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Data Rocket Launches Data Quality Watch®

At the heart of digital transformation is data quality – the ability to trust an organization’s data. To be able to fully trust data and get reliable analytical insights, organizations need to be able to measure its data quality. For that reason, Data Rocket has launched Data Quality Watch®, an automation solution that measures quality and usability of data, is available now for current and new Data Rocket customers. Watch a demo of Data Quality Watch here.

Data Quality Watch is built using Snowflake and Talend, and can either run on demand or on a pre-set schedule.  Data quality metrics are  configured to meet the specific needs of an organization and presented with a set of prebuilt dashboards in PowerBI or Tableau. Data Quality Watch measures data based on five data quality dimensions: 

Data Quality Dimensions 

  • Data Integrity – has the data been compliant and behaving accurately in accordance to business processes and policies? 
  • Data Consistency – is the data consistent across data sources? 
  • Data Completeness – is the data complete and not missing critical data points? 
  • Data Timeliness – is the data up to date?  
  • Data Popularity – is the data being used in the organization? 

How Data Stewards Can Use Data Quality Watch to Bridge the Business – IT Divide 

Without measurement, Data Quality can be a vague concept to digest – Data Quality Watch provides a customizable framework for data to be continually and proactively curated. Better yet, Data Quality Watch makes it easier for technical and business line users to set common goals and establish a Data Quality culture.  

As important as data quality it is – it can be a moving target. Data can reside in multiple source systems and can be changed as it moves through data applications. Data Stewards can use Data Quality Watch to obtain visibility into data objects throughout the data life cycle.  

Data visibility supports better collaboration between IT and business users.

To achieve data visibility, Data Quality Watch performs scheduled data profiling on uniqueness, non-empty values, consistent data patterns, data freshness and data usage. The tool profiles data based on the configuration parameters defined and produces output with finer level of data quality insights. 

Once data is profiled, Data Quality Watch calculates Quality Measure scores by business function. Within each data set, results can be drilled down at the table and column level – providing an easy snapshot of data quality at any point in time. 

Data Quality Watch helps rally all data users around a common understanding of data quality, using an organization’s values as guideposts. Data Quality Watch is configurable by business function – measuring specific data sets against targeted criteria. For example, Data Timeliness might be an important attribute for operations and logistics, but not as critical for other business users – the Data Quality Watch can be configured accordingly. Data Quality Watch can schedule multiple, distinct queries on separate data sets, defined by the user.  

Additionally, Data Quality Watch shows how data quality metrics trend, which can help an organization know the current state of data maturity to ensure it is an upward improvement trend. 

By establishing Quality Measures based on business values, Data Quality Watch helps Data Stewards focus data resolution initiatives on the most important data resources. When Data Quality issues are identified at the data processing level, Stewards can focus resolution efforts on data integration and data prep. For business line corrections, Data Quality Watch’s detailed profiling helps provide a roadmap for reconciliation.  

Too often, business users only think about Data Quality when issues arise – pulling a report that has empty fields or outdated information, or finding multiple versions of the same customer record in a database. By measuring data quality for relevant data points, Data Quality Watch provides guard rails for business users, even as data ecoystems grow. When business users trust data, they are more likely to use data for decision-making. The more data is used, the more it is verified for usefulness. The result is a positive feedback loop with data at its center – and a Data Quality culture is born.  

Data Quality Watch – Amplifying Data Rocket’s Data Governance Toolset 

Data Quality Watch is available to new and existing subscribers to Data Rocket. Data Rocket was created to introduce data governance to every stage of the data life cycle. With Snowflake’s infinitely elastic Data Cloud eliminates siloes, and with a well governed Data Cloud environment, data can be stored, accessed and leveraged from one central location, eliminating duplicate data and providing one version of the truth. With Talend at the core of Data Rocket’s Governed Dynamic Ingestion Engine, Data Rocket provides managed change data capture, preliminary data cleansing, and versioned data history for every change in source system data. 

Data Quality Watch adds to Data Rocket’s capabilities, with a practical tool for measurement and action. Join us September 15 as Passerelle engineers present a live demo of Data Quality Watch.  

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