Boomi

Data Management for AI and Agentic Systems

Building Reliable AI Models and Agents

With Early Release ebooks, you get books in their earliest form—the author’s raw and unedited content as they write—so you can take advantage of these technologies long before the official release of these titles.

Michael Bachman

Data Management for AI & Agentic Systems

by Michael Bachman

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April 2026: First Edition

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Brief Table of Contents (Not Yet Final)

Chapter 1: High-Stakes Data—Why Quality, Governance, and Real-Time Management Matter for AI (available)

Chapter 2: The Data Problem in AI—and the High Cost of Getting It Wrong (unavailable)

Chapter 3: The Essentials of Data Management for AI (unavailable)

Chapter 4: High-Quality Data—The Secret to Smarter AI and Agentic Systems (unavailable)

Chapter 5: Practical Advice for Managing Data in the Age of AI Agents (unavailable)

Chapter 6: Building a Scalable Data Management Framework for AI Agents (unavailable)

Chapter 7: Stories from the Field—Real-World Lessons in AI Data Management (unavailable)

Conclusion: Fix Your Data Before AI Agents Break Everything (unavailable)

Chapter 1. High-Stakes Data—Why Quality, Governance, and Real-Time Management Matter for AI

A Note for Early Release Readers

With Early Release ebooks, you get books in their earliest form—the author’s raw and unedited content as they write—so you can take advantage of these technologies long before the official release of these titles.

This will be the 1st chapter of the final book.

If you’d like to be actively involved in reviewing and commenting on this draft, please reach out to the editor at gobrien@oreilly.com.

The AI (and Agentic) Imperative

Imagine walking into your office one morning to find that your most capable assistant has evolved overnight. Yesterday, they could only answer questions and draft documents when you asked. Today, they’ve begun anticipating your needs, scheduling meetings before you realize you need them, and resolving problems while you sleep. This isn’t science fiction. It’s the shift we’re experiencing as artificial intelligence evolves from a sophisticated tool into an autonomous partner. In November 2022, ChatGPT introduced the world to AI that could generate human-like responses to any prompt. Less than three years later and at the time of the writing of this book, we’re witnessing the emergence of agentic systems that don’t just respond but actively pursue goals, make decisions, and take actions on our behalf. This transformation is as profound as the leap from calculators to computers, and it’s happening at unprecedented speed.

The numbers tell a compelling story. Within a year of generative AI’s mainstream debut, one-third of companies were using it regularly in at least one business function.¹ By 2024, nearly a quarter of C-suite executives were personally using generative AI tools in their daily work.² This explosive growth signals that AI has moved from experimental technology to business imperative.

But generative AI is only the beginning. The next frontier promises even more profound transformation. Unlike generative AI, which creates content in response to prompts, agentic systems can autonomously perceive situations, make decisions, and take actions to achieve goals. These aren’t just tools that assist. They’re digital colleagues that can drive entire processes.

Understanding Agentic Systems

Agentic AI represents a fundamental evolution in artificial intelligence. Where traditional AI analyzes and generative AI creates, agentic AI acts. These systems operate through continuous cycles of observation, planning, execution, and learning. They function much like human workers, but at digital speed and scale.

Consider the practical implications. An agentic system doesn’t just predict equipment failure. It can automatically order replacement parts, schedule maintenance, and adjust production schedules to minimize downtime. It doesn’t merely detect fraud. It can freeze suspicious transactions, notify affected parties, and initiate investigation protocols without human intervention.

The rise of agentic AI is driven by both technological maturation and pressing business needs. Foundation models now understand complex instructions and handle broad task spectrums. These serve as the “brains” of AI agents, enabling them to interpret context and perform multi-step reasoning in ways earlier software couldn’t.³

Business drivers are equally compelling. Enterprises today operate in environments of extreme complexity and speed. Manual decision-making and human-only processes can no longer keep pace. AI agents offer a path to scale decisions and actions at digital speed, around the clock. A recent Deloitte report found that 26% of companies are already exploring autonomous agents extensively in their business.⁴

Strategic Benefits of Agentic Systems

Organizations adopting agentic AI systems report tangible improvements across several dimensions. In operational efficiency, these systems automate multi-step workflows end-to-end, eliminating delays between steps and reducing human handoffs. A field service operation might use an AI agent to automatically prioritize and schedule work orders based on IoT sensor data and asset health. This task previously required coordinators to manually triage each job.

Cost savings accompany efficiency gains. Agentic AI systems reduce human error margins and optimize resource utilization continuously. Google’s data centers demonstrated this early when an AI agent autonomously managed cooling systems and cut energy consumption by up to 40%.⁵ That translated to millions in savings annually.

Decision quality improves when AI agents ingest vast data volumes and analyze them in real time. They don’t fatigue and can evaluate options against defined objectives tirelessly. In financial services, agentic AI monitors transactions continuously, detecting fraud signals or market opportunities that humans might miss, then acting immediately.⁶

Scalability becomes a software problem rather than a hiring challenge. Once an agentic system is in place, handling 100 customer requests isn’t fundamentally harder than handling 10. AI agents perform tasks consistently to the same standard every time, improving compliance and customer experience.

Defining the Data Crisis

Organizations face a data crisis in their AI initiatives. Fundamental data issues threaten to derail AI projects and business goals. The data crisis refers to managing data quality, silos, and governance challenges that have become urgent, high-stakes problems rather than routine IT tasks.

Recently technology analyst firm, Gartner, suggested that by 2027, a staggering 2/5 of Agentic AI projects would be canceled due to, among other things, “inadequate risk controls.” Even though data wasn’t explicitly called out, it was inferred. After all, the article consistently heralds adding value being the true driver for success with AI. Other research suggests only 3% to 7% of enterprise data is actually fit for AI use.⁷, ⁸ If this is true, one can infer from these sobering statistics how unprepared most organizations are for using agentic systems because their data is simply not fit for purpose. The crisis manifests through multiple symptoms that executives should recognize.

AI projects stall or yield erratic results when data quality issues lurk beneath the surface. A pilot machine learning model might work fine on curated test data but produce bizarre outputs in production due to missing fields, incorrect values, or format inconsistencies. When projects repeatedly fail with “data not being ready” as the cause, you’re experiencing the crisis firsthand.

Data scientists report spending up to 80% of their time on data cleaning and preparation rather than actual analysis or modeling.⁹ This represents a massive productivity drain and signals systemic quality issues. When highly skilled professionals act as “data janitors,” the organization has a serious problem.

Conflicting metrics between departments reveal governance breakdowns. If Sales and Finance report different customer counts or revenue figures, they’re likely using different definitions or data sources. These discrepancies erode trust and lead to endless debates about whose numbers are correct.

Why Data Quality Became Critical Now

Several factors converged to make data quality and readiness pressing problems. Data volumes are exploding. The world’s data roughly doubles every few years, growing from 33 zettabytes in 2018 to an estimated 175 zettabytes by 2025.¹⁰ This deluge includes structured database records plus vast amounts of unstructured data from emails, social media, sensors, images, and logs.

Business decisions now happen in real-time or near-real-time. Many choices that used to be made monthly or weekly require immediate action. This puts pressure on data pipelines to deliver clean, current information continuously. Stale or siloed data leads AI models to act on yesterday’s news.

AI itself is far less tolerant of data flaws than traditional reporting systems. Machine learning models are extremely sensitive to data quality. Small errors or biases in training data get amplified by AI, leading to outsized consequences. Organizations learned through painful experience that even advanced models produce erroneous results when fed incomplete or biased data.

The stakes are higher because AI is used for critical decisions. From customer credit scoring to medical diagnostics, the cost of bad data is no longer trivial. A poorly trained AI can do more damage, faster, than a manual process ever could.

External pressures mount as well. New regulations and legal rulings hold executives personally accountable for data governance and quality lapses. Companies now treat data as a key asset and AI as a strategic priority. Deficiencies in that asset are felt more acutely than ever before.

Data Silos: The Hidden Crisis Multiplier

Data silos are pockets of information isolated in different systems or departments. They’re a major driver of the data crisis. Many enterprises have critical information locked in disparate databases, applications, or business units that don’t communicate. Organizations average over 2,000 information silos, ranging from CRM customer records to finance transaction data to product information in spreadsheets.¹¹

These silos severely hinder AI’s ability to draw accurate insights. An AI model is only as good as the data it can access. If an algorithm trains on just one data fragment, it works with limited scope and missing context. The model’s outputs may be technically correct for its data slice but wholly misleading in a broader context.

Silos breed inconsistencies and duplicates. Each system might have its own version of truth. Customer names might be spelled differently in two systems. Inventory items might have different IDs. Without integration, companies struggle to reconcile differences, undermining data quality.

The Datafloq report highlights how silos limit data velocity. Even if you technically have all needed data, if it’s scattered across systems, AI models can’t quickly access and analyze it together. Real-time analytics can improve operations dramatically. Supply chains integrating data in real time can reduce processing times by up to 80%. But benefits only materialize if AI can see all relevant data fast.

The Compound Cost of Poor Data Hygiene

Poor data hygiene refers to lack of proper cleaning, standardization, and data maintenance. It’s a core reason the data crisis worsens. Issues like duplicate records, missing fields, inconsistent formats, outdated entries, and typos seem mundane. At scale, they completely derail AI outcomes.

Consider how poor data hygiene misleads AI. A bank develops an AI model to predict loan defaults. The training data contains duplicated customer records and outdated income information. Some entries miss critical fields due to inconsistent data entry. Others use different formats that fragment data when aggregated. The model learns from this polluted data and might wrongly conclude certain customers are high risk.

Amazon’s experimental hiring algorithm provides a stark example. The AI was trained on ten years of past hiring data that reflected historical gender bias in tech. The AI learned these patterns too well, systematically downgrading resumes mentioning women’s activities.¹² Amazon scrapped the project after realizing the training data itself was biased.

Poor data hygiene means AI builds on a shaky foundation. Duplicate or missing data confuses algorithms. Inconsistent formats break merging and analysis. Outdated information leads models to draw wrong inferences about current conditions. Many organizations have accumulated years of such dirty data. Cleaning it requires diligent, ongoing effort that businesses often postpone until major failures occur.

What ‘Good Data’ Really Entails

Good data forms the foundation of successful AI initiatives. It means high-quality data that’s fit for its intended purpose in both training AI models and powering them in production. For AI systems, good data is trustworthy. It’s fresh, relevant, accurate, and representative of real-world scenarios the AI will encounter.

Good data exhibits several core characteristics. Accuracy is paramount. Data must correctly represent reality. High accuracy means values are true, error-free, and credible. If data is inaccurate, any AI model or decision process using it will be led astray.

Completeness ensures all required data is present. Data is complete when it has no critical gaps. All relevant records and fields needed for the task are available. If 10% of training samples lack labels or key attributes, the model may learn incorrectly.

Consistency means data is uniform and harmonized across datasets and sources. Consistent data doesn’t conflict with itself. If one database lists a product’s price as $100 but another shows $90, that’s an inconsistency. Good data eliminates such discrepancies by enforcing common definitions and formats.

Validity refers to data values conforming to expected formats, ranges, and business rules. A date field should contain real dates in proper format. A categorical variable should only have allowed category values. High-quality data has strong validity with all entries adhering to applicable rules and standards.

The Critical Role of Timeliness

Timeliness represents data freshness and availability when needed. It’s critical in fast-moving business environments and real-time AI systems. Even highly accurate data becomes stale as the world changes. In dynamic scenarios, outdated data can be as problematic as incorrect data.

Real-time and agentic AI systems underscore timeliness importance. Consider an autonomous trading agent or recommendation engine operating on yesterday’s data. They might miss crucial shifts like sudden market drops or customer behavior changes.

The COVID-19 pandemic provided a stark example. AI models trained on normal historical patterns saw those patterns break overnight. Consumer behavior, supply chains, and economic indicators changed dramatically. Models quickly became outdated. Instacart’s demand-forecasting AI accuracy plunged from 93% to 61% because it used weeks-old purchasing data that didn’t capture sudden spikes in items like toilet paper.¹³

Instacart’s data science team solved this by shortening the data window to the last 10 days instead of weeks. They traded data volume for freshness, and model accuracy rebounded. When conditions change quickly, recent data is far more valuable than outdated data.

How Governance Creates Good Data

Strong data governance provides the framework for achieving and sustaining good data. Governance encompasses policies, processes, and oversight ensuring data is managed properly across the organization. If data quality is the goal, governance is the framework making that goal achievable.

Robust governance programs establish clear standards for data quality. They define what “good” looks like for different data domains. Customer data standards might require every record has valid email and phone numbers. Governance assigns ownership and accountability to data stewards responsible for monitoring quality and correcting issues.

Data governance and quality go hand-in-hand. Governance provides strategy and guardrails while quality efforts implement checks and improvements. Without governance, even clean datasets deteriorate as new data arrives without control.

According to Precisely’s 2024 Data Integrity Report, 62% of organizations cited lack of data governance as the primary challenge inhibiting their AI initiatives.¹⁴ This suggests more than half of companies recognize that without governance, AI projects won’t succeed effectively.

The Hidden Value of Metadata

Metadata provides critical context that turns raw data into usable information. It answers essential questions. What does this data field mean? Where did this data originate? When and how was it collected? Who is responsible for it?

One key aspect is business definitions of data elements. A column labeled “Customer” might refer to individual end-users, corporate clients, or transactions. Data dictionaries clarify definitions, units, and allowable values, preventing misinterpretation and ensuring consistency.

Data lineage metadata traces data origins and transformations. When an AI model output is questioned, lineage metadata allows teams to audit the input data path. It’s difficult to trust data if you can’t trace its provenance. For regulatory compliance or ethical AI considerations, you must know if data was collected with consent and whether biases might be inherent due to its source.

Modern AI data management increasingly relies on data catalogs and model documentation. Data catalogs are organized inventories of data assets with metadata. They help users discover appropriate data and understand its context, including quality metrics.

Roadmap of the Book

This book guides technical managers, business leaders, and line of business stakeholders through the critical decisions and strategies needed for successful AI and agentic system deployments. While we include practical examples that practitioners can reference, our primary focus is helping leaders understand why certain data management approaches are essential and how to implement them organizationally.

Chapter 2 establishes the essentials of data management for AI. You’ll understand the core principles that distinguish AI data requirements from traditional analytics and reporting. We examine architectural patterns your teams will need to implement, evaluate modern data stack tools, and identify emerging requirements specific to agentic AI. The chapter helps you make informed decisions about technology investments and understand the implications of bias and ethical data practices for your organization.

Chapter 3 quantifies the data problem in AI and its business impact. We analyze why the explosion of unstructured data renders traditional management methods obsolete. You’ll learn to identify and bridge organizational disconnects between data teams and AI teams. Through detailed case studies, we demonstrate the real costs when bad data meets AI, helping you build the business case for proper data management investment.

Chapter 4 reveals why high-quality data is the differentiator between AI success and failure. You’ll learn to define and measure quality metrics that matter for AI applications. We explain how accuracy, consistency, and completeness directly impact agent performance and business outcomes. While the chapter includes a hands-on example of data cleaning, its purpose is to help leaders understand what their teams face and why data quality initiatives deserve executive attention and resources.

Chapter 5 provides strategic guidance for managing data in the age of AI agents. You’ll learn to implement governance frameworks that balance innovation with control. We explore why AI agents process real-time data fundamentally differently than traditional systems and what this means for your data infrastructure investments. The chapter helps you understand what your technical teams need to build and why certain architectural decisions are critical for success.

Chapter 6 addresses building scalable data management frameworks from an organizational perspective. You’ll discover the key pillars of successful AI data strategies and how to evaluate vendor solutions versus custom builds. We examine the economics of real-time data processing and help you understand when the investment is justified. The chapter also covers future-proofing your data architecture and ensuring your data management approach aligns with responsible AI principles your stakeholders expect.

Chapter 7 brings everything together through real-world lessons from the field. You’ll learn from organizations that successfully transformed their AI capabilities through better data management. We also examine costly failures, helping you recognize warning signs before they impact your organization. Expert insights from both technical and business leaders provide practical wisdom for navigating common challenges.

The book concludes with a strategic imperative: fix your data before AI agents break everything. We examine emerging trends in AI agents and data management, providing guidance on positioning your organization for success in an increasingly autonomous future.

Who Should Read This Book

This book is written primarily for technical managers, CTOs, CIOs, CDOs, and line of business leaders responsible for AI initiatives. You don’t need deep technical expertise, but you should understand basic technology concepts and be comfortable with strategic planning.

Data engineers, AI engineers, and ML practitioners will also find value, particularly in understanding the business context for their work. The examples and architectural discussions help practitioners see why certain approaches are preferred and how their work connects to larger organizational goals.

Business leaders without technical backgrounds can focus on Chapters 1, 3, and 7 for strategic context, while those managing technical teams should read the entire book to engage effectively with their organizations.

How to Use This Book

For strategic planning and budgeting, start with Chapters 1 and 3 to understand the landscape and quantify the opportunity. Use Chapter 6 when evaluating technology investments and Chapter 5 when designing governance structures.

For organizational transformation, Chapters 2 and 4 provide the foundational knowledge needed to lead change effectively. Chapter 7’s case studies offer blueprints and cautionary tales to guide your journey.

For vendor discussions and technology evaluations, Chapters 2, 5, and 6 arm you with the right questions and evaluation criteria. You’ll understand what vendors should provide and what capabilities truly matter.

Your Strategic Journey

Data management for AI isn’t just a technical challenge. It’s an organizational transformation that requires leadership, investment, and sustained commitment. This book provides the knowledge and frameworks to lead that transformation effectively.

You’ll finish with a clear understanding of why data quality, governance, and real-time management are make-or-break factors for AI success. You’ll know how to structure teams, allocate resources, and measure progress. Most importantly, you’ll be equipped to make the strategic decisions that separate AI leaders from those still wondering why their pilots never scaled.

The future belongs to organizations that master the intersection of data management and AI. This book ensures you’re prepared to lead that journey, making informed decisions that deliver real business value from your AI investments.

¹ McKinsey Global Survey, “The State of AI in 2023: Generative AI’s breakout year,” August 2023. https://oreil.ly/STib_

² McKinsey & Company, “The State of AI in 2024,” Survey of 1,491 respondents across 101 countries, July 2024. https://www.mckinsey.com/capabilities/quantumblack/our-insights/the-state-of-ai

³ Stanford Institute for Human-Centered Artificial Intelligence, “AI Index Report 2024,” April 2024. https://hai.stanford.edu/ai-index/2024-ai-index-report

⁴ Deloitte Insights, “TMT Predictions 2025: Autonomous generative AI agents,” January 2025. https://www.deloitte.com/us/en/insights/industry/technology/technology-media-and-telecom-predictions.html#media-entertainment - section on autonomous AI agents

⁵ DeepMind, “DeepMind AI Reduces Google Data Centre Cooling Bill by 40%,” Nature Energy, July 2016. https://deepmind.google/discover/blog/deepmind-ai-reduces-google-data-centre-cooling-bill-by-40

⁶ Gartner, “Innovation Insights: AI Agents,” April 2024. https://www.gartner.com/document-reader/document/5332663?ref=solrAll&refval=479097678

⁷ Gartner Predicts Over 40% of Agentic AI Projects Will Be Canceled by End of 2027, June 2025 https://www.gartner.com/en/newsroom/press-releases/2025-06-25-gartner-predicts-over-40-percent-of-agentic-ai-projects-will-be-canceled-by-end-of-2027

⁸ From a Linked-in Blog From Mark Stouse, CEO of Causal AI, and USC Lecturer. https://www.linkedin.com/pulse/data-quality-crisis-why-fortune-2000-companies-failing-mark-stouse-jlnzc/

⁹ CrowdFlower Data Science Report, “Data Scientists Spend Most Time Cleaning Data,” 2016 https://www.kdnuggets.com/2016/04/crowdflower-2016-data-science-repost.html

¹⁰ IDC, “Data Age 2025: The Digitization of the World,” White Paper, November 2018. www.seagate.com/our-story/data-age-2025/

¹¹ IDC, “Data Age 2025: The Digitization of the World,” White Paper, November 2018. www.seagate.com/our-story/data-age-2025/

¹² J. Dastin, “Amazon scraps secret AI recruiting tool that showed bias against women,” Reuters, October 2018. https://www.reuters.com/article/world/insight-amazon-scraps-secret-ai-recruiting-tool-that-showed-bias-against-women-idUSKCN1MK0AG/

¹³ Fortune, “How Instacart fixed its AI model during the pandemic,” June 2020. https://fortune.com/2020/06/09/instacart-coronavirus-artificial-intelligence

¹⁴ Precisely and Drexel University, “2024 Data Integrity Trends and Insights Report,” September 2024. https://www.prnewswire.com/news-releases/new-global-research-points-to-lack-of-data-quality-and-governance-as-major-obstacles-to-ai-readiness-302251068.html

About the Author

Michael Bachman serves as the Head of Research, Architecture, and Emerging Technology Strategy at the Boomi Innovation Group. He has worked as a Principal Architect and Staff Technologist within the offices of the CTO at Dell, Boomi, and Code 42. His career includes significant roles as a data storage engineer, thought leader, technical evangelist, product manager, and customer advocate across industries and various enterprise technologies. Michael is an inventor, author, podcaster, researcher, consultant, student, teacher, and futurist.