PEOPLE, ORGANIZATION, AND DATA

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.

Work structure and budget organization

It's not enough to invest in a technological and cultural transformation of the company 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 isn't a culture of data-based decision-making 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 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, besides 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)

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.

Assessment

Evaluation and description of the companies' analytical maturity levels using a questionnaire made up of open-end and multiple-choice questions.

Strategy

Definition of the recommended strategies for accelerating the data-driven transformation based on the assessment diagnosis.

Monitoring

Continuous monitoring to map developments, strategies, and bottlenecks.

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:
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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.