Data: The Heart of a Digital Transformation Strategy
The onset of the COVID-19 pandemic severely impacted the Spanish economy, leading to a marked decline in activity, particularly in sectors most affected by lockdowns and reduced mobility.
Spain’s productive fabric is predominantly made up of SMEs, which, by their nature, have a lower degree of digitalization compared to large companies. This scenario placed them at a clear disadvantage in a context where higher digital penetration was key to competitiveness.
The need for rapid and profound digital transformation is pressing. The 2021-2025 Plan to Boost the Digitalization of SMEs involves a set of public initiatives aimed at promoting the adoption of new technologies and the digitalization of businesses. This project aligns with the Recovery, Transformation, and Resilience Plan, which anticipates that, over the next three years, Spain will receive €140 billion from the EU as part of the Next Generation EU stimulus package. According to forecasts, around 30% of these funds will be allocated to digital transformation.
The opportunity to advance and consolidate the great promise of digitalization is unique. However, digital transformation remains an aspirational goal that requires concrete action: How to start? How to tackle a process of this nature?
To begin, I want to discuss “data,” which is the heart of such a transformation. More specifically, I want to refer to “Data Science” projects, as they are closely aligned with business objectives. This is because undertaking a data science process involves managing data in a way that allows for dynamic decisions that benefit businesses. It encompasses an interdisciplinary field involving scientific methods, processes, and systems that extract knowledge from data to analyze the present, predict the future, and make timely business decisions.
A data science project follows a process that can be summarized in 6 steps:
- Problem Definition: Translate the business problem and identify data sources. Clearly define the problem you want to solve by asking: What is my main goal? What business problem do I have? What do I want to explain using data?
- Data Preparation: Select useful data and extract it from sources. A central question here is: How much customer history do I have stored? Who owns the data?
- Data Exploration: Clean and transform the data. Analyze variables to understand their behavior and relationships. A data-driven culture requires systematic decision-making based on a “data cult.” It is crucial to have a team oriented toward this goal.
- Model Creation: Build and train the model. Once the model is constructed, it is possible to predict reality based on any available information. Machine Learning is the major revolution in these processes: the use of computer algorithms allows models to automatically learn from experience.
- Validation and Testing: Adjust parameters and evaluate the model through trial and error.
- Visualization: Present the data using appropriate visual tools.
Certainly, there is no better time to drive a data science process than now.
The described process is only an initial approach to a process that involves investing time and dedication to understand its steps and implications.
Future discussions will delve into each of these phases, exploring their scope and demands.
For now, it is important to be clear about the core of this type of project. The biggest challenge is not obtaining data but deriving meaning from it. A capable team and a systematic analysis are key to ensuring that this process allows the business to make the best decisions and achieve higher and better levels of competitiveness.
Julio Cesar Blanco – March 22, 2022
