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.

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In this presentation, Mitrevski and Tesfaye detailed their experiences developing strategies for multiple enterprise-scale data initiatives and provided an understanding of common data governance and maturity needs. Mitrevski and Tesfaye based their talk on real-world examples and case studies and provided the audience with examples of achieving buy-in to invest in governance tools and processes, as well as the expected return on investment (ROI).

Check out the presentation below to learn: 

• How Leading Organizations are Benchmarking Their Data Governance Maturity
• Why End-User Training was Imperative in Seeing Scaled Governance Program Adoption
• Which Tools and Frameworks were Critical in Getting Started with Data Governance
• How Organizations Achieved Success with Data Governance in Under 12 Weeks
• What Successful Data Governance Implementation Roadmaps Really Look Like

The post Case Studies: Applications of Data Governance in the Enterprise appeared first on Enterprise Knowledge.

If you are exploring ways your organization can benefit from implementing a data governance program, we can help! EK has deep experience in designing and implementing solutions that optimize the way you use your knowledge, data, and information, and we can produce actionable and personalized recommendations for you. Please contact us for more information.

Special thank you to Nina Spoelker for her contributions to this infographic! 

The post 4 Critical Elements of a Successful Data Governance Program appeared first on Enterprise Knowledge.

By appointing a product manager to lead these efforts, organizations can avoid the common pitfalls associated with the application of artificial intelligence to enterprise data. A product manager can ensure that AI applications are solving true business needs, and that they are engineered in a way that is usable across the organization, even by those who are not necessarily familiar with the underlying technology. This is especially important for AI projects due to the technical complexity of the implementations and the amount of requirement translation that needs to happen between business stakeholders and technical developers. Including a product manager will ensure that your organization realizes value quickly, and pursues MVP implementations such as basic regressions and classification algorithms to ensure your investment in both people and technology is appropriated correctly.

What Does a Product Manager Do? 

The product manager is there to ensure that the user is represented both in the design of the overall experience and the performance of results, yet they also represent key stakeholders and ensure the solution fits the market’s needs and delivers on revenue goals. While project managers ensure solutions are delivered on-time according to specifications, the product manager will drive the solution to fit an ever-evolving market, apply new methods and technologies when appropriate, and optimize the value the business actually receives from the adoption of AI technology. In my time as a product manager, I’ve followed the above values to create solutions that optimize square footages for real estate, structure search content for research purposes, and leverage large datasets to determine optimal ESG recommendations.

Advantages of Having a Product Manager 

Throughout this article, I will use my experience as a product manager within the enterprise data space to focus on the specific advantages a product manager gives to the application of AI technologies. I hope this experience gives you the ability to think about your enterprise AI with a more product-centric focus, and clearly understand what is needed to have a truly successful AI transformation within your organization.

Identifying Appropriate Problems 

The first stumbling block most organizations run into when implementing AI at an enterprise scale is making sure they have the correct problem identified, which is a matter that can be solved in an appropriate amount of time. AI is complex, and isn’t something that can necessarily be achieved in a short period of time. However, a product manager can alleviate this problem and lead the process to define a minimum viable product (MVP). This MVP will only contain what is immediately valuable to your organization, and allow you to iterate quickly in order to gather feedback and tailor your solution to your user’s needs. Not only does this help keep initial costs down and cost overruns from occurring, it allows your organization to remain agile and pivot to other solutions and technologies if necessary. A good product manager will also help host demonstrations of development progress and create a clear roadmap for the future. This way, your organization can clearly communicate to stakeholders what features will be included in the future and how the technology will evolve over time.

The next step a product manager can take in helping your organization identify an appropriate problem is to lead the creation of a prototype that can be placed in front of end users. This prototype doesn’t necessarily need to be a full artificial intelligence solution, it could be something as simple as a linear regression. The main point of this prototyping effort is to ensure that what is being built, and the data that is being leveraged, is targeting the correct group of end-users. It is solving a clear issue for them in a simpler way than they have available today. This process will also allow your organization and the development team to identify if they have the appropriate data necessary to solve the problem and if it’s structured to be used in an AI application. This helps in projecting future costs, both in the time it will take to structure data for a full AI implementation and if there are any additional data sets that need to be purchased. If your organization is failing to succeed at the prototyping stage, or you’re projecting high costs for work to continue, this will give you the fastest and a less expensive way to learn early and adjust the product roadmap.

Correctly Applying Innovation to What’s Valuable

One of the biggest values a product manager can bring to an enterprise AI effort is to have a firm understanding of the organization’s data and to elevate conversations with different business groups that clearly demonstrate the organization’s problems. Without these understandings, an organization could put months or even years of development and investment into data exploration, taking up an unknown challenge. It’s not only important for an organization to be able to understand its problems, it’s absolutely critical for them to be able to prioritize them as well. Executives can often hear multiple different problems from different business groups, and have difficulty prioritizing which problems to solve. The product manager is the key member of your organization that will be able to help you identify which solutions will alleviate the most headaches, and generate additional value for your business, ensuring that your investments are being spent in the correct places.

In order for a product manager to make the correct decisions and prioritize appropriately, they need to be able to identify and communicate the organization’s strategic objectives, as well as establish metrics and KPIs that will be used to measure performance. These are imperative to establish ahead of time as it will allow the organization to filter a set of valuable ideas down to the ones that most align with existing objectives, and the ones most likely to meet performance goals. Another key method a product manager will use to prioritize valuable problems is to identify groups that are excited to pursue AI technologies and have data available to use. By filtering problems down to those that help your organization meet strategic goals, and allow collaboration with groups that are inherently excited to use novel technologies, the product manager enables your organization to shift priorities and pursue those challenges that are truly valuable, and make sure that innovative technologies are applied appropriately. This filtering strategy can be shown in a simple funnel as shown below.

Designing a Solution That is Familiar to Your End Users

In your organization’s pursuit of enterprise AI, the product manager will be the main individual who directs how the product grows and how it gets exposed to users. It’s extremely important to design a solution and user experience that will resonate with end users before development begins, to ensure long-term success and the adoption of the solution. Although AI may power the solution, end users who are not familiar with the technology may not care. At the end of the day, they want to see a solution that presents answers to their problems in a clear and concise way, no matter how the answers get created. Product managers can deliver this by leveraging teams of designers and front-end developers solely focused on providing the most appropriate end user experience. Their cross-functional knowledge on how to lead design sessions, define use cases, and create wireframes will prove invaluable to ensuring that the end product delivered to users is familiar and usable.

A product manager will also bring an inherent knowledge of tools that can be used to facilitate conversations and ensure everyone is in agreement on the end result. On one of my more recent projects, we were able to utilize Adobe XD to walk users through different wireframes and mockups, allowing them to see deliverables before they were produced and provide feedback early on in the process. Additionally, we used an online whiteboard application to present roadmaps and guide end-users to document the problems they were facing and the information they needed to solve them. By allowing your product manager to utilize these facilitation tools, you ensure every member of your organization is on the same page, and can address issues in communication of ideas early on in the process. This guarantees that as experimentation and development of your enterprise AI solution continues, the predictions or classifications that are being made are presented to your users in a way that is easily consumable and can be leveraged to generate additional revenue for your organization.

Handling Uncertainty and Communicating Product Value

Enterprises will often face problems not seen in smaller start-up companies when pursuing the creation of AI products. Having a product manager will make addressing all of the common enterprise sources of uncertainty easier, as your organization will have a key point of contact that is knowledgeable and responsible across the AI solution. That means that your product manager will be a go-to resource for solving problems with the size and complexity of data, as well as dealing with outdated or non-performant infrastructure. A product manager will be key in breaking down walls between departments to ensure alignment between business and IT stakeholders. Eventually, their efforts will lead to a more cohesive data strategy, with a focus on how AI will produce real change at your organization. As the product continues to evolve and grow, they’ll help mitigate future uncertainty by being an advocate for an agile software development lifecycle. In the long-term your organization’s engineering managers will continue to be focused on performance, and project managers will continue to focus on delivery, where in contrast the product manager will be focused on how your organization’s adoption of AI can continue to grow and the existing solution can be applied to new and varying use cases.

Once your enterprise AI solution has moved past the initial stages into development or production use, you will need a member of your team to communicate the solution across the organization. For example, the manager of a recommender system tracking possible changes to a company’s ESG policies will have all of the necessary information to make sure that accurate information about how the new AI system will influence upcoming policy decisions. They will make sure this information is conveyed throughout the company, as well as the true business relevancy of the AI product. They will help ensure that both your sales and marketing teams have a deep understanding of the power of the AI solution that has been created, so that your business can deliver clear outcomes to a variety of different users. They’ll also work toward creating rich documentation and training materials around the solution, so that your new users are enabled to get the most out of the AI application that has been developed. A good idea of where your AI product manager should fit amongst your different teams is shown below.

Conclusion

New AI technologies and their applications to business problems continue to evolve, especially as more and more large enterprises begin AI adoption. The challenges normally faced in scaling an AI solution to an enterprise level can be mitigated or even fully addressed by staffing experienced product managers as champions for these applications and the larger vision. They will help ensure the solution solves the right problems for the business, that the product grows and is adopted successfully, and that end-users are enabled with the knowledge to utilize and receive the maximum amount of value from the product. Ultimately, this will lead to the successful adoption of AI across many different business units and use cases. If enterprise AI is built with a product manager leading the way, it can truly deliver on helping your organization achieve its strategic objectives. Here are specific examples and case studies on how we have been approaching this:

• How to Take an AI Project Beyond a Prototype
• Building a Semantic Enterprise Architecture
• Enterprise Artificial Intelligence

Could your organization use a product manager to help guide the way for your enterprise AI transformation? Contact us today!

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