The question of data AI readiness stems from the very real concern that when AI is pointed at data that isn’t correct or that doesn’t have the right context associated with it, organizations could face risks to their reputation, their revenue, or their customers’ privacy. With the additional nuance that data brings by often being presented in formats that require transformation, lacking in context, and frequently containing multiple duplicates or near-duplicates with little explanation of their meaning, data (although seemingly already structured and ready for machine consumption) requires greater care than other forms of knowledge assets to comprise part of a trusted AI solution. 

This blog focuses on the key actions an organization needs to perform to ensure their data is ready to be consumed by AI. By following the steps below, an organization can use AI-ready data to develop end-products that are trustworthy, reliable, and transparent in their decision making.

1) Understand What You Mean by “Data” (Data Asset and Scope Definition)

Data is more than what we typically picture it as. Broadly, data is any raw information that can be interpreted to garner meaning or insights on a certain topic. While the typical understanding of data revolves around relational databases and tables galore, often with esoteric metrics filling their rows and columns, data takes a number of forms, which can often be surprising. In terms of format, while data can be in traditional SQL databases and formats, NoSQL data is growing in usage, in forms ranging from key-value pairs to JSON documents to graph databases. Plain, unstructured text such as emails, social media posts, and policy documents are also forms of data, but traditionally not included within the enterprise definition. Finally, data comes from myriad sources—from live machine data on a manufacturing floor to the same manufacturing plant’s Human Resources Management System (HRMS). Data can also be categorized by its business role: operational data that drives day-to-day processes, transactional data that records business exchanges, and even purchased or third-party data brought in to enrich internal datasets. Increasingly, organizations treat data itself as a product, packaged and maintained with the same rigor as software, and rely on data metrics to measure quality, performance, and impact of business assets.

All these forms and types of data meet the definition of a knowledge asset—information and expertise that an organization can use to create value, which can be connected with other knowledge assets. No matter the format or repository type, ingested, AI-ready data can form the backbone of a valuable AI solution by allowing business-specific questions to be answered reliably in an explainable manner. This raises the question to organizational decision makers—what within our data landscape needs to be included in our AI solution? From your definition of what data is, start thinking of what to add iteratively. What systems contain the highest priority data? What datasets would provide the most value to end users? Select high-value data in easy-to-transform formats that allows end users to see the value in your solution. This can garner excitement across departments and help support future efforts to introduce additional data into your AI environment. 

2) Ensure Quality (Data Cleanup)

The majority of organizations we’ve worked with have experienced issues with not knowing what data they have or what it’s intended to be used for. This is especially common in large enterprise settings as the sheer scale and variety of data can breed an environment where data becomes lost, buried, or degrades in quality. This sprawl occurs alongside another common problem, where multiple versions of the same dataset exist, with slight variations in the data they contain. Furthermore, the issue is exacerbated by yet another frequent challenge—a lack of business context. When data lacks context, neither humans nor AI can reliably determine the most up-to-date version, the assumptions and/or conditions in place when said data was collected, or even if the data warrants retention.

Once AI is introduced, these potential issues are only compounded. If an AI system is provided data that is out of date or of low quality, the model will ultimately fail to provide reliable answers to user queries. When data is collected for a specific purpose, such as identifying product preferences across customer segments, but not labeled for said use, and an AI model leverages that data for a completely separate purpose, such as dynamic pricing models, harmful biases can be introduced into the results that negatively impact both the customer and the organization.

Thankfully, there are several methods available to organizations today that allow them to inventory and restructure their data to fix these issues. Examples include data dictionaries, master data (MDM data), and reference data that help standardize data across an organization and help point to what is available at large. Additionally, data catalogs are a proven tool to identify what data exists within an organization, and include versioning and metadata features that can help label data with their versions and context. To help populate catalogs and data dictionaries and to create MDM/reference data, performing a data audit alongside stewards can help rediscover lost context and label data for better understanding by humans and machines alike. Another way to deduplicate, disambiguate, and contextualize data assets is through lineage. Lineage is a feature included in many metadata management tools that stores and displays metadata regarding source systems, creation and modification dates, and file contributors. Using this lineage metadata, data stewards can select which version of a data asset is the most current or relevant for a specific use case and only expose said asset to AI. These methods to ensure data quality and facilitate data stewardship can aid in action towards a larger governance framework. Finally, at a larger scale, a semantic layer can unify data and its meaning for easier ingestion into an AI solution, assist with deduplication efforts, and break down silos between different data users and consumers of knowledge assets at large. 

Separately, for the elimination of duplicate/near-duplicate data, entity resolution can autonomously parse the content of data assets, deduplicate them, and point AI to the most relevant, recent, or reliable data asset to answer a question. 

3) Fill Gaps (Data Creation or Acquisition)

With your organization’s data inventoried and priorities identified, it’s time to start identifying what gaps exist in your data landscape in light of the business questions and challenges you are looking to address. First, ask use case-based questions. Based on your identified use cases, what data would an AI model need to answer topical questions that your organization doesn’t already possess?

At a higher level, gaps in use cases for your AI solution will also exist. To drive use case creation forward, consider the use of a data model, entity relationship diagram (ERD), or ontology to serve as the conceptual map on which all organizational data exists. With a complete data inventory, an ontology can help outline the process by which AI solutions would answer questions at a high level, thanks to being both machine and human-readable. By traversing the ontology or data model, you can design user journeys and create questions that form the basis of novel use cases.

Often, gaps are identified that require knowledge assets outside of data to fill. A data model or ontology can help identify related assets, as they function independently of their asset type. Moreover, standardized metadata across knowledge assets and asset types can enrich assets, link them to one another, and provide insights previously not possible. When instantiated in a solution alongside a knowledge graph, this forms a semantic layer where data assets, such as data products or metrics, gain context and maturity based on related knowledge assets. We were able to enhance the performance of a large retail chain’s analytics team through such an approach utilizing a semantic layer.

To fill these gaps, organizations can look to collect or create more data, as well as purchase publicly available/incorporate open-source datasets (build vs. buy). Another common method of filling identified organizational gaps is the creation of content (and other non-data knowledge assets) to identify a gap via the extraction of tacit organizational knowledge. This is a method that more chief data officers/chief data and AI officers (CDOs/CDAOs) are employing, as their roles expand and reliance on structured data alone to gather insights and solve problems is no longer feasible.

As a whole, this process will drive future knowledge asset collection, creation, and procurement efforts and consequently is a crucial step in ensuring data at large is AI ready. If no such data exists for AI to rely on for certain use cases, users will be presented unreliable, hallucination-based answers, or in a best-case scenario, no answer at all. Yet as part of a solid governance plan as mentioned earlier, the continuation of the gap analysis process post-solution deployment can empower organizations to continually identify—and close—knowledge gaps, continuously improving data AI readiness and AI solution maturity.

4) Add Structure and Context (Semantic Components)

A key component of making data AI-ready is structure—not within the data per se (e.g., JSON, SQL, Excel), but the structure relating the data to use cases. As a term, ‘structure’ added meaning to knowledge assets in our previous blog, but can introduce confusion as a misnomer in this section. Consequently, ‘structure’ will refer to the added, machine-readable context a semantic model adds to data assets, rather than the format of the data assets themselves, as data loses meaning once taken out of the structure or format it is stored in (e.g., as takes place when retrieved by AI).

Although we touched on one type of semantic model in the previous step, there are three semantic models that work together to ensure data AI readiness: business glossaries, taxonomies, and ontologies. Adding semantics to data for the purpose of getting it ready for AI allows an organization to help users understand the meaning of the data they’re working with. Together, taxonomies, ontologies, and business glossaries imbue data with the context needed for an AI model to fully grasp the data’s meaning and make optimal use of it to answer user queries. 

Let’s dive into the business glossary first. Business glossaries define business context-specific terms that are often found in datasets in a plaintext, easy-to-understand manner. For AI models which are often trained generally, these glossary terms can further assist in the selection of the correct data needed to answer a user query. 

Taxonomies group knowledge assets into broader and narrower categories, providing a level of hierarchical organization not available with traditional business glossaries. This can help data AI readiness in manifold ways. By standardizing terminology (e.g., referring to “automobile,” “car,” and “vehicle” all as “Vehicles” instead of separately), data from multiple sources can be integrated more seamlessly, disambiguated, and deduplicated for clearer understanding. 

Finally, ontologies provide the true foundation for linking related datasets to one another and allow for the definition of custom relationships between knowledge assets. When combining ontology with AI, organizations can perform inferences as a way to capture explicit data about what’s only implied by individual datasets. This shows the power of semantics at work, and demonstrates that good, AI-ready data enriched with metadata can provide insights at the same level and accuracy as a human. 

Organizations who have not pursued developing semantics for knowledge assets before can leverage traditional semantic capture methods, such as business glossaries. As organizations mature in their curation of knowledge assets, they are able to leverage the definitions developed as part of these glossaries and dictionaries, and begin to structure that information using more advanced modeling techniques, like taxonomy and ontology development. When applied to data, these semantic models make data more understandable, both to end users and AI systems. 

5) Semantic Model Application (Labeling and Tagging) 

The data management community has more recently been focused on the value of metadata and metadata-first architecture, and is scrambling to catch up to the maturity displayed in the fields of content and knowledge management. Through replicating methods found in content management systems and knowledge management platforms, data management professionals are duplicating past efforts. Currently, the data catalog is the primary platform where metadata is being applied and stored for data assets. 

To aggregate metadata for your organization’s AI readiness efforts, it’s crucial to look to data stewards as the owners of, and primary contributors to, this effort. Through the process of labeling data by populating fields such as asset descriptions, owner, assumptions made upon collection, and purposes, data stewards help to drive their data towards AI readiness while making tacit knowledge explicit and available to all. Additionally, metadata application against a semantic model (especially taxonomies and ontologies) contextualizes assets in business context and connects related assets to one another, further enriching AI-generated responses to user prompts. While there are methods to apply metadata to assets without the need for as much manual effort (such as auto-classification, which excels for content-based knowledge assets), structured data usually dictates the need for human subject matter experts to ensure accurate classification. 

With data catalogs and recent investments in metadata repositories, however, we’ve noticed a trend that we expect will continue to grow and spread across organizations in the near future. Data system owners are more and more keen to manage metadata and catalog their assets within the same systems that data is stored/used, adopting features that were previously exclusive to a data catalog. Major software providers are strategically acquiring or building semantic capabilities for this purpose. This has been underscored by the recent acquisition of multiple data management platforms by the creators of larger, flagship software products. With the features of the data catalog being adapted from a full, standalone application that stores and presents metadata to a component of a larger application that focuses as a metadata store, the metadata repository is beginning to take hold as the predominant metadata management platform.

6) Address Access and Security (Unified Entitlements)

Applying semantic metadata as described above helps to make data findable across an organization and contextualized with relevant datasets—but this needs to be balanced alongside security and entitlements considerations. Without regard to data security and privacy, AI systems risk bringing in data they shouldn’t have access to because access entitlements are mislabeled or missing, leading to leaks in sensitive information.

A common example of when this can occur is with user re-identification. Data points that independently seem innocuous, when combined by an AI system, can leak information about customers or users of an organization. With as few as just 15 data points, information that was originally collected anonymously can be combined to identify an individual. Data elements like ZIP code or date of birth would not be damaging on their own, but when combined, can expose information about a user that should have been kept private. These concerns become especially critical in industries with small population sizes for their datasets, such as rare disease treatment in the healthcare industry.

EK’s unified entitlements work is focused on ensuring the right people and systems view the correct knowledge assets at the right time. This is accomplished through a holistic architectural approach with six key components. Components like a policy engine capture can enforce whether access to data should be given, while components like a query federation layer ensure that only data that is allowed to be retrieved is brought back from the appropriate sources. 

The components of unified entitlements can be combined with other technologies like dark data detection, where a program scrapes an organization’s data landscape for any unlabeled information that is potentially sensitive, so that both human users and AI solutions cannot access data that could result in compliance violations or reputational damage. 

As a whole, data that exposes sensitive information to the wrong set of eyes is not AI-ready. Unified entitlements can form the layer of protection that ensures data AI readiness across the organization.

7) Maintain Quality While Iteratively Improving (Governance)

Governance serves a vital purpose in ensuring data assets become, and remain, AI-ready. With the introduction of AI to the enterprise, we are now seeing governance manifest itself beyond the data landscape alone. As AI governance begins to mature as a field of its own, it is taking on its own set of key roles and competencies and separating itself from data governance. 

While AI governance is meant to guide innovation and future iterations while ensuring compliance with both internal and external standards, data governance personnel are taking on the new responsibility of ensuring data is AI-ready based on requirements set by AI governance teams. Barring the existence of AI governance personnel, data governance teams are meant to serve as a bridge in the interim. As such, your data governance staff should define a common model of AI-ready data assets and related standards (such as structure, recency, reliability, and context) for future reference. 

Both data and AI governance personnel hold the responsibility of future-proofing enterprise AI solutions, in order to ensure they continue to align to the above steps and meet requirements. Specific to data governance, organizations should ask themselves, “How do you update your data governance plan to ensure all the steps are applicable in perpetuity?” In parallel, AI governance should revolve around filling gaps in their solution’s capabilities. Once the AI solutions launch to a production environment and user base, more gaps in the solution’s realm of expertise and capabilities will become apparent. As such, AI governance professionals need to stand up processes to use these gaps to continue identifying new needs for knowledge assets, data or otherwise, in perpetuity.

Conclusion

As we have explored throughout this blog, data is an extremely varied and unique form of knowledge asset with a new and disparate set of considerations to take into account when standing up an AI solution. However, following the steps listed above as part of an iterative process for implementation of data assets within said solution will ensure data is AI-ready and an invaluable part of an AI-powered organization.

If you’re seeking help to ensure your data is AI-ready, contact us at info@enterprise-knowledge.com.

The post How to Ensure Your Data is AI Ready appeared first on Enterprise Knowledge.

Sara Nash, Principal Consultant at Enterprise Knowledge, will be moderating the keynote session titled “Data Architecture for AI” at Data Architecture Online’s annual event on Wednesday, July 23rd at 11:30am EST.

Through this session, attendees will gain valuable insights into best practices, common pitfalls, and forward-looking strategies to align their data architecture with the accelerating pace of AI. The panelists will discuss topics such as:

• The shift from traditional data warehouses to real-time, scalable, and decentralized frameworks;
• The role of data governance and quality in training reliable AI models; and 
• How organizations can future-proof their infrastructure for emerging AI capabilities.

For more information on the conference, check out the schedule and registration here. 

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An entitlement management model is an important part of data governance. Unified entitlements provide a holistic definition of access rights. You can read more about the value of unified entitlements here. This model can be designed and implemented within a semantic layer, providing an organization with roles and associated permissions for different types of data users. Below is an example of an organizational entitlements model with roles, and explanations of an example role for fictional user Carol Jones.

Having a consistent and predictable approach to entitlements within a semantic layer framework makes decisions easier for human administrators within a data governance framework. It helps to alleviate questions around how to gain access to information needed for projects if it is not already available to a user, given their entitlements. Clearly defined, consistent, and transparent entitlements provide greater ease of access for users and stronger security measures for user access. The combination of reduction in risk and reduction in lost time makes entitlements an essential area of any enterprise semantic layer framework.

	Efficiency New users are able to be onboarded with the correct permissions sooner by an administrator with a clear understanding of the permissions this new user needs. As the user’s role evolves, they can submit requests for increased permissions.
	Risk Mitigation Administrators and business leads at a high level within the framework are able to see all of the users in a business area and their associated permissions within the semantic layer framework. If the needs of the user change, or as users leave the company, the administrator can quickly and easily remove permissions from the user account. This method of “pruning” permissions within an entitlements model reduces risk by mitigating the chance of users maintaining permissions to information they no longer need.
	Diagnostics In a data breach, the point of entry can be quickly identified.
	Identify Points of Contact Users who can see the governance model can quickly identify points of contact for specific business areas within an organization’s semantic layer framework. This facilitates communication and collaboration, enabling users to see points of contact to permission areas across the organization.

An entitlement management model addresses the issue of “which users can do what” with the organization’s data. This is commonly addressed by considering which users should be able to access (read), edit (write, update), or create and delete data, often abbreviated as CRUD. Another facet of the data that must be considered is the visibility users should have. If there are parts of the data that should not be seen by all users, this must be accounted for in the model. There may be different groups of users with read permissions, but not for all the same data. These permissions will be assigned via roles, granted by users with an administrative role. 

C=Create, R=Read, U=Update, D=Delete

One method to solve this problem is to develop a set of heuristics for users that the administrator can reference and revise. By having examples of the use cases that they have granted permissions for, they can reference these when deciding what permissions to grant new users within a model, or users whose data needs have evolved. It is difficult to predict all individual user needs, especially as an organization grows and as technology advances. Implementing a set of user heuristics allows administrators to be consistent in granting user permissions to semantically linked data. They are able to mitigate risk and provide appropriate access to the users within the organization. The table below shows some common heuristics, who to apply them to and a decision if the entitlements needs further review. A similar approach is the Adaptable Rule Framework (ARF).

This method serves as a precursor to documenting a formal process for entitling, which should include the steps, sequence, requirements, and timeliness in which users are entitled to access data augmented by a semantic layer. These entitlements will determine where in the semantic layer framework users can go and their ability to impact the framework through their actions. Decisions and documentation of these process elements provide thorough consistency within an organization for managing entitlements.

Enterprise Knowledge (EK) has over 20 years of experience providing strategic knowledge management services. If your organization is looking for more advice for cutting-edge solutions to data governance issues, contact us!  

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Unlike standalone LLMs, which often struggle with outdated or irrelevant information, RAG architectures ensure domain-specific knowledge transfer by providing some organizational context in which an AI model operates within the enterprise. This makes RAG a critical tool for aligning AI outputs with an organization’s unique expertise, reducing errors, and enhancing decision-making. As organizations increasingly rely on RAG for tailored AI solutions, a strong data governance framework becomes essential to ensure the quality, integrity, and relevance of the knowledge fueling these systems.

At the heart of RAG’s success lies the data driving the process. The quality, structure, and accessibility of this data directly influence the effectiveness of the RAG architecture. For RAG to deliver context-aware insights, it must rely on information that is accurate, current, well-organized, and readily retrievable. Without a robust framework to manage this data, RAG solutions risk being hampered by inconsistencies, inaccuracies, or gaps in the information pipeline. This is where RAG-specific data governance becomes indispensable. Unlike general data governance, which focuses on managing enterprise-wide data assets, RAG data governance specifically addresses the curation, structuring, and accessibility of knowledge used in retrieval and generation processes. It ensures that the data fed into RAG models remains relevant, up-to-date, and aligned with business objectives, enabling AI-driven insights that are both accurate and actionable.

A strong data governance framework is foundational to ensuring the quality, integrity, and relevance of the knowledge that fuels RAG systems. Such a framework encompasses the processes, policies, and standards necessary to manage data assets effectively throughout their lifecycle. From data ingestion and storage to processing and retrieval, governance practices ensure that the data driving RAG solutions remain trustworthy and fit for purpose.

To establish this connection, this article delves into key governance strategies tailored for two major types of RAG: general/vector-based RAG and graph-based RAG. These strategies are designed to address each approach’s unique data requirements while highlighting shared practices essential to both. The tables below illustrate the governance practices specific to each RAG type, as well as the overlapping principles that form the foundation of effective data governance across both methods.

What is Vector-Based RAG?

RAG Vector-Based AI leverages vector embeddings (embeddings are mathematical representations of text that help systems understand the semantic meaning of words, sentences, and documents) to retrieve semantically similar data from dense vector databases, such as Pinecone or Weaviate.  The approach is based on vector search, a technique that converts text into numerical representations (vectors) and then finds documents that are most similar to a user’s query. This approach is ideal for unstructured text and multimedia data, making it particularly reliant on robust data governance.

What is Graph RAG?

Graph RAG combines generative models with graph databases such as Neo4j, AWS Neptune, Graphwise, GraphDB, or Stardog, which represent relationships between data points. This approach is particularly suited for knowledge graphs and ontology-driven AI.

Key Data Governance Practices for RAG

Practices Applicable to Both Vector-Based and Graph-Based RAG

Practices Unique to Vector-Based RAG

Practices Unique to Graph-Based RAG

Refer to Top 5 Tips for Managing and Versioning an Ontology for suggestions on ontology governance. 

Refer to Taxonomy Design Best Practices for more on taxonomy governance.

Case Study: Impact of Lack of RAG Governance

• Inaccurate and Irrelevant Insights: Without proper RAG governance, AI systems may pull outdated or inconsistent information, leading to inaccurate insights and flawed decision-making that can cost organizations time and resources. 
  • “Garbage In, Garbage Out: How Poor Data Governance Poisons AI”
    This article discusses how inadequate data governance can lead to unreliable AI outcomes, emphasizing the importance of proper data management.
    labs.sogeti.com
  • “AI’s Achilles’ Heel: The Consequence of Bad Data”
    This article highlights the critical role of data quality in AI performance and the risks associated with poor data governance.
    versium.com
  • “Understanding the Impact of Lack of Data Governance”
    This resource outlines the risks and consequences of poor data governance, providing insights into how it can affect business operations.
    actian.com
• Difficulty in Scaling AI Systems: A lack of structured governance limits the scalability of RAG solutions. As the volume of data grows, it becomes harder to ensure that the right information is retrieved and used, resulting in inefficient AI models.
• Data Silos and Inaccessibility: Without proper metadata management and access control, important knowledge may remain isolated or inaccessible, reducing the effectiveness of AI in providing actionable insights across departments.
• Compliance and Security Risks: The absence of governance may lead to failures in data sovereignty and privacy requirements, exposing the organization to compliance risks, potential breaches, and reputational damage.
• Loss of Stakeholder Confidence: As RAG outputs become unreliable and inconsistent, stakeholders may lose confidence in AI-driven decisions, affecting future investment and buy-in from key decision-makers.

Conclusion

Effective data governance is crucial for RAG, regardless of the retrieval method. RAG Vector-Based AI relies on embedding standards, efficient indexing, quality controls, and strong metadata management, while Graph RAG demands careful management of ontologies, taxonomy, and tracking data lineage. By applying tailored governance strategies for each type, organizations can maximize the value of their AI systems, ensuring accurate, secure, and compliant data retrieval.

Graph RAG AI is the future of contextual intelligence, offering unparalleled potential to unlock insights from interconnected data. By combining advanced graph technologies with industry-best data governance practices, EK helps organizations transform their data into actionable knowledge while maintaining security and scalability.

As organizations look to unlock the full potential of their data-driven solutions, robust data governance becomes key. EK delivers Graph RAG AI solutions that reflect domain-specific needs, with governance frameworks that ensure data integrity, security, and compliance. Please check out our case studies for more details on how we have helped organizations in similar domains. EK also optimizes graph performance for scalable AI-driven insights. If your organization is ready to elevate its RAG initiatives with effective data governance, contact us today to explore how we can help you transform your data into actionable knowledge while maintaining security and scalability.

Is your organization ready to elevate its RAG initiatives with robust data governance? Contact us to unlock the full potential of your data-driven solutions.

The post Data Governance for Retrieval-Augmented Generation (RAG) 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.

The post Data Product Accelerator appeared first on Enterprise Knowledge.

Thomas Mitrevski, Principal Consultant for Data Management at Enterprise Knowledge and Laurent Dresse, Chief Evangelist for DataGalaxy, will present a joint webinar on the topic of implementing a data catalog and garnering adoption across your organization.

Within the webinar, Mitrevski and Dresse will cover how to:

• Deploy data catalogs for maximum impact;
• Overcome adoption challenges and boost user engagement; and
• Drive global data governance and foster a data-driven culture.

This interactive one-hour session will include real-world examples from Enterprise Knowledge demonstrating how to evaluate your current catalog maturity, develop actionable use cases, and identify where crucial information resides within your organization to best support your catalog use cases. 

The webinar will take place Thursday, October 31, at 11:00 a.m. EDT. The event is free, but registration is required.

Register for the webinar here!

The post EK / DataGalaxy Joint Webinar appeared first on Enterprise Knowledge.

In this engaging session, Nozari and Majumder explored the challenges and opportunities presented by the rapid evolution of Large Language Models (LLMs) and the exponential growth of unstructured data within enterprises. They also addressed the critical intersection of technology and data governance necessary for managing AI responsibly in an era dominated by data breaches and privacy concerns. 

Check out the presentation below to learn more about: 

• A comprehensive framework to define and identify dark data
• Innovative AI solutions to secure data effectively
• Actionable insights to help organizations enhance data privacy and achieve regulatory compliance within the AI-driven data ecosystem

The post Mastering the Dark Data Challenge: Harnessing AI for Enhanced Data Governance and Quality appeared first on Enterprise Knowledge.