Organizing Data Teams and Measuring the Success of Data Observability

This chapter is about how to introduce data observability to your team. It describes the different types of data teams, the different types of organizations these teams need to fit into, and how you can measure the success of this initiative.

First, we will analyze the data team, understand its main roles in detail, and analyze the characteristics of each role and the way they work together. It will also be important to understand how these data teams can be organized and how to better organize the data teams to achieve optimum results. We will see that there are different ways to organize a team, all with their advantages and disadvantages. We will analyze these in detail.

We will also see that these data teams are not easy to manage and that the organization depends on several other factors, such as their size, their maturity, and how the entire company is structured. We will also see that certain types of...

Defining and understanding data teams

In recent years, investment in data platforms and tools has grown exponentially. At the same time, and proportionally, investment in data teams has increased to the point where the number of data teams is in the hundreds and even thousands.

On the one hand, this has been and continues to be an exciting time for the data ecosystem and for those who work in it, but on the other hand, this exponential growth has also brought with it a whole new set of challenges, not only technical but also organizational. Over the years, several questions have spontaneously risen:

• How can I scale a data team?
• What skills and roles are required for the success of my data investment?
• How does management, such as the hiring process and budget, differ for these specific roles?
• How can I improve communication between my data team and the rest of the organization?

These are non-trivial questions that are difficult to answer. Simply put, we...

Data mesh, data quality, and data observability – a virtuous circle

Data mesh, data quality, and data observability are three very important components that can help you build a robust and effective data strategy in your organization since each component plays a specific role in ensuring that data is accurate, consistent, and available. All of these components can ensure that your organization can make informed decisions and base your decisions on data that is not only available but also as accurate as possible.

By working together, these three components – data linkage, data quality, and data observability – can create a virtuous cycle that builds confidence in the data and the strength of your data infrastructure and architecture, leading to better outcomes for your data teams and, of course, all stakeholders who rely on your data teams and outcomes.

To understand how and why these components can and must work together, it’s important to know what...

The first steps toward data observability and how to measure success

Implementing a data observability initiative in an organization requires performing a series of steps. This section will provide general guidelines to help you get started.

First, you have to identify and involve the stakeholders who will be affected by the data observability initiative. This may include data analysts, data engineers, business analysts, and other members of the data team.

After detecting the stakeholders, it’s time to define the clear scope of this data observability initiative. This may include what data sources to include, what types of data quality issues you want to address, and what metrics you will use to measure success. Usually, the best way to do this is to define the KPIs for measuring the success of the data observability initiative. You must define the KPIs that will be used to track the effectiveness of the initiative, such as the number of incidents resolved, time to resolution...

Measuring success

Having established the plan, we now need to measure the success of our data observability initiative. While this can be difficult, it is important to understand the effectiveness of the initiative and identify areas for improvement. Here are some ways to measure the success of a data observability initiative:

• Reduce data incidents: One of the main goals of a data observability initiative is to reduce the number and severity of data incidents. You can measure the success of the initiative by tracking the number of incidents before and after the initiative is implemented, as well as the severity of those incidents. Ideally, you should see a decrease in both areas.
• Time to resolution: Another important metric you should track is the time it takes to resolve data incidents. The faster you can identify and resolve issues, the better. You can measure the success of the initiative by tracking the average time it takes to resolve incidents before and after the...

Summary

In this chapter, we took a deep dive into how data teams have evolved by understanding the various roles and their responsibilities.

We also faced and understood how complex it is to organize these teams, which often work horizontally within the entire company.

We also learned about the key factors for the success of these teams as well as the key factors for the success of large and complex initiatives, such as the introduction of a data observability project in the company.