cognitio analytics

Data Trust & Data Governance

Having high quality data and reliable information about the data drives quantifiable business impacts through:


Better, more confident decision making


Faster and more accurate analytics, through less time gathering & cleaning the data and more time developing answers to key business problems


Superior follow-up after implementation; once you have clean data, establishing monitoring dashboards is quick and easy

Data Trust &
Data Governance​

We build data quality guardrails to ensure your data is continuously high quality

What Is The Meaning Of My Data?

We help to make your metadata easier to search through and navigate

How Are My Data Sources Connected To Each Other?

We clarify the relationship between your different databases and tables

How Do We Know That The Data Are Good?

We build sets of analysis capabilities and reports that measure Data Quality across key dimensions and relevant metrics

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We walk with you throughout
Your Data Trust Journey

Our approach to Data Trust starts with your organization’s Culture and keeps Culture top of mind throughout the journey.

Data Trust exists when decision-makers and analytics professionals can believe what they are seeing, because:

Data Governance has answered the questions about the meaning of the data and what it should be.

  • Data Literacy has provided the users the information and knowledge necessary to do high quality analysis efficiently and effectively.
  • Data Quality tracks the data through the lifecycle, ensuring that the data you are working with is correct and fit for use.
  • Your Business Culture has evolved to support these data trust investments & processes and utilize the learnings to make quick and confident business decisions.

Does everyone who needs data access understand:

How to report “suspicious” data observations?

  • The guardrails of data use (including proper and improper uses and interpretations)?
  • How to access data training?
  • How to onboard new team members?
  • The expected shared definitions of key definitions and metrics?

Does everyone who interacts with your data understand:

Who specifically is responsible for each data domain in your organization?

  • Who to reach out to when you have questions or concerns?
  • Expectations and responsibilities across each phase of the data lifecycle?
  • The lineage and calculation rules behind your data and metrics?
  • How to inform, track, manage and access your metadata assets?

tracking the experiences in relation to each other as well as independently, the fulness of the customer journey can be analyzed and optimized across the range of key touch points.

Does everyone who interacts with your data understand:

  • How are their data derived?
  • The guardrails of data use and data security?
  • Important linkages and disconnects between your data sources
  • How to properly interpret, analyze and report on the data
  • Which specific data sources are the actual “source of truth” for specific information needs

Does everyone who interacts with your data understand:

How to report suspicious data observations?

  • The expected range of values for specific metrics?
  • Where to find the “backstory” (e.g., methodology, specific definitions, calculation rules) for their data?
  • Where to find additional context about their data?

Our Approach Addresses
All Phase of Your Data Lifecycle

Our approach to Data Trust starts with your organization’s Culture and keeps Culture top of mind throughout the journey.

What We Do

We help our clients by understanding and adapting to their current state realities and then helping
them to create a better way going forward. We work effectively in tandem with the large
multinational management consultancies as well as in place of them

Contact Us To Find Out More

Data science solutions that work for you