data and analytics governance

Data Governance

Data Governance, Analytics, and Data Sciences, unlike any time prior, and unlike any other organizational asset, have quickly become one of the most valuable assets for competitive organizations.  With AI and Data Sciences becoming more commonplace and much simpler to implement, the need for clean and useful data continues to grow.

At Stephens Insight Group, LLC, we believe that organizations should treat their data as an asset that everyone cultivates.  We will help you develop an Information Domain Management program, and then begin to build out appropriate data governance as each domain requires.  Each domain is different, and savvy Data Governance Practitioners will focus their efforts consistently across domains, yet specific to each.

In other words, domains that suffer from poor data quality should have the same Data Quality framework applied consistently across different families of information.  Domains that lack organizational awareness or have inconsistent language may need to implement metadata management best practices.  But not every domain needs a full Data Quality Framework, and not every domain needs an enterprise-wide business glossary.

We'll help you invest your time and resources with a use-case based approach, where you'll get the most value.  Then we'll help you find ways to showcase that value to build momentum for future Data Governance initiatives.

Defining Data & Analytics Governance

Data governance, at its core, enables organizations to benefit more from their data.  What software vendors and practitioners have turned that core value into is a series of products, tools, methodologies, and terminology that tends to vary less and less as time goes on.  According to current literature and modern trends, it's most closely associated with Data Quality Management and Metadata Management, but it in fact is much broader.  Data Governance encompasses all things related to the organization's cultural approach to data, along with roles, processes, councils, and structures intended to derive increased value from data.

Analytics Governance is the process organizations use to govern priorities for use-cases, and to set the standard for both the creation and consumption of analytics capabilities across the organization.  As organizations decentralize their data and analytics capabilities, the need to govern for consistency, efficiency, and quality becomes more and more critical.

Data & Analytics Governance Services

  • Data Governance Strategy - Design and Implementation
  • Analytics Governance Strategy - Design and Implementation
  • Metadata Management - Best-Practices, Approach
  • Master Data Management - Communication, Organizational Success, Strategy
  • Data Quality Management - Visualizing, Communicating, Strategy
  • Data Governance Structural Design
  • Analytics Standards Development
  • Roadmap & Roll-out
  • Value Stream Analysis
  • Domain-based Governance
  • Key Leadership Role Development

Advanced Specialties

  • Healthcare System
  • Healthcare Payer
  • Master Data Management/Reference Data Management
  • Communicating Value
  • Alignment to Operations

AI Governance

At the core of it, Artificial Intelligence is nothing but algorithms with certain sets of rules. AI systems can learn from the iteration of tasks where the computer data (aka machine learning algorithms) are fed to the system. This is exactly how machine learning can get better at doing their specified tasks, without any external interference. With AI moving at an exponential curve upwards, governing it is something that must be addressed by leaders and organizations of all kinds.

In addition to utilizing AI to your advantage in the marketplace it is vital to analyze risk, liabilities and dangers that lay ahead. For example, there have been recent legislation drafted and signed at the federal and/or state specific level. AI compliance is now an issue that must be focused on to minimize risk as well as maximize value.

Defining AI Governance

The process of creating and implementing policies, ethical guidelines, standards, and practices to ensure that AI systems are and remain safe, ethical, and beneficial for humans.

AI Governance Services

  • AI Governance Strategy - Design and Implementation
  • Roadmap & Rollout
  • Key Leadership Role Development
  • Leveraging AI Governance into Value
  • Risk Management through AI Governance

Advanced Specialties

  • Healthcare System
  • Healthcare Payer
  • Master Data Management/Reference Data Management
  • Communicating Value
  • Alignment to Operations