Data products aren’t mandatory for building BI dashboards, ML models or AI solutions, but they dramatically improve your odds of delivering successful, repeatable outcomes by adding semantic clarity and governance.

One Unified Intelligence Platform 

In data and AI architecture, data products (trusted, reusable data assets with clear meaning, ownership, governance, and a defined way to be consumed) are the glue that makes the various architecture components operate as one unified intelligence platform.

Without data products, each tool in the architecture operates using its own interpretation of the data. With them, analytics, ML and AI share a consistent semantic foundation regardless of vendor stack. Consider how this plays out across two different ecosystems: An SAP-centric architecture and an AWS-native architecture.

SAP-Centric Architecture: SAP S/4HANA, Datasphere, Joule, and Databricks

• SAP S/4HANA generates operational data
• Datasphere models and governs it
• Joule and AI services consume it
• Databricks or similar platforms extend advanced analytics

All rely on shared, governed data products to maintain consistent business meaning.

AWS-Native Architecture: AWS S3, RedShift or Athena, SageMaker, and QuickSight

• Raw data lands in AWS S3
• The data is transformed through Redshift or Athena
• Then feeds both SageMaker models and QuickSight dashboards

Using data products ensures consistent definitions, governance, and reusable interfaces across analytics and AI workflows in this architecture.

Strong Predictors of AI Success

Organizations experimenting with AI can produce early results without structured data products. But when AI initiatives are measured by scalability, reliability, and enterprise adoption, a consistent pattern emerges: High-performing organizations treat governed data products as a foundational layer rather than an afterthought.

Data products act as strong predictors of success because they:

• Address a primary root cause of AI failure: Inconsistent semantics and unreliable data foundations.
• Emerge independently across high-maturity AI organizations, even when different technology stacks are used.
• Enable repeatability and governance, allowing models and analytics to move from experimentation to production.
• Support cross-domain AI, where insights and models span multiple business functions.
• Align with modern enterprise architectures, including SAP’s evolving data and AI strategy.
• Correlate with stronger business outcomes: Organizations adopting governed data layers consistently outperform those that rely on ad-hoc pipelines.

BI dashboards, ML models and AI solutions can be built without formal data products, but why would you want to? Organizations seeking scalable, reliable, enterprise-grade outcomes consistently find that data products are indispensable to achieving a successful outcome. 

Organizational alignment matters as much as the technology

A second critical predictor of success is proactive engagement from the C-suite. Data has long driven strategic advantage in data-intensive industries such as Finance, Media, and Retail. But today data’s importance extends across every sector. 

Executive sponsorship ensures that data products are treated as business assets, not just technical artifacts.

Technical and operational readiness must progress together

Adopting data products requires both of the following:

• Technical enablement: Platforms, architecture, and tooling
• Operational capability: Ownership models, governance processes, and data modeling skills

These dimensions are interdependent. Any delay in adopting either one can slow time-to-value. We recommend beginning with focused technical pilots that demonstrate clear business outcomes through small, easily understood implementations. 

Early wins help build momentum, validate governance approaches, and create the organizational alignment needed to scale.

Understanding the Technical Platform 

Let’s return to our SAP-centric and AWS-native examples introduced earlier for this discussion.

Spaces 

In SAP Datasphere architecture, Spaces are the primary organizational construct used to structure and govern data products. Multiple Spaces enable both long-term data domain ownership and cross-domain collaboration, as well as temporary collaboration environments. 

Spaces are the most crucial construct for data products. Spaces provide for both long-term Data Domain and Cross Domain creation as well as shorter-term collaboration spaces.

Create Spaces for different data domains like Customer Data, Product Data, Sales Data, Financial Planning & Analysis (FP&A), Social Data, Streaming Data, Financial Data, HR Data, and Manufacturing Data. 

Enable data sharing and collaboration among these Spaces to encourage reuse (e.g., for an R&D project), while ensuring sensitive data is protected using methods like data masking and authorization. The PERMISSION Space authorization table, managed by designated security and administrative users, controls access rights for sharing data across these Spaces.

Architecture

The overall architecture to assemble and consume data products can be defined wholly within SAP Business Data Cloud. Alternatively, with a little (not a lot) more work, the architecture can be built with SAP Datasphere and Databricks tools – or with AWS cloud tools like S3, Redshift, Athena, and Quick Suite. 

The architecture is typically organized into layered components:

• Inbound Layer: Capture or federate raw data from source systems and external platforms.
• Harmonization Layer: Standardize, transform, and clean data to ensure consistent structure and meaning across domains.
• Propagation Layer: Create unified consumption entities – such as analytic data products, semantic models, and reporting views – that can be reused across BI, ML, and AI scenarios.
• Reporting Layer: Optimize views specifically for reporting and analytics to support consistent branding, presentation, and user experience.

Governance

Effective data products require defined governance practices to ensure trust, consistency, and usability across domains. Establish clear ownership, naming conventions, and data lineage so users can understand and rely on the data they consume. Adopt a data catalog to manage data products and associated assets.

Roadmap

Adoption should follow a structured, value-driven roadmap aligned to business priorities and execution readiness. Create the roadmap based on business value, organizational priorities, and the ability to execute, typically a “crawl, walk, run” maturity approach.

Define initiatives by domain and cross-domain opportunities tied to clear business outcomes and supporting business cases. Select two to three visible data product opportunities that are achievable – not overly complex, but meaningful enough to demonstrate delivery and value.

Types of Data Products

Organizations typically work with two primary categories of data products: 

• Certified data products provided or governed centrally.
• Custom data products built internally or through partners. 

The following sections describe how these approaches apply within SAP Business Data Cloud (BDC) and broader data architectures.

Certified Data Products

Certified data products are governed, production-ready assets that follow standardized definitions, quality controls, and ownership models.

Data products arrive in SAP Business Data Cloud (BDC) in a basic form containing only the essential data for a business entity. Within SAP BDC, basic data products can be combined with other basic data products to form derived data products. These derived data products provide broader business context and are typically more useful for analytics and AI consumption.

Note: BDC is not mandatory to build your own or adopt preconfigured data products from SAP partners.

The following figure shows an SAP Business Data Cloud example of how source-level data products evolve into derived, consumer-oriented data products and higher-level business insights.

Source: SAP. Introducing Business Data Cloud. Focusing on Data Products and Intelligent Applications

Build Your Own 

In addition to centrally certified data products, organizations may build their own commercial-grade or self-service data products tailored to specific business needs. These internally developed data products can still follow certified standards for governance, UX, and lifecycle management to ensure consistency and reuse across BI, ML, and AI initiatives.

For certified dashboards and commercial-grade data products, we recommend the following delivery lifecycle:

Self service is a development that now brings organizations foundational value from data products. Using existing BI dashboards and Spaces as a starting poinit, self-service users can now rapidly bring new BI dashboards and Spaces into use with organized, certified data already available.

Data Product Marketplaces

Data product marketplaces provide curated assets that accelerate adoption by offering preconfigured datasets, models, and analytics aligned to specific business domains.

SAP:  Available within SAP Business Data Cloud (BDC) via the SAP Business Accelerator Hub. These offerings include curated datasets, integration components, and analytical applications designed to support data-driven decision-making.

See: Data Product | Data Products | SAP Business Accelerator Hub

Apiphani:  Available with or without SAP BDC. Organizations can select from an Apiphani catalog of preconfigured agents and KPIs spanning energy and manufacturing domains such as Finance, Engineering, Supply & Demand, Sales, and HR.

Implementation and Operational Considerations

Moving from data product concepts to real-world adoption requires a combination of governance practices, technical design decisions, and operating model alignment. The following considerations focus on how organizations evaluate potential data products, enable controlled self-service, and make architecture choices that balance agility with consistency.

These practices are not tied to a single platform; they apply across SAP Business Data Cloud, AWS-native environments, and hybrid architectures. Establishing clear evaluation criteria, access models, and data integration patterns helps ensure that data products remain scalable, governed, and reusable as adoption grows.

Data Products Evaluation Template

Use a consistent framework to evaluate and prioritize candidate data products:

• Opportunity / Purpose 
• Business Priorities (Specific ROI or enabling priorities and strategies)
• Core BI and AI Value (qualitative, quantitative, and strategic impact)
• Technology and Data Availability
• Deployable / Time to Value

Self-Service Data Access

Implement self-service capabilities that allow business users to explore and model data independently while relying on governed data products as a foundation. This reduces reliance on centralized IT and increases agility without compromising consistency.

User Groups and Permissions

Define user groups and reusable roles to enforce appropriate access and authorization. Clearly structured roles help manage who can view, modify, or share data products across domains.

Remote Tables vs. Data Replication

Determine whether to use remote tables for real-time access without duplication, or replicated data for improved performance. Remote tables support immediate updates, while replication is better suited for performance-critical analytics. 

CDS Views

When creating remote tables, we prefer using Core Data Services (CDS) views over direct S/4 tables to enhance performance and maintainability. 

Operating Model

Successfully adopting data products requires more than architecture and tooling. It requires an operating model that aligns business leadership, governance structures, and technical delivery. Organizations that scale BI, ML, and AI initiatives treat data products as long-lived assets supported by clear ownership, domain leadership, and enterprise coordination. 

The following roles and practices outline how operating models evolve to sustain governed, reusable data products across SAP Business Data Cloud, AWS-native, and hybrid environments.

Domain Leadership

Data domain strategy should align directly with business priorities and execution. Business leaders manage domains of defined size and scope, ensuring accountability for outcomes and data quality. While data products may integrate multiple domains, each data product should have a primary domain responsible for definition and implementation.

Data Product Owners guide success through key lifecycle phases — Concept, Business Planning, Development, Launch, and Support — shifting organizations from traditional project delivery toward a product-based operating model.

Data Product Ownership

A Data Product Owner is a business-savvy, technically aware steward responsible for ensuring that each data product remains accurate, governed, discoverable, and valuable for analytics and AI use cases.

This role operates at the intersection of business, data engineering, and data science and is one of the most important roles in a modern SAP data architecture. Key responsibilities include:

• Promoting and communicating data product value
• Representing consumer needs and adoption priorities
• Owning business meaning, definitions, and semantic consistency
• Ensuring data quality and trust
• Coordinating with other Data Product Owners across domains
  

Center of Excellence

The Center of Excellence (CoE) provides enterprise-wide leadership across discovery, governance, innovation, and community engagement. The CoE partners with domain leaders and Data Product Owners to catalog and manage data assets, collaborates with IT infrastructure teams on permissions and standards, and maintains a shared forum for tools, patterns, and emerging use cases.

Data Catalog

IT and apiphani teams jointly maintain secure infrastructure operations, managing system requests, incidents, and ongoing platform optimization. A centralized data catalog supports discoverability, governance, and lifecycle management of data products.

Building effective data pipelines requires specialized expertise across architecture, engineering, DevOps, and consumption design. Successful implementation depends on strong integration practices, security alignment, and continuous performance monitoring across enterprise environments.

C-Suite Role

Executive sponsorship is essential to drive organizational alignment around data, analytics, and AI. The C-suite plays a critical role in shifting mindset and prioritizing data products as strategic assets.

Engage executive leadership early to establish visibility and alignment, and deepen involvement once initial pilot data products demonstrate measurable value.

Culture and Mindset Changes

Together with evolving ways of working across Domain Leaders, Data Product Owners, and the CoE, the C-Suite enables the shift toward a data-driven culture, with the following focus areas guiding the transition to a steady-state operating model..

1. Executive teams recognize and expect data products as key drivers of business performance, consistently delivering above-benchmark results and exceptional outcomes in strategic initiatives.
2. Market leaders leverage embedded data products throughout their products, customer interactions, and operations. These organizations consistently generate and implement new ideas to enhance existing data products and develop new ones, driving continuous innovation.
3. Data Pipeline Acceleration begins to show how reusable solution components and reliable data transformations and data views turn into system and user consumption at increasing speed to value, i.e., the AWS Data Flywheel.
4. Data Self Service enables comprehensive data access across the enterprise. The platform provides streamlined data discovery, enterprise-grade analytics, and automated business insights at scale powered by tools such as SAP Just Ask.

Conclusion

Data products provide the structure that allows BI, ML, and AI initiatives to move beyond experimentation into scalable, governed business capabilities. Whether implemented within SAP Business Data Cloud, AWS-native architectures, or hybrid environments, success depends on more than technology alone. It requires clear ownership, strong governance, and an operating model aligned to business outcomes. 

Organizations that treat data as a product, supported by domain leadership and executive sponsorship, create a foundation for repeatable innovation, faster time to value, and sustained competitive advantage.

About the Authors

James Kendrick

Principal Director of Data and Analytics Products at apiphani.

Mario de Felipe

Global Director of SAP Technology and Innovation at apiphani.

As with prior technology waves, the current AI surge is marked by rapid adoption, inflated expectations, and uneven results. AI has become ubiquitous across enterprise strategy discussions, which often outpace the organizational foundations required to support it.

We are witnessing efforts to achieve incredible outcomes in process automation and the leveraging of machine-level intelligence to produce great decision-making capabilities in a deployable operational platform. These efforts, however, are generating a depressing statistic:

95% of Enterprise AI Projects Fail.*

That’s correct. 95 out of 100 AI projects fail to meet their success criteria, which begs the question, why? What is the Achilles’ heel of most AI strategies? What is preventing their success as well as an attractive return on investment?

The answer, though not trivial, is straightforward.The Achilles’ heel of AI strategy is a persistent lack of data quality and the absence of effective data governance. Without integrity in the data foundation — and clear accountability for how data is created, managed, and used — even the most sophisticated AI strategies collapse under their own weight.

A series of high-profile AI failures illustrates this reality:

• Google’s AI search tool tells users to “eat rocks” for your health
• Zillow’s home-buying debacle shows how hard it is to use AI to value real estate
• Taco Bell rethinks AI drive-through after man orders 18,000 waters

These stories illustrate that AI fails not because it thinks poorly, but because it learns poorly from data that lacks governance. Let’s take a deeper dive into both subjects.

 * Toscano, Joe, “Why 95% Of AI Projects Fail — And 4 Ways To Be In The 5% That Succeed”, Forbes, Sept 2025, Forbes

The Quality of Data

Data is everything to AI. AI requires enormous amounts of data inputs and sources to feed its voracious, machine-driven appetite and to refine and improve its logical models and neural networks. AI does not fix bad data; it amplifies it. If training data is incomplete, biased, or out-of-date, AI models produce distorted predictions that erode trust and create compliance risks.

This is compounded by another problem we’ve observed across the organizations we support. AI projects are often driven by technology aspirations rather than enterprise data realities. The result: Proof-of-concept models that never scale, analytics that contradict themselves, and insights no one fully trusts.

What are the root-causes of data quality failures?

• Fragmented Data Ecosystems: Data is scattered across ERP, CRM, and MES (for example), as well as unstructured sources with little synchronization. To achieve real-time decision-driving capabilities, all required data must be available and presented in real-time. This is a status that few organizations have achieved.
  • Example: Customer churn models trained on CRM data without capturing support tickets or billing records. 
  • Impact: AI underestimates risk or misclassifies outcomes due to incomplete learning context.
• Poor Data Quality and Data Origination: Inconsistent master data, missing lineage, and unreliable inputs feeding critical algorithms.
  • Example: Manufacturing AI reading incorrect temperature values due to uncalibrated IoT sensors. 
  • Impact: Predictive maintenance or quality control models generate false alerts or fail to detect anomalies.
• Duplicate or Redundant Data: This is one of the most prevalent and most difficult conditions for automated remedies: the issue of repeated records inflating the apparent frequency or weight of certain features.
  • Example: One of the most important discoveries we made for the manufacturing division of a pharmaceutical company was navigating the “splits and collisions” of fragmented patient data. 
  • Impact: Multiple instances of the same patient data records were completed by skewing the results of AI algorithms for tracking insurance remediation.
• Lack of Data Lineage and Traceability: This category involves the inability to track data origin, transformations, and ownership of data inputs. These failures often stem from poor data quality, amplification of bias, regulatory violations, and models that cannot generalize. This is because the origin, transformations, and quality of the data were not tracked.
  • Example: Unity Technologies’ $110 million ad-targeting error. The core issue stemmed from Unity’s ad targeting system, which utilized data from various sources to personalize ad delivery. A lack of clear data lineage meant that the origin and transformations of the data used to train and operate the ad-targeting AI were not fully understood or documented. 
  • Impact: This failure demonstrates how poor data management, including a lack of lineage, can lead to incorrect AI model outputs, resulting in a significant economic loss.

There are many more categories for examining the examples and impacts of poor data quality. The remedy for these problems is the focus of the second half of this examination: Strong corporate data and governance structures will largely eliminate the data problems that cause the high rate of failure for AI initiatives.

Corporate Governance for AI Strategy

Governance is often misunderstood as a bureaucratic layer that is similar to the deployment of other system guardrails, like password management and trouble tickets. Governance is the operating system of a well-functioning, data-driven enterprise and is a critical factor in using AI effectively and responsibly across the organization.

One of the earliest indicators of ineffective AI governance mirrors a challenge many organizations faced 15 years ago with the emergence of “shadow IT.” This happened as SaaS applications spread rapidly, introducing a subscription-based model that allowed individual teams to set up their own tools (e.g., separate Salesforce instances) without IT oversight. 

The result was a wild west scenario of uncontrollable data usage and the exposure of corporate intellectual property and sensitive financial data. It introduced considerable risk and left IT with limited opportunity to regain control without a fundamental shift in governance policies. The same issues are currently happening today with the proliferation of AI projects at the enterprise department level. Unclear ownership, ad-hoc data stewardship, and an absence of executive oversight are the primary contributors to ineffective AI strategy. One of the first ways to restore control is through the strict application of governance protocols designed for AI use cases and business-aligned deployments.

What are the hallmarks of an effective AI governance structure?

1. Strategic Alignment and Value Stewardship

AI governance ensures that AI investments are explicitly tied to enterprise objectives, not isolated technology initiatives. Governance bodies (typically operating at the Board and executive committee level) prioritize AI use cases based on measurable business value, risk tolerance, and strategic relevance.

This function answers the following fundamental questions:

• Why is AI being deployed?
• Where does it create competitive advantage?
• Which AI initiatives should be scaled, paused, or terminated?

Without this layer, organizations experience AI sprawl, duplicated models, and fragmented investments with unclear ROI.

2. Data Integrity and Trust Enablement

Because AI systems are only as reliable as the data they consume, governance establishes ownership, accountability, and quality standards for enterprise data assets. This includes:

• Data lineage and provenance requirements
• Authoritative data sources (“single source of truth”)
• Quality thresholds for model training and inference
• Controls over synthetic, third-party, and externally sourced data

In mature organizations, governance treats data as a regulated strategic asset, not an operational byproduct. This directly mitigates the Achilles’ heel of AI via confidently automated decisions built on untrusted data.

3. Risk, Ethics, and Regulatory Oversight

AI governance institutionalizes risk management across the AI lifecycle, including:

• Model bias and fairness
• Explainability and auditability
• Regulatory compliance (current and emerging)
• Legal, reputational, and operational exposure

Rather than relying on ad hoc ethical reviews, mature governance embeds repeatable controls that are reviewed, tested, and enforced – like financial controls or cybersecurity frameworks. This is increasingly critical as regulators and courts treat AI-driven decisions as corporate acts, not technical artifacts.

4. Operating Model and Decision Rights

Effective AI governance clearly defines who owns what decisions:

• Who approves AI use cases?
• Who certifies models for production?
• Who is accountable when AI outcomes are wrong?
• Who can override or shut down an AI system?

As AI autonomy increases, governance replaces ambiguity with formal decision rights, escalation paths, and kill-switch authority. This prevents “shadow AI” and ensures humans remain accountable for machine-driven outcomes.

5. Continuous Oversight and Adaptation

Unlike static policies, mature AI governance is dynamic and evolutionary. It continuously:

• Monitors model performance and drift
• Reassesses risk as data and business conditions change
• Incorporates new regulations and standards
• Retires models that no longer meet trust or value thresholds

This transforms governance from a gatekeeper into a living management system; one that adapts at the same pace as AI itself. Adopting a new approach to governance is the first critical step in improving your data quality as well as putting effective guard rails around your data and making your entire operative process ready for the effective use of AI technology.

Without governance, AI efforts degrade through model drift, shadow initiatives, and uncontrolled risk – eroding long-term value. Strong governance ensures higher-quality data, clear guardrails, and an operating model that enables AI to deliver reliable, sustainable outcomes.

Determining the Proper Path to a Sustainable AI Strategy

Over-reliance on platforms and tools, rather than alignment with business goals and operating models, is a fundamental flaw of AI strategy that can be rectified through adoption of best practices. Apiphani works with enterprise organizations operating complex, mission-critical systems (like SAP), where reliability, accuracy, and accountability are non-negotiable. In these environments, AI initiatives cannot be separated from the conditions in which they operate.

What we consistently observe is that the models themselves rarely drive AI failures. They occur when advanced capabilities are introduced into environments with fragmented data, unclear ownership, and insufficient operational discipline.

Addressing this challenge does not require additional tools or more sophisticated algorithms. It requires establishing the foundational conditions that allow AI to operate reliably and predictably at scale. Our apiphani AI Strategy Framework is anchored by three pillars. 

Here’s how we do it.

1. Data Integrity Foundation

• A comprehensive data quality assessment (focused on accuracy, completeness, timeliness, and lineage) is the foundation for evaluating and optimizing data architecture for performance
• The establishment of a data integrity index as a benchmark for AI readiness
• Automated validation workflows using AI-driven data profiling and anomaly detection

2. Governance by Design

We’ve designed an AI Center of Excellence (CoE) that offers a consistent, scalable model for implementing effective AI strategy. Elements include:

• A Data and AI Governance Council aligned to business domains
• Policy frameworks for model lifecycle management, ethical AI, and compliance
• Metadata management and lineage tracking to ensure transparency

3. AI Value Realization

• Integration of governance metrics into AI ROI dashboards
• Diagnostic tools to visualize data and governance health
• Continuous improvement cycles connecting governance KPIs to business outcomes

The Path Forward

Organizations that treat governance as the backbone rather than the brake of AI strategy will outperform peers who chase the latest models without considering their foundations. The future of enterprise AI belongs to companies that understand this simple truth: AI is only as intelligent as the integrity of the data and governance that supports it.

Apiphani helps organizations generate powerful AI strategies by aligning data strategy, governance, and AI implementation into a single, coherent framework that delivers measurable business value. 

The first step is our AI Readiness Assessment, which evaluates your organization across data readiness, platform and operational maturity, governance and risk controls, and the ability to safely deploy AI in mission-critical environments.

Authors:
James Kendrick, Principal Director, Professional Services, apiphani
Duane Tomlinson, Data Success Manager, Atlan

Data informs nearly all business decisions and is the main driver of company innovation. A 2025 Gartner poll found that 89% of CEO and senior business executives say effective data, analytics, and AI governance is essential for enabling business and technology innovation. Yet, many organizations have neither a data strategy nor a data governance program in place. 

Recipe for Data Success

Implementing a data strategy and governance program doesn’t happen overnight. Companies that rush into it often lack a clear vision and goals for their data. Without these, and without a plan for broadly communicating the strategic importance of your data program – and its critical success factors – you may be putting the program’s success at risk. 

In an informed organization, everyone understands why data strategy and governance are important. They also know their role in its implementation and have been educated on the end benefits. Conversely, poor communication creates confusion between the data strategy designers and those involved in its execution. This confusion wastes time, can be hard to recover from, and puts your whole data program at risk.

How to Implement a Data Strategy & Governance Program

Apiphani worked with Atlan, a leading active metadata platform and a modern data collaboration workspace, to create a combined human / technical environment for our client, a global manufacturer of innovative gas turbine components used by the clean energy industry.

Our objective was to instill data as a critical part of the client’s evolving digital culture. Together, we implemented a catalog and governance program for data product assets that were classified into 10 data domains. This gives our client the ability to sustain value at scale, as new and evolving data products are continually developed.

In this article, we share what we learned from our experience and explore a framework that includes the five steps necessary to achieve a data governance program, in motion, that fits your company’s culture.

Step 1: Set Context for Governance Over the Expansive Use of Data

The success of any initiative depends on how well it’s executed. A clear vision with well-defined goals and outcomes are a critical success factor. 

Successful organizations start by engaging a data champion who can advocate for expanding the role of data in driving successful business outcomes. Next they involve key business leaders who either have a need for specific data, or who have already requested specific data to drive better business outcomes. 

Engaging with these leaders uncovers opportunities to use data more expansively. Understanding their business challenges is also necessary. What’s preventing them from leveraging specific data for key business decisions? Difficulty extracting it from its current source? Lack of a “single source of truth”? Finding the answer begins to lay the foundation of the value of data governance. 

Once you involve the right business leaders, identify the following information:

1. Specific data opportunities, data challenges, data initiatives, or compelling events
2. Current state of data strategy and progress in advancing data usage
3. Current data and analytics environment, including its strengths and weaknesses
4. Data domains and data products around which to organize the future state 

Often, we see the initial data governance effort tied to retroactive governance. This means there has been an initial push on the existing data within the organization, and there has been some effort to enrich this data and then drive awareness of the enhanced discoverability of the data, considering “Enabling Self-Service” complete. 

What’s often forgotten are the future-state human behaviors that must be adopted to ensure the data enrichment is proactively embedded within the organization’s business processes. Ask these questions:

• What is the operating model? 
• What are the governance priorities for data domains and products? 
• What will the beneficiary’s workflow look like? 

These are all considerations to ensure retroactive work becomes proactive in the future.

Step 2: Formulate and Align Around a Governance Scope

Next, formulate a data governance program to match the current state. It may change or expand over time in accordance with data’s expanding role in driving successful business outcomes (see Step 1).

Start scoping by evaluating and understanding the current state and goals of the business in the context of the following five pillars of expansive use of data: