In this article, we address some of the most important debates currently happening in the world of data. We reflect on the role that data analytics areas play in companies today. We review the main challenges they face and the traditional ways of structuring them.
Additionally, we provide advice on how to transform the analytics area from meeting isolated requirements to delivering solutions that provide real business value.
What is the main problem that the data analytics area faces today?
Not long ago, data analytics areas were always within the realm of technology, but now the trend is for them to increasingly depend on a leadership team or even be directly integrated into it. This makes them more exposed—both visibly and in terms of budget—and places a greater responsibility on them to meet the challenge of producing results. However, the reality is that many data areas still struggle to deliver value to the business. This is a problem that troubles many data leaders.
As things stand today, it is very challenging to develop data & AI products that people can access autonomously and use to make better decisions. The good news is that there is still much to be done. If we can properly organize the data area, we will be able to deliver real business value.
Current context of data analytics areas
The situation is complex: in organizations, individuals with sufficient expertise to define data objectives and projects are still not abundant. Expectations are unclear, information is obtained from different sources, best practices are not followed, and many more challenges exist. The result? On one hand, data analytics areas are overwhelmed without a clear direction, and on the other hand, businesses invest heavily in data teams that are expensive and fail to deliver the expected value.
Unlike other IT areas or services, analytics is a cross-discipline that spans the entire organization. It relies on an architecture of different components that provides people with quick and easy access to information, enabling them to make better decisions. Therefore, analytics is not a technology or a product.
How companies organize their data analytics areas: advantages and disadvantages
Given this context, companies have generally organized the data analytics area in two ways:
- As an analytical department that provides services to various business units.
- In different integrated analytical departments within each business unit.
While the decision of where to place the area depends on various factors, below, we explain the advantages and disadvantages of each of these forms.
SINGLE STRUCTURE -> SERVING VARIOUS BUSINESS UNITS:
In general, IT is a centrally operated area within the organization, comprising sub-areas that depend on it, and this is where the analytics area would be located. Like other departments, the area operates by processes and meets the demands of its internal clients (finance, sales, logistics, human resources, etc.) This type of organization is generally understood by all companies and is not always up for discussion.
However, over time, doubts often arise in data analytics areas: Who is the internal client? Sales? Logistics? Administration and finance? The answer is simple: all of them are! So, which one takes priority when everyone demands at the same time? Who decides what is most important? Which project is carried out first?
These structures have some advantages, such as high synergy of technical profiles and access to data management. However, while these teams will have high expertise in analytics, they may lack in-depth knowledge of each business area. Additionally, in these setups, priority management is often complex and can lead to bottlenecks that cause problems in the value delivery chain. Some of the most common issues include:
- Unmet demand due to lack of prioritization: Multiple projects emerge in different areas that inevitably lead to conflicts of interest when it comes to prioritization.
- Lack of maturity in data-driven cultures: Many companies fail to see value in analytics developments. They don’t know how to place orders because teams are not trained, or areas are not mature enough. They are unaware of the potential and forms of analytics, making culture and organizational communication crucial.
- Lack of commitment from other areas: Generally, analytics issues cut across the entire company. It’s essential to involve different areas as each has a valuable contribution. Involving areas in analytics that are not project owners is a central challenge that requires attention.
Scheme with a centralized data analytics area.
DIFFERENT STRUCTURES -> INTEGRATED WITHIN THEIR BUSINESS AREAS:
However, there are also companies that are changing the paradigm: they have created specific data analytics teams for each area (finance, sales, logistics, human resources, etc.) In this way, the department ceases to be the sole service provider to the entire organization and begins to respond to the demands of each particular area.
In these cases, each analytics team is integrated into the business, which makes us gain agility and makes it easier to establish priorities. However, some other difficulties arise:
- Loss of synergy in resources: Technical profiles are distributed across different business areas. This makes it difficult for them to share knowledge, work methods, or experiences.
- Information silos: Each area manages its own information in isolation. This can lead to duplication in another area or in several areas, and an increase in costs may occur if data is not properly governed.
- Cost increase: It is possible that costs may rise due to the acquisition of more software licenses, more analytical profiles, and often different architectures.
Scheme with a decentralized data analytics area.
💡 The best way to organize a data analytics area is to create hybrid structures that include mixed teams with technical profiles and experts from the business area.
How to provide data analytics solutions that generate real business value?
To do this, we recommend:
1) Build Loyalty: Work to ensure that key stakeholders understand the value that data analytics projects bring to the business. These are the individuals who need to see concrete results. Seek high-level sponsors, individuals with decision-making capabilities who can witness how analytics brings improvements to their processes.
For example, a logistics manager who needs to see real-time truck routes to optimize operations, a marketing director who uses data to retain (and gain) customers, or a maintenance leader who has a clearer idea of which equipment needs repair, etc. The examples are numerous; the important thing is to find people willing to support analytics projects because they see real value in data usage.
It’s also crucial to involve the individuals who made the request, making them part of the project to ensure that the solution delivered will have real value for the business.
2) Set Short-Term Objectives: Start with small projects that show results. Aim to deliver value as quickly as possible; this will lead responsible individuals in different areas to find utility in analytics projects and support them. It is crucial not to be perceived as an area that consumes large budgets without delivering value. Focus energy on achieving quick victories and create the necessary momentum to show results. Avoid grandiose projects as they often do not end well.
3) Govern the Data: Companies make mistakes related to isolating analytics. We cannot implement in isolation; it’s important to align initiatives with other organizational goals. We need to be able to integrate ourselves into the company, making sure they know who we are and what we do, rather than being perceived as an isolated area that no one is clear about.
Challenges for Data Analytics Areas
Experience tells us that there is no one-size-fits-all approach to organizing the analytics area. It will depend on the company’s level of maturity in using its data, its culture, and its size. However, if we want the analytics area to deliver real value, we need data analysis specialists and business experts to collaborate. It is crucial to define processes to align data and analytics initiatives with business objectives.
All these challenges have solutions. If the data team has become a bottleneck or if there is an excess demand for analytics projects, it’s always possible to hire external teams from qualified providers to work on critical projects for the company. If, on the other hand, what is lacking is a uniform methodology with different criteria at play, efforts should be focused on defining standards and, above all, training people. After all, that is the most asset we will have.
We need to build business knowledge in the analytics areas and vice versa. All of this contributes. Let’s think that it’s not the same to receive an order and fulfill it as to provide solutions that add value to the real business.
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The original version of this article was written in Spanish and translated into English by ChatGPT.
