Where should we currently position the Data area within organizations?

Today, we’ll be discussing an important question: where should the broad area of data be positioned within organizations – in business units, IT departments, or perhaps as a separate organizational unit? Thank you for being here to share your insights!

Good morning, ?ukasz. First of all, thank you for accepting our invitation and deciding to participate in our podcast.

?ukasz Wróblewski :

Good morning. I'm very pleased to be here today with you and have this conversation because, as you could say, I am a passionate about this field. I’m delighted to have the opportunity to share my knowledge with others, and I hope it can reach even further.

We have an interesting topic today, which consists of several questions, and we will try to answer them during our conversation. To begin, I suggest we define the key skills and competencies for a team that deals with the broad area of data, regardless of where this team is located within the organization.

Of course, the most prioritized skills are, as probably everyone knows, technological skills – the fundamental knowledge of the tools we use, whether it's databases, data pipelines, data processing, or handling large volumes of data. However, I think it's worth noting that people in this data field, besides the essential technical competencies, should also possess what I would call softer skills. I don’t mean traditionally defined soft skills but rather abilities related to understanding the business, its processes, so that they can collaborate as effectively as possible with business partners due to the nature of the data field, which is never one-sided. It’s a bridging area between different worlds.

Additionally, I would also say that what distinguishes people in this team is, on one hand, openness to technology, due to the rapid development in this area, but also the ability to build a bridge between the fast-evolving technological world and the business world, which has to find its way in this constant change. We’re currently seeing an expansion in data volume and AI, and at the same time, businesses have to navigate these changes effectively.

You emphasize that, apart from technological skills, business competencies and the ability to collaborate are also important. This is crucial because in a rapidly evolving data environment, it’s not enough to know the technology – you must also understand how to support business goals. So, we have a team with broad competencies. Let's move on to who should sponsor data projects in our organization. What characteristics would be key for such a sponsor?

I’d say it really depends on the organization and the goals set for the data area. As you mentioned earlier, this area is quite broad. We can have a project related more to just reporting, a project focused on delivering data, or a full-scale project that covers both the technological and reporting parts. From my experience, in many cases, a business sponsor tends to work better for such a project, especially in the early stages when the organization is still learning how to handle data. This is because a business sponsor is very close to the business side, which allows for better translation of project expectations and challenges faced by the business, leading to more effective solutions in the project. Another aspect worth mentioning, especially when defining a sponsor, is that data projects often have a characteristic challenge: it’s not easy to estimate the ROI or the direct profit the organization will achieve immediately after implementing such a project. That’s why I believe that a sponsor should also be a strong advocate for these kinds of projects and innovations across various levels of management.

We see that the choice of the right sponsor directly affects the success of the project in the organization. ?ukasz, how do you think placing such a data team within the IT department can impact the speed and efficiency of data-driven decision-making across the organization?

Given that, as we mentioned earlier, technological competencies are key here, the IT department allows us to locate and find the right people with the skills needed to best and most efficiently utilize the tools and platforms the organization uses, thus delivering the data or information the business seeks to make decisions. On the other hand, and this might be an unpopular opinion, I believe that individuals deeply rooted in IT tend to approach processes, whether it’s data flows or reporting processes, more operationally. This allows for a higher level of data quality control and process quality control, which, from my perspective, also impacts decision efficiency. It’s hard to make an effective decision based on incomplete or uncertain data. Furthermore, greater focus on technology and the opportunities it provides through IT placement can drive the use of data, encourage the business to make the best use of the data, and simultaneously simplify and automate processes wherever possible, to achieve the goals as efficiently as possible.

I agree with your insights. Let’s now look at the issue of the placement of our data team from another angle. Could you please point out the main advantages and disadvantages of positioning a team responsible for data in the business structure of the organization?

I believe one of the main advantages that comes to mind is more efficient implementation of governance – meaning the set of rules related to data operations and processing. This is because, at least based on my experience, such teams are often located in finance or controlling areas, which are typically highly structured and standardized departments. This allows for a more unified approach to processes and rules within the organization. Additionally, I think a significant advantage of this placement is that the team is closest to the analysts and data consumers, allowing them to fully understand their needs and deliver what is expected by business partners more efficiently. As for the disadvantages, I believe placing the team solely within business structures may somewhat disconnect them from the technological realities and capabilities that the organization possesses. From a business perspective, the focus is on the goal we want to achieve, and sometimes less attention is paid to how we will achieve it.

However, from my perspective, especially coming from an IT background, it's crucial to consider how we achieve the goal, such as the tools and methods used. It’s essential to avoid creating technical debt, which I think is a risk when such teams are placed purely in financial departments.

From what you’re saying, it seems that placing the data team within the business structures has its advantages, like proximity to analysts and a better understanding of user needs, but there are also risks, such as detachment from technological possibilities and the accumulation of technical debt. So, there’s no universal approach, and the key is collaboration between business and IT. What are your thoughts on effectively engaging both sides to create the necessary synergy in the data area?

As you mentioned, data projects are never one-sided. They always require synergy between business, technology, and the data team itself. I think a good approach to achieving this synergy is through the creation of cross-functional teams that don’t focus solely on one of the mentioned aspects. By establishing such teams and their daily operations, we create a space where people from different environments, whether business, data, or technology, can exchange experiences and knowledge, enriching the work in this field. Additionally, I think it’s important to emphasize the selection of the right people. This rule applies not only to data teams but to all areas. Having the right advocates or evangelists within such teams can ensure that the solutions provided are used effectively. I believe that having someone who is passionate about the subject, who can engage and inspire others, is crucial to maintaining the synergy between the technological, business, and data realms. Ultimately, the success of any project depends on the contribution of all involved parties.

Indeed, building cross-functional teams that combine business, technology, and data knowledge is essential for creating the right synergy. It’s also important to have individuals who act as "data advocates" and can inspire and engage others. However, in large organizations, wouldn’t it be better to create a dedicated unit responsible for managing the entire data area? What are your thoughts on this?

I’d say it depends. In smaller organizations, we often encounter situations where roles are divided among people working in different areas, which makes specialization or the creation of separate units more difficult. However, in larger organizations, as you mentioned, it seems like a natural evolution to separate the data area because, over time, the responsibilities of people from business or IT who are involved in data begin to consume more resources and time, making it harder to perform other duties efficiently. That’s why I believe that, particularly in larger units, separating this area allows individuals to focus on core data activities. These individuals are not just part of IT or business, with data-related tasks as an additional responsibility, but they are fully aware of their role in the organization, allowing them to develop further in this space.

What challenges might arise when creating such a central data unit in a large organization?

One challenge I would call the “syndrome of being in-between.” As we discussed earlier, data projects require synergy between business and technology. When you have individuals who aren’t directly part of IT or business but are in-between those two sides, the challenge is to build the necessary relationships with both sides. If the data team is placed in IT or business, those relationships naturally exist because of the team’s location. However, when the data team is separated, while it allows them to focus on data, it also requires building strong relationships with both IT and business, which can be challenging, especially in dynamic environments where both sides have more and more commitments.

You’re addressing an important issue of the “syndrome of being in-between,” where data teams balance between IT and business. It’s true that these teams must not only focus on data but also build strong relationships with both sides, which can be difficult. Therefore, it’s crucial to think carefully about where to position the unit responsible for data in the organization to maximize its value. What factors do you think should determine this choice?

Unfortunately, I might disappoint you and our listeners here, as in the case of Data Governance, there isn’t a golden rule that we can apply everywhere to move from point zero to the desired outcome. Each organization must, in a way, discover its path through trial and error. However, if we were to decide where such a unit should be located, I’d say the key is not to create a data revolution. In other words, start where analytics is already focused.

What if, after analyzing these factors, we find that the current placement of the data unit in our organization isn’t optimal? How can we manage the transformation between different “schools” of organizational placement for the data unit?

For a mature organization, this can be a significant and costly change because restructuring an already established area can be challenging. I believe that transformation happens during the maturation process, where responsibilities can still migrate between departments. However, the key factor in such a transformation is having the right people in place who can lead the process and engage management in advocating for the significance of the data area. It’s also worth mentioning that separating the data area is a natural step, especially in larger organizations, as they mature and recognize the need for data in making effective decisions. However, we should always remember that data is merely a tool to achieve a business goal – the goal is to deliver information that business partners can use to make informed decisions.

I fully agree with your observation that transforming the data area in mature organizations can be challenging, especially when significant structural changes are required. It’s crucial to have the right people in place who can engage both operational and management teams. Let’s now move to a more specific situation: what should be done when a data project evolves into a permanent process? How should its placement within the organization change then?

This is a question that probably many of our listeners have asked themselves. It’s one of the first signs of the data area maturing in an organization – when a project transitions from a project mode into a permanent process. My advice here is not to treat this process as something attached due to organizational standards. For instance, if the project was sponsored by IT, it becomes an IT process; if it was sponsored by business, it becomes a business process. I would avoid such easy classifications because, in all these data projects and processes, the key is the active engagement of all sides. It’s essential to focus not only on the end consumers of the data but also on the producers to create full synergy within the process.

Thank you, ?ukasz, for a very interesting conversation. Sharing your knowledge, insights, and thoughts on the placement of the data unit within an organization was incredibly valuable. I’m sure our listeners followed the discussion with great interest.

Thank you once again for giving me the opportunity to share this knowledge, which I hope was valuable to our listeners. And what I can say is: I wish everyone success, as those who have worked in this area know it’s a rewarding and important field, though sometimes a challenging one.