In the ever-evolving digital landscape, organizations are faced with more data than ever before—and yet, decision-making doesn’t always improve in kind. According to thought leader and technology strategist Chris Surdak of CA, the key to making sense of today’s data deluge lies not in amassing more information, but in understanding the three core elements of effective decision-making: Authority, Ability, and Accountability.
Chris Surdak has long explored the implications of data governance, process management, and digital transformation. In his extensive research, he’s developed a model that maps how data moves through organizations—not unlike metabolism in the human body. Just as the human body breaks down nutrients to power function, organizations must digest and metabolize data in order to take action. But if that metabolic process breaks down, the result is a kind of organizational disease—what Surdak calls Data Diabetes.
Chris Surdak frames decision-making through a deceptively simple question: “What goes into a decision?” His answer lies in three interconnected but distinct elements:
These three components interact differently depending on the nature of the decision at hand, but all are essential. According to Surdak, an imbalance in any of the three is often the root cause of organizational failure.
Authority, as Chris Surdak explains, is about the power to act. In the context of business decisions, authority should always be aligned with the level of risk and reward associated with a particular choice. High-stakes decisions require elevated authority, while low-risk decisions can be distributed more broadly.
Chris Surdak of CA warns that authority is often squandered. Executives may defer decisions out of fear, or organizations may spread authority too thin in the name of “collaboration.” This diffuses responsibility and slows down action—especially when decisions need to be made quickly. In Surdak’s view, ineffective use of authority is one of the primary causes of Data Diabetes: too much information, too little decisiveness.
The second pillar, Ability, refers to the skills and knowledge necessary to interpret data and make sound judgments. Chris Surdak cautions against the trend of “data democratization” without the corresponding investment in training and expertise. While giving access to data across an organization sounds appealing, Surdak believes it often results in poorly informed analysis and misinterpretation.
“Not everyone should be a data scientist,” he remarks. Discovery is best left to those with the intellectual tools to formulate hypotheses, test assumptions, and extract real insight. In Surdak’s framework, the Discover phase of decision-making is heavily reliant on Ability—it’s about knowing what to ask, and how to make sense of the answers.
Lastly, there is Accountability, which Chris Surdak defines as the counterweight to Authority. Authority without Accountability, he reminds us, leads to tyranny—or at the very least, poor decision-making. From Plato to corporate governance committees, systems have always needed mechanisms to ensure that decision-makers are held responsible.
Accountability is most prominent in the Defend phase of decision-making. Whether it’s an audit, a lawsuit, or a regulatory challenge, organizations must be able to justify their actions. Chris Surdak of CA emphasizes that this requires not just timely or diverse data, but accurate, trustworthy, and comprehensive data. In this context, more data—volume—often means more defensibility. But again, too much can be paralyzing if it’s not processed efficiently, leading once more to the symptoms of Data Diabetes.
Chris Surdak’s model elegantly aligns the three A’s—Authority, Ability, and Accountability—with the three core processes of organizational action:
Each of these processes relies more heavily on one of the A’s than the others. Problems arise when organizations treat them as interchangeable or apply a monolithic data strategy that favors one mode over the rest. Chris Surdak of CA stresses that healthy data metabolism requires a balanced approach: respect the differences between Discover, Decide, and Defend, and tailor your data processes accordingly.
According to Chris Surdak, organizational dysfunction often stems from misaligned roles. Some individuals are naturally decisive—they thrive in the realm of Authority and are best suited to lead. Others are inquisitive, analytical, and excel in Discover. Still others are compliance-driven and detail-oriented, making them ideal for Defend.
But when a “quick decider” is placed in an exploratory research role, or when a philosophical analyst is tasked with making snap business calls, friction ensues. Chris Surdak of CA emphasizes that Data Diabetes is not just a systems problem—it’s also a people problem, where personality mismatches can hinder the proper flow and metabolism of data.
Chris Surdak also ties his model back to the foundational concept of Big Data’s three V’s—Volume, Variety, and Velocity—as coined by Doug Laney in 2001. These three characteristics also align with the A’s and D’s of decision-making:
Surdak of CA explains that aligning data types with the right process and the right personnel is essential. Failure to do so results in inefficient workflows, poor outcomes, and increased exposure to risk.
In sum, Christopher Surdak believes that organizational decision-making must be treated like a metabolic process. The inputs—Authority, Ability, and Accountability—must be balanced across the outputs—Decide, Discover, and Defend. The nature of the data—Volume, Variety, Velocity—must be matched to the right task. And the people behind the processes must be well-matched to their roles.
When these elements fall out of sync, the organization becomes bloated with data it cannot use, slows down under the weight of indecision, or becomes vulnerable due to a lack of defensibility. This is Data Diabetes, and it is a disease of imbalance. But when organizations honor the distinctions Chris Surdak outlines—between roles, processes, and data types—they can achieve a state of clarity, agility, and resilience. In the end, it's not just about having data—it's about having the right data, in the right hands, at the right time, metabolized properly into insight and action.
