They say data is new oil. And its importance is paramount in digital transformation projects. There is no arguing here, but let’s not forget that such data‑driven transformation is a complex process that requires deep expertise, team coordination, and strong management. Today, large‑scale projects in digital transformation increasingly involve the Agile approach and competent specialists. But how well does this (initially) product development methodology fit the challenges and scale of data‑driven digital transformation projects? Let’s talk about it.

Three pillars of Agile

If you decide on the Agile methodology, the success of your data project in digital transformation will hinge on several key factors:

1. Internal stakeholder involvement: A project without a clear business owner lacks funding, decision‑making power, and prioritization. The project sponsor should be a senior business executive who understands the objectives, evaluates risks, and identifies the potential benefits.
2. A strong team: Whether internal or external, the team must be aligned and well‑coordinated across all levels, from linear managers to project owners. Moving from a “client‑contractor” model to a unified team structure based on Agile principles is essential. With Agile, business‑side product owners play a crucial role in setting priorities and guiding development toward optimal results, while IT leaders (scrum masters, team leads, etc.) ensure feasibility in terms of architecture and infrastructure.
3. Thorough initial planning: Before embarking on the journey, it is imperative to have a plan. It must include every aspect: architecture decisions, IT tools, budgeting, quality metrics, etc. While smaller companies can easily afford Agile flexibility and iterative prototyping, large corporations would instead need preliminary testing phases to secure stakeholder approvals and measure success.

What hinders Agile in data projects

One of the main challenges in applying Agile methodology to data projects is that some tasks cannot be broken down into short sprints. Especially in large enterprises. For example, data loading into a new system in enterprises may take over a week. Procurement of IT solutions can take three to six months due to corporate approval cycles. Regulatory and documentation approvals are often lengthy and paper‑based in large organizations.

Another challenge is data security. Many sensitive data cannot be transferred to development and testing environments. In traditional software development, this does not pose significant issues, as a large volume of data is not required to create a high‑quality solution. However, in data‑driven projects, the data itself is the product — meaning that even with the most advanced masking algorithms, the quality of the final solution can be significantly compromised.

Ideally, developers (especially external contractors) should not have access to industrial data, financial records, or personal information. However, building effective solutions without understanding the specific data they are meant to process remains challenging. To overcome this issue, specific project roles can be introduced to handle limited‑access data. This, in turn, creates increased information security risks, complexities in access management, and additional infrastructure requirements.

Finally, Agile’s incremental progress tracking doesn’t always apply to data projects. For instance, a monthly report for a large enterprise is either fully complete or not at all — it cannot be “50% ready.” To reconcile this with Agile, companies must adopt a product‑oriented approach, ensuring that transformation goals align with delivering clear, packaged functionalities.

How to make the most of Agile in your data project

To successfully implement data projects within Agile frameworks, companies must adopt a data‑driven approach based on the following:

• Data management infrastructure
  • Understand where data is stored, its quality, and its accessibility.
  • Implement data governance tools and integrate data analysis into overall business processes.
  • Ensure employees (including a data engineer) actively manage and utilize data in their workflow.
• Business intelligence (BI) and decision‑making
  • Deploy BI tools (e.g., QlikView, Tableau) for real‑time dashboards and predictive analytics. Build dedicated data teams to enhance business decision‑making. Improve success metrics (time‑to‑market, processing capacity, speed, or the volume of analyzed data) by transitioning from delayed reporting to real‑time analytics.
  • Implement a top‑down approach, ensuring new data management principles are embedded at all levels of the organization.
• Developing data‑driven products
  • Advanced visualization technologies, such as VR‑based real‑time reporting, can enhance decision‑making.
  • Data‑driven innovations require strong internal leadership, which means the Chief Data Officer (CDO) plays a vital role in overcoming cultural resistance.

What pitfalls you may face

Challenges in data‑driven transformation arise mainly when project roles are misaligned. For example, if the product owner fails to manage priorities, decisions fall to either top executives who lack time for details or IT specialists who lack business insight.

Similarly, if the architect chooses the wrong IT system solution, the team lead mismanages the technical implementation, the infrastructure team provides an incorrect software and hardware setup, or the system analyst makes mistakes in design, the outcome will be the same — delays and compromised quality.

The cost of fixing an error increases exponentially over time. Investing in a preliminary proof‑of‑concept or prototype is far cheaper than launching a full‑scale solution prematurely. If any critical component is overlooked, months of work by dozens of specialists can be wasted, requiring costly rework or a complete project restart.

Additionally, external disruptions can impact data projects. For instance, if a company shifts its product line due to global market changes, it may completely alter data requirements, forcing a 180‑degree pivot in transformation strategy.

To mitigate these risks, project teams need proactive monitoring, clear risk ownership, and continuous adaptability.

In conclusion

A well‑structured Agile approach can enhance data‑driven digital transformation, but it requires balancing flexibility with careful planning and expert execution. It’s essential to remember that IT infrastructure is merely a tool — the key to success lies in understanding what infrastructure is needed, how to configure it, and how to use it efficiently. Without the right talent and expertise, even launching a transformation project is impossible, let alone its efficient execution. Thus, companies must develop key expertise internally, supplementing it with external consultants and contractors where necessary.

At Axellect, we understand that no two data‑driven digital transformation projects are alike. That’s why we take a fully customized approach, offering end‑to‑end solutions tailored to each client’s specific needs. Our team of IT talents specializes in delivering custom development rather than relying on off‑the‑shelf solutions. This flexibility reduces vendor lock‑in, allowing companies to scale their data‑driven transformation at their own pace and budget. By leveraging our deep industry expertise, we help businesses across various sectors — from finance to manufacturing — implement Agile in a way that maximizes efficiency, guiding them through every phase of transformation, from initial planning and architecture design to full deployment and ongoing optimization.