Drawing from multiple client engagements across sectors, Tesfaye and DeMay shared patterns and lessons learned from initiatives where KM, Data Management, and AI teams are working together to create a more connected and intelligent enterprise. They highlighted the growing need for integrated strategies that bring together semantic modeling, content management, and metadata governance to enable intelligent automation and more effective knowledge discovery.

The presentation emphasized how KM professionals can lead the way in designing sustainable semantic architectures, building cross-functional partnerships, and aligning programs with organizational priorities and AI investments. Presenters also explored how roles are shifting from traditional content stewards to strategic enablers of enterprise intelligence.

Session attendees walked away with:

• Insight into how KM roles are expanding to meet enterprise-wide data and AI needs;
• Examples of how semantic layers can enhance findability, improve reuse, and enable automation;
• Lessons from organizations integrating KM, Data Governance, and AI programs; and
• Practical approaches to designing cross-functional operating models and governance structures that scale.

The post The Evolution of Knowledge Management & Organizational Roles: Integrating KM, Data Management, and Enterprise AI through a Semantic Layer appeared first on Enterprise Knowledge.

The Solution

EK worked with the agency’s data office to develop a strategy to collect, connect, and distribute data. Focused on helping the agency improve data capture, quality, usage, and lineage, EK collaborated with over 15 executives and engaged with stakeholders such as project coordinators, system owners, and everyday users to collect feedback and align data management strategies with specific use cases and the agency’s strategic initiatives. Key activities that informed our resulting recommendations included:

• Hosting working group meetings to foster collaboration and alignment across different business areas, regions, and staff offices.
• Providing a strategic and measurable assessment of the agency’s current data processes, using EK’s Data Maturity Benchmark to establish a baseline for improvement.
• Reviewing a sampling of over 100 organizational memos, policies, and manuals to ensure solutions were in accordance with industry standards and regulatory mandates.
• Executing and designing a collaborative, custom-built Organizational Data Needs Assessment Survey informed by stakeholder and organizational objectives; and achieving target demographic participation through the development and implementation of a strategic Communications Plan.
• Conducting 8+ comprehensive software system capability assessments evaluating the organization’s data and metadata landscape to identify strengths, challenges, and gaps within existing metadata management practices and tools.
• Performing a gap analysis to identify areas of alignment between existing tools and business needs.
• Developing a reusable Tool Evaluation Matrix to evaluate data tools against the agency’s specific functional, operational, and business needs.
• Engaging over 70 people across 23 interviews and focus groups with diverse business units to gather crucial perspectives to inform long-term recommendations, goals, and objectives.
• Facilitating townhalls and workshops to garner executive and organizational buy-in.

The EK Difference

EK developed a comprehensive understanding of the agency’s needs through a mix of customized discovery activities and stakeholder engagement. By leveraging EK’s proprietary Data Maturity Benchmark, deep knowledge of industry best practices, and a collaborative approach with key agency stakeholders, EK delivered a tailored solution that provided the agency with a clear understanding of existing system capabilities, a list of similar tools and their features that could be procured in the future, and actionable recommendations to optimize its tool investment strategy. These efforts ultimately enable the agency to make strategic, informed decisions to enhance efficiency, data accessibility, and long-term operational success.

EK’s extensive experience in developing data management strategies and solutions resulted in recommendations that were both data-driven and aligned with the agency’s existing processes and available resources. With EK’s support, the agency now has practical, scalable approaches to enhance and modernize their data management practices through improved access and visibility across resources.

The Results

EK ultimately provided this federal agency with actionable recommendations, ranging from people-oriented incentives to technical investments, to improve their data management processes. EK helped the organization develop quantifiable metrics to enhance data accessibility, quality assurance, and utilization. Where employees previously faced challenges related to a decentralized organization, such as navigating multiple data catalogs, EK enabled the organization to create a more cohesive and user-friendly data environment by establishing standardized metrics for completeness, quality control, and data management. This reduced frustration and inefficiencies in finding and reusing critical datasets across the organization.

EK’s adaptable Tool Evaluation Matrix allows the agency to effectively analyze future investment considerations in metadata management, data catalog, and data repository systems. Additionally, EK’s efforts have provided the organization with action-oriented approaches to address critical gaps to advance their data management initiatives. These foundations will accelerate the agency’s ability to scale their data management processes and explore advanced data solutions, such as metric dashboarding, implementation of an enterprise-wide inventory or data catalog, and advanced architecture and discoverability capabilities. Consequently, the improvements made based on these foundations will empower personnel to efficiently locate, share, and utilize existing data, reducing duplication and fostering a more collaborative and data-driven culture. All of these improvements will assist the agency in building foundational elements of a cloud data analytics program.

Interested in improving your organization’s data management processes? Contact us today!

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The post Maturing Data Processes at a Decentralized Federal Organization appeared first on Enterprise Knowledge.

This blog explores how the integration of secure access management within an enterprise AI framework can transform enterprise AI systems from simple automation tools into secure, context-aware knowledge platforms. We’ll discuss approaches for how modern Role-Based Access Control (RBAC), enhanced by AI capabilities, works to streamline and create a dynamic ecosystem where information flows securely to those who need it most.

Understanding Enterprise AI and Access Control

Enterprise AI represents a significant advancement in how organizations process and utilize their data, moving beyond basic automation to intelligent, context-aware systems. This awareness becomes particularly powerful when combined with sophisticated access management systems. Role-Based Access Control (RBAC) serves as a cornerstone of this integration, providing a framework for regulating access to organizational knowledge based on user roles rather than individual identities. Modern RBAC systems, enhanced by AI, go beyond static permission assignments to create dynamic, context-aware access controls that adapt to organizational needs in real time.

Key Features of AI-Enhanced RBAC

1. Dynamic Role Assignment: AI systems continuously analyze user behavior, responsibilities, and organizational context to suggest and adjust role assignments, ensuring access privileges remain current and appropriate.
2. Intelligent Permission Management: Machine learning algorithms help identify patterns in data usage and access requirements, automatically adjusting permission sets to optimize security while maintaining operational efficiency, thereby upholding the principles of least privilege in the organization.
3. Contextual Access Control: The system considers multiple factors including time, location, device type, and user behavior patterns to make real-time access decisions.
4. Automated Compliance Monitoring: AI-powered monitoring systems track access patterns and flag potential security risks or compliance issues, enabling proactive risk management.

This integration of enterprise AI and RBAC creates a sophisticated framework where access controls become more than just security measures – they become enablers of knowledge flow within the organization.

Secure Access Management for Enterprise AI

Integrating access management with enterprise AI creates a foundation for secure, intelligent knowledge sharing by effectively capturing and utilizing organizational expertise.

Modern enterprises require a thoughtful approach to incorporating domain expertise into AI processes while maintaining strict security protocols. This integration is particularly crucial where domain experts transform their tacit knowledge into explicit, actionable frameworks that can enhance AI system capabilities. The AI-RBAC framework embodies this principle through two key components that work in harmony:

1. Adaptable Rule Foundation (ARF) for systematic content classification
2. Expert-driven Organizational Role Mapping for secure knowledge sharing

While ARF provides the structure for explicit knowledge through content tagging, the role mapping performed by Subject Matter Experts (SMEs) injects critical domain intelligence into the organizational knowledge framework, creating a robust foundation for secure knowledge sharing. The ARF system exemplifies this integration by classifying and managing data across three distinct levels, while SMEs provide the crucial expertise needed to map these classifications to organizational roles. This combination ensures that organizational knowledge is not only properly categorized but also securely accessible to the right people at the right time, effectively bridging the gap between AI-driven classification and human expertise.

The Adaptable Rule Foundation (ARF) system exemplifies this integration by classifying and managing data across three distinct levels:

• Core Level: Includes fundamental organizational knowledge and critical business rules, defined with input from domain SMEs.
• Common Level: Contains shared knowledge assets and cross-departmental information, with SME guidance on scope.
• Unique Level: Manages specialized knowledge specific to individual departments or projects, as defined by SMEs.

SMEs play a crucial role in adjusting the scope and definitions of the Core, Common, and Unique levels to inject their domain expertise into the ARF framework. This ensures the classification system aligns with real-world organizational knowledge and needs.

This three-tiered approach, powered by AI, enables organizations to:

• Automatically classify incoming data based on sensitivity and relevance
• Dynamically apply appropriate access controls using expert-driven organizational role mapping
• Enable domain experts to contribute knowledge securely without requiring technical expertise
• Adapt security measures in real-time based on organizational changes

The ARF system’s intelligence goes beyond traditional access management by understanding not just who should access information, but how that information fits into the broader organizational knowledge ecosystem. This contextual awareness ensures that security measures enhance, rather than hinder, knowledge sharing.

The Future of Enterprise AI

As organizations continue to leverage AI capabilities, the interaction between access management and enterprise AI becomes increasingly crucial. This integration ensures that AI systems serve as secure, intelligent platforms for knowledge sharing and decision-making. The combination of dynamic access controls and enterprise AI framework creates an environment where:

• Security becomes an enabler rather than a barrier to innovation
• Domain expertise naturally flows into AI systems through secure channels
• Organizations can adapt quickly to changing knowledge needs while maintaining security
• AI systems become more contextually aware and organizationally aligned

If your organization is looking to enhance AI capabilities while ensuring robust data security, our enterprise AI access management framework offers a powerful solution. Contact us to learn how to transform your organization’s knowledge infrastructure into a secure, intelligent ecosystem that drives innovation and growth.

The post Enterprise AI Meets Access and Entitlement Challenges: A Framework for Securing Content and Data for AI appeared first on Enterprise Knowledge.

The future of enterprise data management and architecture is evolving rapidly in some areas and returning to core principles in others. Based on our experience through our engagements across industries, diverse projects and client use cases, and our vendor partnerships, we continue to have the opportunity to observe and address the dynamic challenges organizations are facing in managing and getting value out of their data. These interactions, coupled with inputs from our advisory board, are helping us gain a good picture of the evolving landscape. 

Drawing from these sources, I have identified the key trends in the data management and architecture space that we expect to see in 2025. Overall, these trends highlight how organizations are adapting to technological advancements while shifting towards a more holistic approach – focusing on people, processes, and standards – to maximize returns on their data investments.

1. Wider Adoption of a Business or Domain-Focused Data Strategy

The conventional approach to data management architecture often involved a monolithic architecture, with centralized data repositories and standardized reporting systems that served the entire organization. While this worked for basic reporting and operational needs, the last decade has proven that such a solution couldn’t keep pace with the complexities of modern businesses. In recent years, a more agile and dynamic approach has gained momentum (and adoption) – one that is putting the business first. This shift is driven by the growing need not only to manage vast and diverse data but also to address the persistent challenge of minimizing data duplication while making data actionable, relevant, and directly aligned with the needs of specific business users.

A Business or Domain-Focused data strategy approach emphasizes decentralized data ownership and federated governance across various business domains (e.g., customer service, HR, sales, operations) – where each domain or department owns “fit-for-purpose” tools and the data within. As a result, data is organized and managed by the business function it supports, rather than by data type or format. 

This has been an emerging trend for a couple of years and part of the data mesh architecture. It is now gaining traction through the wider adoption of business-aligned data products or data domains in support of business processes – where data products empower individual business units to standardize and contextualize their data and derive actionable insights without heavy reliance on central IT, data teams, or enterprise-wide platforms. Why is this happening now? We are seeing two key drivers fueling the growing adoption of this strategy:

1. The shift in focus from the physical data to descriptive metadata, and the advancement in the corresponding solutions that enable this approach (such as a semantic layer or data fabric architectures that connect domain-specific data platforms without the need for data duplication or migration); and 
2. The rise of Artificial Intelligence (AI), specifically Named Entity Recognition (NER), Natural Language Processing (NLP), Large Language Models (LLMs) and Machine Learning (ML) – playing a pivotal role in augmenting organizational capabilities with automation.

As a result, we are starting to see the traditional method of relying on static reports and dashboards becoming obsolete. By integrating the federated capabilities and trends discussed below, we anticipate organizations moving beyond static reporting dashboards to the ability to “talk” to their data in a more dynamic and reliable way.

2. Semantic Layer Data Architecture

One of the key concepts that is significantly fueling the adoption of modern data stacks today is the “zero-copy” principle – building a data architecture that greatly reduces or eliminates the need to copy data from one system to another, thus allowing organizations to access and analyze data from multiple sources in real-time without duplicating it. This principle is changing how organizations manage and interact with their data.

In 2020, I first discussed Semantic Layer Architecture through a white paper I published called, What is a Semantic Architecture and How do I Build One?. In 2021, Gartner dubbed it “a data fabric/data mesh architecture and key to modernizing enterprise data management.” As the field continues to evolve, technical capabilities are advancing semantic solutions. A semantic layer in data architecture takes a metadata-first approach and is becoming an essential component of modern data architectures, enabling organizations to simplify data access, improve consistency, and enhance data governance. 

From an architect’s point of view, a semantic layer architecture adds significant value to modern data architecture and it is becoming a trend organizations are embracing – primarily because it provides the framework for addressing these traditional challenges for the data organization:

• Business alignment through standardized metadata by translating business context and relationships between raw data through metadata and ontology, making it ‘machine reliable’; 
• Simplified data access for business users through shared vocabulary (taxonomy);
• Enhanced data connection and interoperability through a virtualized access and central source of “view” that connects data (through metadata) from various sources without requiring the physical movement of data; 
• Improved data governance and security by enforcing the application of consistent business definitions, metrics, and data access rules to data; and
• The flexibility to future-proof data architecture by decoupling the complexities of data storage and presentation facilitates a zero-copy principle and ensures data remains where it is stored, without unnecessary duplication or replication, This helps organizations create a virtualized layer to address the challenges of working with diverse data from multiple sources while maintaining consistency and usability.

This trend reflects a broader shift from legacy application/system-centric architecture to a more data-centric approach where data doesn’t lose its meaning and context when taken out of a spreadsheet, a document, SQL table, or a data platform – helping organizations unlock the true potential of their knowledge and data.

3. Consolidation & Rebundling of Data Platforms 

The enterprise data technology landscape has been going after the “modern data stack” strategy, characterized by a best-of-breed approach, where organizations adopt specialized tools from various vendors to fulfill different needs – be it data storage, analytics, data cataloging and discovery, or AI. However, with the growing complexity of managing multiple platforms and tighter budgets, organizations are facing mounting pressures to optimize. 

Much akin to the retro experience that we’re seeing within the TV streaming industry, the landscape of data technologies is undergoing a significant shift – one of rebundling. This change is primarily driven by the need to simplify data management solutions in order to handle increasing organizational data complexity, optimize the costs associated with data storage and IT infrastructure across multiple vendors, and enhance the ability to experiment with and extract value from AI.

As a result, we are seeing the pace of technology bundling and mergers and acquisitions accelerating as large, well-established data platforms are acquiring smaller, specialized vendors and offering integrated, end-to-end solutions that aim to simplify data management. One good, well-publicized example of this is Salesforce’s recent bundling with and acquisition of various vendors to unveil the Unlimited Edition+ bundle, which provides access across Slack, Tableau, Sales Cloud, Service Cloud, Einstein AI, Data Cloud, and more, all in a single offering. In a recent article, my colleague further discussed the ongoing consolidation in the semantic data software industry, highlighting how the sector is increasingly recognizing the importance of semantics and how well-funded software companies are acquiring many independent vendors in this space to provide more comprehensive semantic layer solutions to their customers.

In 2025, we expect more acquisitions to be on the horizon. For CIOs and CDAOs looking to take advantage of this trend, there are important factors to consider. 

Limitations and Known Challenges:

• Complexity in data migration: Migrating data from multiple platforms into a unified one is a resource-intensive process. Such transitions typically introduce disruptions to business operations, leading to downtime or performance issues during the shift. 
• Data interoperability: The ability of different data systems, platforms, applications, and organizations to exchange, interpret, and use data seamlessly across various environments is paramount in today’s data landscape. This interoperability ensures data flows without losing its meaning, whether within an organization (e.g., between departments and various systems) or externally (e.g., regulatory reporting). Single-vendor technology bundles are often optimized for internal use, and they can limit data exchange with external systems or other vendors’ tools. This creates challenges and costs when trying to integrate non-vendor systems or migrate to new platforms. To mitigate these risks, it’s important for organizations to adopt solutions based on standardized data formats and protocols, invest in middleware and APIs for integration, and leverage cloud-based systems that support open standards and external system compatibility.
• Potential vendor lock: By committing to a single platform, organizations often become overly dependent on a specific vendor’s technology for all their data needs. This limits the data organization’s flexibility, especially when new tools or platforms are required, forcing the use of a proprietary solution that may no longer meet your evolving business needs. Relying on one platform also restricts data access and complicates integration with other systems, hindering the ability to gain holistic insights across your organizational data assets.

Benefit Areas:

• Better control over security and compliance: As businesses integrate AI and other advanced technologies into their data stacks, having a consolidated security framework is particularly top of mind. Facilitating this simplification through a unified platform reduces the risks associated with managing security across multiple platforms and helps ensure better compliance with regulatory data security requirements.
• Streamlined access and entitlement management: Consolidating the management of organizational access to data, roles, and permissions allows administrators to unify user access to data and content across applications within a suite – typically from a central dashboard, making it easier to enforce consistent access policies across all connected applications. This streamlines better management to prevent unauthorized access to critical data. It helps ensure that only authorized users have the appropriate access to diverse types of data, including AI models, algorithms, and media, strengthening the organization’s overall security posture.
• Simplified vendor management: Using a single vendor for a bundled suite reduces the administrative complexity of managing multiple vendors, which sometimes involves different support processes, protocols, and system compatibility issues. A unified data platform provides a more streamlined approach to handling data across systems and a single point of contact for support or troubleshooting.

When properly managed, bundling has its benefits; the focus should be on finding the balance, ensuring that data interoperability concerns are addressed while still leveraging the advantages of bundled solutions. Depending on the priority for your organization, this trend will be beneficial to watch (and adopt) for your streamlined data landscape and architecture.

4. Refocused Investments in Complementary AI Technologies (Beyond LLMs)

While LLMs have garnered significant attention in conversational AI and content generation, organizations are now recognizing that their data management challenges require more specialized, nuanced, and somewhat ‘traditional’ AI tools that address the gaps in explainability, precision, and the ability to align LLMs with organizational context and business rules. 

Despite the draw to AI’s potential, many organizations prioritize the reliability and trustworthiness of traditional knowledge assets. They also want to integrate human intelligence, ensuring that an organization’s collective knowledge – including people’s experience and expertise – is fully captured. We refer to this as Knowledge Intelligence (KI) rather than just AI, to indicate the integration of tacit knowledge and human intelligence with AI, thereby capturing the deepest and most valuable information within an organization.

As such, organizations have started reinvesting in Natural Language Processing (NLP), Named Entity Recognition (NER), and Machine Learning (ML) capabilities, realizing that these complementary AI tools are just as essential in tackling their complex enterprise data and knowledge management (KM) use cases. Specifically, we are seeing this trend reemerging to embrace the advancements in AI capabilities for enabling the following key priorities for the enterprise. 

• Expert Knowledge Capture & Transfer: Programmatically encoding expert knowledge and business context in structured data & AI;
• Knowledge Extraction: Federated connection and aggregation of organizational knowledge assets (unstructured, structured, and semi-structured sources) for knowledge extraction; and
• Business Context Embedding: Providing standardized meaning and context to data and all knowledge assets in a machine-readable format.

We see this renewed focus in holistic AI technologies as more than just a passing shift, it is marking a pivotal trend in the world of enterprise data management as a strategic move toward more reliable, intelligent, and efficient information and data management. 

For organizations looking to enhance their ability to extract value from experts and diverse data and content assets, the trend in comprehensive AI capabilities facilitates this integration and ensures that AI can operate not just as a tool, but as an intelligent organizational partner that understands the unique nuances of an organization – ultimately delivering knowledge and intelligence to the data organization.

5. A Unified Approach to Data and Content Management: Data & Analytics Teams Meet Unstructured Content & Knowledge Management

One of the most subtle yet significant changes we have been seeing over the last 2-3 years is the blending of traditionally siloed data management functions. In particular, the boundary between data and knowledge management teams is increasingly dissolving, with data and analytics professionals now addressing challenges that were once primarily the domain of KM. This shift is largely due to the growing recognition that organizations need a more cohesive approach to handling both structured and unstructured content.

Just a few years back, data management was largely a function of structured data, confined to databases and well-defined formats and handled by data engineers, data analysts, and data governance officers. Knowledge and content management, on the other hand, dealt primarily with unstructured content such as documents, emails, and multimedia, managed by different teams including knowledge officers and document management specialists. 

However, in 2025, as organizations continue to strive for a more flexible approach to benefit from their overall organizational knowledge assets, we are witnessing a convergence where data teams are now actively engaged in managing unstructured knowledge. With advancements in GenAI, machine learning, NER, and NLP technologies, data and analytics teams are now expected to not only manage and analyze structured data but also tackle the complexities of unstructured content – ranging from documents, emails, text, and social media posts to contracts and video files. 

By bridging the gap between data teams and business-oriented KM teams, organizations are able to better connect technical initiatives to actual use cases for employees, customers, and their stakeholders. For example, we are seeing a successful adoption of this trend with the data & analytics teams at a large global retailer. We are supporting their content and information management teams’ ability to enable the data teams with a knowledge and semantic framework to aggregate and connect traditionally siloed data and unstructured content. The KM team is doing this by providing knowledge models and semantic standards such as metadata, business glossaries, and taxonomy/ontology (as part of a semantic layer architecture) – explicitly providing business context for data, categorizing and labeling unstructured content, and providing the business logic and context for data used in their AI algorithms.

In 2025, we expect to see this trend to become more common for many organizations looking to enable cross-functional collaboration, with traditional data and IM offices starting to converge and professionals from diverse backgrounds working together to manage both structured and unstructured data.

5. Shift in Organizational Roles: From Governance to Enablement

This trend reflects how the previously mentioned shifts are becoming a reality within enterprises. As organizations embrace a more integrated approach to connecting overall organizational knowledge assets, the roles within the organization are also shifting. Traditionally, data governance teams, officers, and compliance specialists have been the gatekeepers of data quality, privacy, and security. While these roles remain crucial, the focus is increasingly shifting toward enablement rather than control.

Additionally, knowledge managers are steadily growing beyond their traditional role of providing the framework for sharing, applying, and managing the knowledge and information of an organization. They are now also serving as the providers of business context to data teams and advancements in Artificial Intelligence (AI). This heightened visibility for KM has pushed the industry to identify more optimized ways to organize teams and measure and convey their value to organizational leaders. On top of that, AI has been fueling the democratization of knowledge and data, leading to a growing recognition of the interdependence between data, information, and knowledge management teams. 

This is what is driving the evolution of roles within KM and data from governance and control to enablement. These roles are moving away from strict oversight and regulation and towards fostering collaboration, access, and self-sufficiency across the organization. Data officers and KM teams will continue to play a critical role in setting the standards for data quality, privacy, and security. However, as their roles shift from governance to enablement, these teams will increasingly focus on establishing frameworks that support transparency, collaboration, and compliance across a more data-centric enterprise – availing self-service analytics tools that allow even non-technical staff to analyze data and generate insights independently.

As we enter 2025, the landscape of enterprise data management is being reshaped by shifts in strategy, architecture, platform focus, and the convergence of data and knowledge management teams. These changes reflect how organizations are moving from siloed approaches to a more connected, enablement-driven model. By leveraging a combination of AI-powered tools, self-service capabilities, and evolving governance practices, organizations are unlocking the full value of their data and knowledge assets. This transformation will enable faster, more informed decision-making, helping companies stay ahead in an increasingly competitive and rapidly evolving business environment.

How do these trends translate to your specific data organization and landscape? Is your organization embracing these trends? Read more or contact us to learn more and grow your data organization.

The post Data Management and Architecture Trends for 2025 appeared first on Enterprise Knowledge.

Our approach begins with a current state assessment to evaluate the maturity and effectiveness of your existing governance structures. We use EK’s proprietary governance matrix to assess key areas, such as roles and responsibilities, processes, technology integration, and cultural alignment. This assessment helps us identify areas of strength and opportunities for improvement.

Throughout the next six months, we lead cross-functional workshops to align stakeholders—including business units, IT, and executive leadership—on a unified vision for governance. These sessions focus on tailoring governance frameworks to your specific use cases, ensuring that your governance structure addresses your most pressing data challenges.

As part of the interactive working sessions, EK delivers role-based training to key stakeholders, such as data stewards and data analysts, guiding them through the creation of operating models, governance run-books, and procedural workflows. Our approach ensures that these processes are adaptable and scalable as your organization grows.

During the executive planning workshops, we work with your leadership team to establish a long-term roadmap for governance, outlining clear tasks and use cases that ensure continuous improvement. We also provide templated workbooks to guide the implementation of these governance structures, ensuring they can be easily applied across different business units and functions. For organizations seeking a more immediate implementation, we offer an optional pilot, where a governance prototype is developed and tested within a specific business unit or domain.

By the end of the Data Governance Program Starter Kit, your organization will receive:

• Expert-Crafted Elements of a Successful Governance Framework: Including operating models, workflows, and governance run-books tailored to your specific needs. These models and processes will ensure that governance is not only scalable but also embedded in your daily operations.

• Processes and Procedures: Custom-designed processes that incorporate governance rules and compliance standards into your organization’s workflows, ensuring that data quality and security are maintained across the enterprise.

• Templated & Ready-to-Use Workbooks: A set of reusable working materials designed to guide the rapid implementation of governance structures, ensuring that processes can be replicated across business units and adapted as necessary.

• Roadmap of Tasks & Use Cases: A clear, actionable roadmap outlining the steps necessary to maintain and expand your governance framework. This roadmap will provide the strategic direction needed ensure that your governance program evolves as your data needs grow.

• Training: Role-based training for key governance stakeholders, such as data stewards and data analysts, ensuring that your team is equipped to manage and expand governance practices long after the engagement ends.

• Optional Pilot Implementation: EK offers the option to implement a governance prototype within a high-priority domain, providing an opportunity to test governance practices in a real-world setting before scaling across the organization.

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EK’s approach to the Data Catalog Expansion Workshop is crafted to ensure the long-term success of your data catalog initiatives already underway.

During this 3-week workshop, we will focus on how to address common pain points across five critical areas: Governance, Business Glossary, Technical Integrations, People, and Culture.

This will facilitate intensive collaborative sessions across business units, the IT organization, and leadership to ensure that catalog tasks are understood and effectively implemented. These sessions aim to improve the maturity of your data catalog practices, institute preventative measures against recurring issues, and secure executive buy-in to drive the direction of the catalog program.

By the end of the Data Catalog Expansion Workshop, your organization will be equipped with the skills needed to recommend Tactical and Strategic Approaches to Address Identified Gaps. Based on these gaps, EK will provide actionable insights to enhance your governance and catalog structure. This will include improvements in the following areas:

• People and Roles: Defining clear roles and responsibilities for ongoing catalog management and data stewardship.
• Glossary Development and Metadata Modeling: Establishing and refining metadata frameworks to enhance data discoverability and usability.
• Processes and Procedures: Streamlining operations to enhance data governance and catalog maintenance.
• Culture: Cultivating a data-centric culture across all levels of the organization to support sustained data governance efforts.

Roadmap Based on Iterative Working Sessions: We will create a well-defined, customized action plan that focuses on key areas like glossary development and data source integrations. This roadmap will not only address immediate blockers but also lay a foundation for expanding critical governance elements that were identified during the workshop.

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EK’s Data Governance Maturity Engagement leverages a proprietary governance maturity benchmark developed in conjunction with multiple enterprise-scale clients to conduct a baseline assessment of your data governance program across five key spectrums: Governance, People, Processes, Technology, and Culture. This assessment provides a clear comparison between your organization’s current state and the target maturity level required to support advanced data governance practices.

During the first four weeks, we facilitate collaborative workshops across your business units, IT, and leadership teams to pinpoint key governance challenges and diagnose the gaps impacting data governance maturity. Through interactive sessions, we ensure that all stakeholders understand the intricacies of governance, aligning on a unified vision for improvement.

By the end of the engagement, EK will create a high-level actionable plan that outlines specific steps for improving your governance program. This plan breaks down critical areas such as required people and roles, processes and procedures, technology alignment, and cultural adoption, ensuring that governance improvements are achievable and sustainable over time. We also provide a starter governance model that your organization can immediately implement and scale as your governance needs evolve.

At the conclusion of the engagement, your organization will have:


Measurable Maturity Score: Receive a data governance maturity score based on expert-crafted criteria, providing a detailed view of how your current governance program measures up against industry standards.

Actionable Plan to Improve Governance: A comprehensive roadmap that breaks down targeted areas for improvement, including:

• People and Roles: Establishing clear ownership and accountability within your governance structure.
• Processes and Procedures: Enhancing workflows to ensure consistency, compliance, and scalability.
• Technology Alignment: Evaluating and recommending tools that support governance and enhance data management capabilities.
• Culture: Facilitating the necessary cultural shifts to promote data stewardship and ensure buy-in from all stakeholders.

Starter Governance Model & Roadmap: A well-defined, actionable plan to maintain and expand on governance improvements, ensuring your data governance program can adapt to future challenges.

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Our 12-week Data Product Accelerator begins with use case discovery workshops to define the business problems and opportunities that your data products will address. Working closely with your Data Product Owners and Stewards, we focus on defining high-priority use cases that will deliver immediate value, such as executive reporting or just-in-time analytics. These workshops will align stakeholders on the expected outcomes of the data products, ensuring a unified vision across the organization.

During the first month, EK leads collaborative roadmap and design sessions to outline the strategy for data product development. These sessions are designed to help your organization understand the requirements and considerations of data product creation, from data integration and enrichment to analytics and reporting. By leveraging best practices and industry standards, we guide your team through the process of designing products that are fit-for-purpose and aligned with your business priorities.

Over the next two months, we work with embedded development teams to implement and integrate data products into your existing infrastructure. This phase includes the development of metadata models, calculation rules, and contextual data integrations that ensure data products are scalable and reusable across the organization. Our team collaborates closely with your internal teams to deliver 2-3 data products that meet immediate business needs, driving adoption and showcasing the value of data-driven decision-making.

By the end of the Data Product Accelerator engagement, your organization will have:

• Defined, Prioritized Data Product Use Cases Aligned to Business Needs: Clear, tailored use cases for initial data product implementation, ensuring alignment with your organization’s most pressing business needs and setting the stage for future expansion.
• Implemented Prototypes for Immediate Use and Value Demonstration: Implementation and integration of 2-3 data products for prioritized use cases, demonstrating how data products can be leveraged to solve specific business problems and support operational goals.
• Clarity and Repeatability: Creation of structured metadata models, calculation rules, and contextual integrations that ensure the scalability and repeatability of your data products, with clear documentation to guide ongoing management.
• Custom Guidance to Scale and Drive Adoption: Customized training and knowledge transfer sessions to ensure that your internal teams can maintain, scale, and expand the data product development process, with a focus on long-term sustainability.

These deliverables ensure that your organization is equipped with the frameworks, tools, and knowledge required to build and maintain a scalable, value-driven data product pipeline.

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This infographic serves as your roadmap to understanding and uncovering dark data within your organization, showcasing practical interventions to secure and capitalize on your most underutilized assets. At Enterprise Knowledge, we specialize in transforming complex data landscapes into structured, actionable intelligence. If you’re ready to enhance your data management strategies and mitigate risks associated with dark data, reach out to our experts today for tailored solutions that empower your data governance efforts. 

For information on dark data discovery, explore this infographic on Unlocking Dark Data: AI Strategies for Enhanced Data Governance.

The post How Data Becomes Dark appeared first on Enterprise Knowledge.

We’re thrilled to announce that the agenda for the highly anticipated Semantic Layer Symposium is now finalized! This event will bring together top minds in data management and industry leaders to explore the evolving role of Semantic Layers in organizations.

With just five weeks to go, excitement is building as we prepare to gather in Munich on October 17th, at the iconic Bayerischer Hof Hotel.

With the agenda now finalized, we invite you to secure your spot and be a part of this fantastic event. Tickets are limited, so don’t wait—register now, and join us for a day of learning, innovation, and networking. 

Register here, and download our agenda here!

Why Attend?

This one-of-a-kind symposium offers attendees actionable insights on how to leverage Semantic Layers to connect and optimize organizational data and knowledge, enhancing decision-making, operational efficiency, and insight generation. 

Join us to:

• Learn practical strategies for creating a more intelligent enterprise through better data integration;
• Gain firsthand knowledge from expert speakers on how to implement semantic standards and improve data interoperability; and
• Hear about real-world case studies showcasing the transformative potential of Semantic Layers across various industries.

Highlights of the Day

• Expert Panels, Sessions, & Keynotes: Hear from data leaders like Malcolm Hawker, Tiankai Feng, Daniel Canter, Lulit Tesfaye, Andreas Blumauer, Atanas Kiryakov, David Clarke, and Peter Hopfgartner who will share their experiences and best practices.
• Networking Reception: End the day with a relaxed networking session at the Bayerischer Hof rooftop bar, overlooking Munich’s skyline.

Get to Know our Speakers!  

• Malcolm Hawker – Malcolm Hawker is a thought leader in the fields of Data Strategy, Master Data Management (MDM), and Data Governance. As a former Gartner analyst, Malcolm has authored industry-defining research and has consulted some of the largest businesses in the world on their enterprise data and analytics strategies. Having served as a Chief Product Officer, Head of IT, and strategic business consultant, Malcolm is an industry leader with over 25 years’ experience at the forefront of data-enabled business transformations. Malcolm is a frequent public speaker on data and analytics best practices, and he cherishes the opportunity to share practical and actionable insights on how companies can achieve their strategic imperatives by improving their approach to data management.
  

• Daniel Canter – ​​Daniel Canter is the ASML Learning and Knowledge Management Center of Expertise Knowledge Management Lead with 25 years’ experience in research, market intelligence, strategy and knowledge management. Daniel has an in depth understand of the data, information and knowledge inside organizations and how it is applied to drive business decisions. He has spent much of his career applying knowledge management for the improvement and development of organizations and people. During his career Daniel has been successful in developing enterprise taxonomies, content libraries, knowledge management systems, engaged communities of practice and many other of the tools of knowledge management. Daniel has worked mostly for Global companies, many of which are household names, and now heads up the central ASML knowledge management team.
  
• Dr. Norbert Gergely – Dr. Norbert Gergely is the Head of Data Architecture and Lifecycle at Zeiss Group. He has advanced data management and architecture experience across roles at MAN Truck & Bus, Vodafone, and Sky Germany. His expertise spans over server-less architectures, cloud migrations, and governance, significantly impacting data strategy and operations. Inside Zeiss, his team is responsible for developing data oriented self-service capabilities integrated in inward customer facing applications, thus supporting the adoption in the area of data lifecycle and product management, metadata management, and semantics.

• Tiankai Feng – Tiankai Feng is the Data Strategy & Data Governance Lead at Thoughtworks Europe. With 10+ years experience in Data Analytics, Data Governance and Data Strategy, he found a passion for the human aspect of data: how to collaborate, communicate and be creative around data. He is the author of “Humanizing Data Strategy”, and is passionate about making data more understood, approachable and fun through unconventional methods like music and memes.
  

• Lulit Tesfaye – Lulit Tesfaye is a Partner and the VP for Knowledge & Data Services at Enterprise Knowledge, LLC., the largest global consultancy dedicated to Knowledge and Information management. Lulit brings over 15 years of experience leading diverse information and data management initiatives, specializing in technologies and integrations. Lulit is most recently focused on employing advanced Enterprise AI and semantic capabilities for optimizing enterprise data and information assets.
• Industry-leading technical panelists, including: 
  • Andreas Blumauer – CEO & Founder – Semantic Web Company 
  • Atanas Kiryakov – CEO & Founder – Ontotext 
  • David Clarke – Executive Vice President Semantic Graph Technology – Squirro 
  • Peter Hopfgartner – CEO – Ontopic

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