Data Governance Consulting

Marlabs helps organizations create a framework for managing their data that aligns with their business goals and objectives. Our consultants provide guidance on best practices for data collection, storage, analysis, and use. We also help establish guidelines for data quality, security, privacy, compliance, and ethical use.

Data Governance Services

Data Governance Strategy

Marlabs consultants can help organizations develop a comprehensive data governance strategy that aligns with their business goals and objectives.

We have experience designing the governance framework for data management, including policies; procedures; and standards for data collection, storage, analysis, and use.

MDM & Data Quality Management

Can you trust your data? Data governance and master data management (MDM) go hand-in-hand.
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Data governance policies can define the rules and processes for creating, updating, and deleting master data, while MDM technology can enforce these rules and ensure that master data is accurate, complete, and consistent.

Training and Support

A strategy doesn't do any good if it only lives on paper. Adoption and adherence is critical to the success of data governance initiatives.

Our consultants provide training, support, workshops, and coaching to help employees understand and comply with data governance policies and procedures.

What Is Data Governance, and Why Is It Important?​

Data governance is the foundation for reliable, secure, and actionable data for any organization. It ensures data accuracy, consistency, and responsible use to help organizations make informed decisions that reduce risk and drive value. Data governance isn’t just an advantage—it’s essential for building trust and unlocking business potential for your company.​​

Here are just a few things poor data governance can cost businesses:​
  • $3.1 Trillion: *IBM’s estimate of the yearly cost of poor-quality data, in the US alone, in 2016.
  • 50% Time Wasted: The amount of time that knowledge workers waste in hidden data factories, hunting for data, finding and correcting errors, and searching for confirmatory sources for data they don’t trust. *IBM’s estimate of the yearly cost of poor-quality data, in the US alone, in 2016.
  • 60% Time Spent: The estimated portion of time that data scientists spend cleaning and organizing data.
Source: HBR https://hbr.org/2016/09/bad-data-costs-the-u-s-3-trillion-per-year

Data Governance Requires Change at Two Levels​

Organizational Level:

At an organizational level, our change management efforts assess and understand an organization’s:
  • Current cultural attributes, which may provide a solid basis for or be an impediment to the change ​
  • Prioritization of change initiatives in an effort to monitor change fatigue and saturation, as well as to build change agility ​
  • Shared vision and strategic intent for the change
  • New or modified business processes, systems, policies, behaviors, rewards, performance indicators, and procedures needed to successfully work in the future state
  • Structure and individual roles needed to support and reinforce the change effort

Individual Level:

At an individual level, our change management efforts address and manage individuals':
  • Unique perspectives, biases, motivations, behaviors, mindset, resistance, and reactions to increase acceptance and commitment in a more productive and resilient way ​
  • Willingness, ability, knowledge, skills, and time capacity necessary to transition to the future state
  • Sponsorship and active leadership needs to ensure successful change and coach an individual through personal transition

Data Governance Foundation Accelerator

LEGEND is a series of accelerators from our MAP framework. Our data governance accelerator is a comprehensive program that helps organizations establish effective foundation for data governance quickly and easily. It provides a set of pre-built templates, tools, and best practices that are tailored to your organization's specific needs, enabling you to jumpstart your data governance initiative and achieve results faster. If your organization is struggling to get a data governance strategy off-the-ground, this is an accessible first step.

Establish a Foundation to Grow Your Data Governance Strategy
  • DG Roles and Responsibilities
  • Data Catalog Platform Selection Guide
  • Sample DG Procedures
  • Sample DG Policies
  • Sample Data Quality Rules
  • Sample Data Quality Management Plan
  • Sample Data Governance Council Agenda
  • Steward Training
  • Sample Charter
  • Owner Training
Build an MDM Business Case
Master data management a critical component of an effective and sustainable data governance strategy. Too many organizations short-change or skip this critical step due to the difficulty and time involved.

Marlabs partners with Profisee to help your organization establish an MDM strategy that is automated and monitored. At no cost, Marlabs and Profisee will help you build a business case for master data management in your organization.
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How Intelligent Automation and AI Can Enable Data Governance

Intelligent automation goes far beyond typical process automation. By leveraging AI and machine learning, IA transcends integration and empowers effective governance.
Eliminates Error: IA systems can greatly reduce errors both by automating tasks that are prone to human mistakes and by automating the verification and validation of inputs.
Data Consistency: IA can ensure data and business processes are processed and handled the same way every time, resulting in consistency and accuracy.
Data Integrity: IA helps maintain data accuracy and reliability by acting as a control for unauthorized changes and duplicated or conflicting information and by monitoring for anomalies.
Data Transparency: IA provides better visibility into data trends and patterns. It can provide unparalleled insight, context, and real-time reporting of essential information.

Why Is Data Governance Important to AI?​

As artificial intelligence (AI) becomes more integrated into decision-making processes, having a solid data governance framework is essential. So, why is data governance necessary for AI?​

Data Quality:

AI models depend on high-quality, accurate, and complete data. Poor data governance leads to flawed inputs, which result in unreliable AI outcomes.​

Data Security & Compliance:

AI systems must adhere to regulations (GDPR, HIPAA, etc.). Data governance ensures that data handling meets compliance standards, safeguarding the organization against legal risks.​

Data Integration:

AI systems require data from multiple sources. Proper data governance ensures consistency and standardization, making it easier to integrate and analyze data from disparate systems.

Accountability & Transparency:

Governance provides a clear chain of responsibility to ensure that data owners, stewards, and users are accountable for data integrity, security, and usage, which are critical for ethical AI use.

Scalability & Efficiency:​

As businesses grow, so does the amount of data they generate. Data governance ensures AI systems can scale without sacrificing data quality or performance.

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