First of all, we need to talk about organizational alignment and why it is so important.

What do I mean by organizational alignment? Alignment is when a company works as a single organism, like gears, where all elements complement each other, ensuring the smooth operation of the entire mechanism.

The success of all your data projects comes down to shared, well-communicated values within the company, while lack of organizational alignment is the number one reason why most data science and AI projects fail to be executed or adopted.

Whole books can be written about company alignment. It’s a vast subject. I’m not trying to replace any of them, but I couldn’t write a book about data strategy without mentioning it and how it relates to our initiatives.

Some symptoms allow us to quickly identify misalignment between the company’ data initiatives and the organization itself:

  • People don’t understand what data science is, how it works and how it will help them or the organization.
  • Solutions are developed but not used;
  • A general fear or distrust of data and AI.

In companies with a healthy data strategy, people understand the DSAI solutions - not necessarily technically but conceptually. They know why these solutions exist and how they help them, individually and as an organization.

When implemented right, these solutions are quickly absorbed by the organization, many times in a fully transparent, seamless way. By a “seamless way” I mean that the most successful designs are not visible at all, they work in the background, and people realize their significance only if they were taken away for some reason.

These systems are, without exception, centered around people’s needs and the company’s goals and integrate perfectly with the existing solutions.


When an organization is aligned, everyone is working in the same direction and aware of the same goals.

The most obvious thing to do is to define a long-term vision. This abstract concept reveals what an organization most hopes to be and achieve long-term at the highest level.

However, even though that it’s great to have a direction in which the company is heading, its natural ambiguity makes it open to different interpretations. That’s why it’s also helpful to turn your strategy into concrete goals and divide them into measurable objectives and actions that employees should take to achieve the overall goal.

Many frameworks exist to measure goals and objectives. One of the first was MBO (Management by Objectives), presented in 1954. At the time of this writing, probably the most known one is SMART. However, companies are increasingly introducing the concept of OKRs (Objective Key Results) and KPIs (Key Performance Indicators) which is also a way to monitor performance and direction.

Don’t worry if you’re not familiar with these concepts. They will be explained in the simplest way possible further down the book, in particular on our upcoming chapter on The Importance of Observation and Tracking Metrics and KPIs. In this chapter we will explore how selecting and employing the right KPIs can illuminate our path, ensuring our data-driven initiatives remain aligned with our overarching goals.

In a data-driven company vision, objectives and the health of day-to-day operations can be measured with data and driven by data. These measurements are necessary to build a successful data strategy since your designs will be based on them and then further evaluated against previously defined indicators.

Need-driven, not tool-driven

The pile of projects that don’t see any adoption is tall and keeps on rising.

Perhaps the main reason for this is that they do not satisfy the real need, and even if they do, they do it badly. Often, acting with good intentions, we forget that only those who face the difficulties we’re trying to solve really understand them and, most likely, they will be the ones applying your solution.

If we want these projects to be accepted and adopted, DSAI projects must be user-centered, i.e., based on people’s actual needs and demands, with feedback from the right people. The right people are rarely data scientists, because these are usually not the ones facing the problems they are solving.

Focus on the highest priority needs and problems, learn as much from the people that face them, and use technology to solve them.

Be need-driven, not tool-driven.

This book will help you understand how to do this by conducting interviews and observations with the people who will actually use the solutions.

Communication and understanding

I can’t even say how many times I found someone was afraid to talk to me knowing that I have worked on automation and even built super-human artificial intelligence. It’s a reasonable fear as we often see automation as a way to replace humans. However, I see artificial intelligence and data science differently. I see them as leverage to empower people, not replace them.

For companies seeking to grow, the ultimate goal of the data strategy is not to replace people in organizations but to help them perform their jobs more efficiently and effectively than before. People will be replaced at their repetitive, simple processes. However, once these are automated, people can then focus on high-quality problems, problems that only humans can solve, problems that are truly complex and require creative solutions or human interaction.

This idea is not the most common one. Since childhood, films, articles, and other media have presented us with only one side of technology and AI, usually an adversarial one. However, we should understand that when applied correctly, artificial intelligence and data science won’t end up creating overlords ruling the world. Instead, they will serve us and help us achieve what we want.

The only way to change this opinion is through communication. There is a whole chapter in this book written on communication but if I can give you two rules right now: keep it simple and make it visual.

From Alignment to Action: Setting the Stage for Initiatives

As we have explored, organizational alignment forms the backbone of successful data strategy, acting as the cohesive force that ensures all efforts and resources are directed towards shared goals and values. This alignment is not an end but a beginning, setting the stage for the next critical step in our journey: the transformation of data science and AI ideas into impactful Initiatives.

In the forthcoming chapter on Initiatives, we delve into the practical aspects of turning these aligned efforts into concrete actions. We will explore how to define, prioritize, and adapt initiatives in a way that maximizes their impact and efficiency. The transition from alignment to action represents a crucial phase where strategic visions are operationalized, ensuring that the organization’s collective efforts translate into meaningful advancements.

Understanding how to effectively transform these ideas into initiatives will enable us to apply our aligned strategies in a manner that is not only productive but also dynamically responsive to the evolving landscape of data science and AI. This progression from alignment to actionable initiatives embodies the essence of a truly data-driven organization, one that is poised for sustainable success in an increasingly data-centric world.

Stay tuned as we embark on this next chapter, Initiatives, where we will lay out the blueprint for translating organizational alignment and strategic goals into real-world success stories.