approach, you can benefit from trustable, actionable data and answer

  • What data is important and what is not?
  • What data needs to be improved or remediated?
  • What data can be used in reporting and analytics?
  • What data pipeline changes are needed to address schema/data deviation or drift?
  • What models can be built to enrich customer experiences?
  • Can I trust this report I am signing off on and am I as compliant as I think I am?

Recently published

Gartner Data Quality Software Comparison - DQLabs Rated High

BLOGS

Gartner Data Quality Software Comparison – DQLabs Rated High

Gartner, Inc. just released a comprehensive comparison of the leading suppliers of Data Quality software which included DQLabs. The Connecticut-based technology research and consulting company published this report covering data management solutions for technical professionals.

Gartner undertook this study after surveys with leading organizations revealed that capabilities provided by traditional data quality software require too much manual effort and are no longer sufficient to meet the needs of organizations operating in rapidly evolving data environments. In this highly anticipated review, several key categories were examined, and 20 criteria rated to allow technical data and analytics professionals to assess these enterprise data quality platforms and their offerings.

For organizations that require either high degrees of data quality or are seeking a holistic approach to managing all of their data, this report would be of great use. The report categories that DQLabs rated highly in are as follows:

Supporting capabilities: Describes capabilities that support core functionalities of a data quality tool, including connectivity, scalability, deployment options and integration options

Data quality analysis and profiling: Describes capabilities that support the analysis of the structural and contextual aspects of data

Defining, assessing and validating rules: Describes capabilities to define, create and deploy data quality rules to assess and validate the quality of a data asset

Remediation and enrichment: Describes capabilities to parse, standardize, cleanse and enrich data using a combination of automated transformation logic and manual workflows

Monitoring: Describes the capabilities to automatically track the quality of data, determine if appropriate levels are being maintained and notify users if problems are detected

DQLabs clearly took first place by being rated by Gartner as either medium or high in 19 of the 20 listed criteria. Data management platforms like DQLabs can automate profiling, rule generation, rule deployment, monitoring, data cleansing, enrichment and remediation workflows. Proficient data quality platforms like DQLabs are tied to data governance and use integration to metadata management solutions to deploy data quality rules, track rule deployments, share validation results and support remediation efforts. To this extent, DQLabs was recognized as a pinnacle provider of a leading-edge augmented data management platform after careful review.

This report provides clear evidence that organizations with data management needs in the areas of data quality, defining and validating rules, remediation and enrichment as well as monitoring and supporting capabilities, would be well supported by the augmented data management platform from DQLabs.

For further details regarding the results of this report please contact your DQLabs representative.

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Best Practices

Gartner Data Quality Software Comparison - DQLabs Rated High

BLOGS

Gartner Data Quality Software Comparison – DQLabs Rated High

Gartner, Inc. just released a comprehensive comparison of the leading suppliers of Data Quality software which included DQLabs. The Connecticut-based technology research and consulting company published this report covering data management solutions for technical professionals.

Gartner undertook this study after surveys with leading organizations revealed that capabilities provided by traditional data quality software require too much manual effort and are no longer sufficient to meet the needs of organizations operating in rapidly evolving data environments. In this highly anticipated review, several key categories were examined, and 20 criteria rated to allow technical data and analytics professionals to assess these enterprise data quality platforms and their offerings.

For organizations that require either high degrees of data quality or are seeking a holistic approach to managing all of their data, this report would be of great use. The report categories that DQLabs rated highly in are as follows:

Supporting capabilities: Describes capabilities that support core functionalities of a data quality tool, including connectivity, scalability, deployment options and integration options

Data quality analysis and profiling: Describes capabilities that support the analysis of the structural and contextual aspects of data

Defining, assessing and validating rules: Describes capabilities to define, create and deploy data quality rules to assess and validate the quality of a data asset

Remediation and enrichment: Describes capabilities to parse, standardize, cleanse and enrich data using a combination of automated transformation logic and manual workflows

Monitoring: Describes the capabilities to automatically track the quality of data, determine if appropriate levels are being maintained and notify users if problems are detected

DQLabs clearly took first place by being rated by Gartner as either medium or high in 19 of the 20 listed criteria. Data management platforms like DQLabs can automate profiling, rule generation, rule deployment, monitoring, data cleansing, enrichment and remediation workflows. Proficient data quality platforms like DQLabs are tied to data governance and use integration to metadata management solutions to deploy data quality rules, track rule deployments, share validation results and support remediation efforts. To this extent, DQLabs was recognized as a pinnacle provider of a leading-edge augmented data management platform after careful review.

This report provides clear evidence that organizations with data management needs in the areas of data quality, defining and validating rules, remediation and enrichment as well as monitoring and supporting capabilities, would be well supported by the augmented data management platform from DQLabs.

For further details regarding the results of this report please contact your DQLabs representative.

View More Arrow image