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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 &
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.
Needs Assessment
1Collection
2Storage & Security
3Analysis
4Reporting
5Retirement\ Destruction
6- What do you want to accomplish with the data?
- What questions do you need to answer?
- How quickly do you need the data?
- In what format do the data need to be?
- Where did I get the data?
- When did I get the data?
- How did I get the data?
- Who owns the data?
- How are the data defined, derived, etc.?
- How do I know the data are valid?
- Why do I have the data (e.g., mandates)?
- Where are the data?
- How can I find the data?
- In what format are the data?
- How did the data get there?
- Have the data been changed?
- Are the data private or otherwise sensitive?
- Does access need to be limited?
- What do the data mean?
- How good is the data’s quality?
- What are the limitations?
- How timely are the data?
- How can I use the data appropriately?
- How do others use the data?
- How do the data relate to other data?
- What do the data mean to the reader?
- What was reported in the past?
- Why have the data changed?
- What may or may not be reported at the individual level? In Aggregate?
- Which business rules govern report generation and data protection?
- Where will the data be archived?
- When do the data become invalid?
- What are the implications of preserving the data after that date?
- What procedures are required to destroy the data properly (e.g., “delete,” shredding, degaussing)?
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
- Program Onboarding Workshopping and Communications
- Current State Assessment
- Baseline Measurements
- Informing Your RFPs (e.g., documentation support for digital transformation efforts/ cloud migration)
- Review prior efforts and inform what should be taken forward
- Organizational Development & Change Management Support (e.g., Charter, RACI, Process & Policy Manual, Comms Program)
- Internal Communications
- Support recruitment of data domain support teams
- Metadata management tools guidance
- Metrics support (prioritizing, reporting, refinement)
- Program tracking
- Align & document support (SMEs, data owners/stewards) for current data sources
- Continued Data Domain Team Recruiting & Onboarding Support
- Data harmonization (post-merger data sources, new data sources, new reporting requirements, etc.)
- Program tune-ups/ refinements as identified and needed
- Maintain & monitor data quality dashboards and metrics
- Ongoing coaching
Contact Us To Find Out More
Data science solutions that work for you
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