Learn how to align people, organization and data to drive innovation and sustainable growth in your company.

How to succeed in data driven transformation?

From digital to data driven

Many companies are still far from reaching the level of maturity of the so-called digital leaders, those companies that differentiate themselves in the market through the intensive use of digital technologies and business strategies that are only possible in the digital world.

In this context, it is already possible to coin a new term that more accurately reflects the ultimate goal of the digitalization of companies: data driven transformation. This concept refers to the continuous process of transforming data into business assets to generate innovation, improve processes and optimize costs, helping companies to secure a permanent competitive advantage in the market.

The main difficulties in data driven transformation

This is a multidimensional and complex challenge that involves people, processes, technology and demands a strategic vision and a flexible organizational structure capable of meeting a scalable, intelligent and inclusive long-term data strategy. Check it out below:

Misaligned strategy

It seems obvious that poor quality data or data that is difficult to access are major difficulties in achieving data driven transformation. However, our opinion is that these are minor challenges compared to the lack of strategy we see in most data and AI investment initiatives.

Stakeholder management

It is necessary to manage the interests of different stakeholders in the organization so that everyone is aligned on the strategic importance of this initiative.

Organization of the work structure and budget

It's not enough to invest in a technological and cultural transformation of the organization if the work structure doesn't reflect modern forms of team organization and budget management.

Building a data driven culture and data literacy

It is now possible to use external training platforms without having to invest heavily in the production of specialized content.

It should be stressed that investments in training and the dissemination of knowledge will not produce results if there is no data-based decision-making culture in the organization. That's why it's important to encourage top management to build indicators, use scorecards, OKRs and other data-based management techniques whenever possible.

Choosing the right data architecture

Companies need quality data to evolve in the data driven transformation, but this is not the reality for most organizations.

In many cases, their data is stored in an unstructured and disorganized way in different systems spread across different business departments, forming so-called data silos.

The low quality of this information adds more uncertainties and complexities to the technological progress and advancement of companies in the data driven transformation, including:

  • The increase in data silos.
  • The need for integration between systems.
  • Security and governance risks.
  • Increased storage capacity.

The data driven transformation cycle

The data driven transformation cycle begins with a strategic vision aligned with the massive use of digital technologies and data.

Next, the core competencies of organization and work processes, people and culture, and the data platform are developed.

Finally, the use cases are materialized through the construction of data products that follow a continuous learning cycle, with agile methodology and data-based experimentation. Only then will the true value of data driven transformation be realized by companies.

We can also convert this cycle into the "data driven transformation equation":

Vision x (Build + Measure + Innovate) x Products = Value

(People + Organization + Data)

Why use POD?

Methodology based on 3 pillars


Developing the necessary skills and defining the ideal team structure for building a lasting and scalable data culture. This pillar centralizes people in the strategy.


Develop an appropriate organizational model and make the necessary structural changes to promote and sustain a data driven culture over the long term.

This pillar includes the role of leadership, cultural strategy and education to ensure that the organization's people, processes and tools are aligned with this mission.


Implementing a robust data infrastructure with well-defined governance parameters and processes to meet the demands and needs of the data culture. These are key components of analytical maturity.

This pillar assesses the mastery of governance processes, data infrastructure, platform and data products, including the technologies, methodologies and processes used.

5 levels of maturity

As they evolve through these stages, organizations must extract more value from data and generate more return on their investments. The POD determines five levels of analytical maturity:

Level 0: Operational

Data is collected by operational applications such as ERPs and CRMs. There is no analysis process.

Level 1: Initial

There are measured indicators and metrics reported in isolation.

Level 2: Managed

Data initiatives are underway. Analytical maturity is a priority objective for the company.

Level 3: Optimized

There are governance processes and data platforms in place. There is a roadmap for improvement and evolution of analytical maturity.

Level 4: State of the Art

The organization is at the frontier of processes, culture and data technology.

Level 5: Visionary

There are innovations and initiatives that challenge the frontiers of knowledge.

Practical methodology

Analytical maturity models can be comparative, descriptive or prescriptive in nature. POD combines them all in a holistic methodology, which consists of three stages: assessment, strategy and monitoring.


Evaluation and description of the companies' analytical maturity levels using a questionnaire made up of open and closed questions.


Definition of the recommended strategies for accelerating the datadriven transformation based on the assessment diagnosis.


Continuous monitoring to map developments, strategies and bottlenecks.

Adapted analytical maturity model

A company's analytical maturity levels depend not only on the will to make it happen, but also on economic and cultural infrastructure.

Based on the analysis of different maturity models, we can identify some problems and trends that are solved by POD:

There is a lack of in-depth holistic solutions and formalized models
Most documents and MMAs are available on blogs and whitepapers, creating inconsistency and a lack of depth in the topics.

Too much theory and not enough practice
The analytical maturity models do not provide enough information on how to apply them in practice.

Non-standardized tools and technologies
There are few standardized tools or methodologies, descriptions of initiatives or action points, making practical application difficult.

Focus on larger companies
The current models fail to bring holistic and democratic approaches to companies of all sizes.

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