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The Start of My Data Story: The Early Days:

My professional journey in data management has been a fascinating process, shaped by the different business situations I have been a part of. It is a story of continuous learning, which began not in an IT department, but in a marketing team.

Indeed, during the mid-2000s, whilst I was in the fourth year of my industrial engineering degree, I was doing an internship in a marketing department. The company needed me to extract, prepare and process data from their transactional database of supermarkets to analyse the purchasing habits of frequent customers and evaluate their response to various commercial initiatives. The main aim was to find the best offers in order to drive consumption.

A scheduled job created by the IT department regularly transferred data from the transactional system to another database. At that time the marketing department had no automatic way to process or visualise this information. My role involved querying, analysing and extracting data from the database, moving it to Excel, cleaning it, using pivot tables for organisation and finally creating charts. These charts served as reports to provide leaders with a clear view of sales performance and customer response to commercial actions.

These insights supported data-driven marketing by helping to optimise pricing through targeted loyalty card discounts. This rewarded frequent customers for the products they bought most often thus improving retention. Furthermore it allowed us to compare these groups with customers who did not have a loyalty card evaluating the impact of promotions on their future spending. This work was the start of my career in data as I strengthened my skills in Excel and Visual Basic for Applications VBA, a programming tool integrated into Excel, to automate the data preparation process. More importantly it established my understanding of how a marketing department functions as a strategic business unit.

At the same time, I was attending a university course called "Systems Analysis and Design". This course increased my interest in this area, as I learned the basics of understanding a company’s information needs and how to design software to support them. I also learned how to model a database using two approaches: multidimensional and relational, and how to design systems using flowchart diagrams, which are very useful for showing how data moves from one point to another. I was very excited about this area and later became a teaching assistant for the course.

The Same Methods in Different Companies:

In my next roles, the technology I used remained quite similar. At an insurance company, once again as a marketing intern, I worked with transactional, claims (also known as "sinister"), and client databases. These allowed us to extract data and build models to estimate the expected income from different insurance products. We were also able to identify products whose revenue was below target, understand the possible causes and recommend solutions based on financial scenario simulation data.

Not long after I worked full-time at a telecommunications company during the late 2000s and early 2010s. My role was to identify large scale solutions for customer complaints which were a real gold mine of information. We discovered that the main reason for dissatisfaction was the invoice due to broken promises regarding discounts. At that time the Product, Billing and Customer Service departments had different goals and there was no shared objective to reduce these errors.

By analysing the data I understood that the company’s ERP was not prepared for the complexity of new offers and manual registration caused constant mistakes. Since fixing the system at the root was not feasible we decided to act at a later management layer. We requested the product catalogues in Excel and through a Visual Basic program I developed we cross referenced that information with sales and complaints. This tool allowed the service team to see the catalogue and the invoice on a single screen to fix errors quickly. The final step was proactivity: we began identifying errors before they reached the customer. This innovation later integrated into the company intranet reduced complaints to a meaningful extent and transformed the customer experience which earned me an award on behalf of the team.

More information about this experience in the post "Predicting customer complaints".

After gaining this experience I felt it was time to grow professionally and decided to move to Belgium to pursue an MBA. My goal was to deepen my knowledge in areas such as team management, operations, finance and marketing. This training radically changed my perspective. Through the Corporate IT Strategy course I stopped seeing technical solutions as simple support tools and began to understand how business strategy can leverage technology to create market disruption. We studied cases of success and failure that proved technology is inseparable from business execution. In the mid 2010s cloud computing was the new frontier and artificial intelligence was already emerging as a technology with immense transformative potential although its widespread adoption was still in the early stages.

Moving to Spain and a Big Step in Automation:

After completing my MBA and developing some projects in Lima, including a role in demand planning, I moved to Spain in the mid 2010s. My first full time job was at a publishing company and it felt quite familiar compared to my previous experience. Although the company analysed sales using a transactional system, my role focused on optimising supply chain management through data. At the beginning I used familiar tools such as Excel to process information, extract business insights, create dashboards and identify opportunities to improve the supply chain. I also developed predictive models to anticipate demand.

However the volume of manual tasks required to support logistical control processes was significant meaning the team spent a large part of their time on data processing and refinement. The IT department focused only on supporting transactional data but did not provide transformed or cross referenced information between sales data and the logistical KPIs needed to ensure customers received their products when they needed them. This information gap made it difficult to meet customer expectations and limited our ability to respond to problems within the supply chain.

With the support of my team I led the automation of these data processes extracting data from the transactional system and moving it into a normalised database. For instance I learned to use AutoIt to implement robotic data extraction because the company lacked direct extraction processes. Similarly we had to process unstructured data such as delivery notes and invoices creating automatic workflows to identify key information like effective delivery dates. This allowed us to anticipate errors through programmed alerts and improve management precision.

Despite these automations the company remained limited by an ERP designed for a much simpler logistical era. Once again I found myself in a similar role to my previous experience in telecommunications making a later management layer viable instead of fixing the source. Although imperfect, this solution prevented the company from losing control and provided the certainty needed to understand that updating the core system was essential to remain competitive.

My next role, in the advertising industry during the late 2010s and early 2020s, marked a turning point in my career. I spent the first two years understanding media audit calculations and automating these complex processes using Excel and VBA. Subsequently, I collaborated with the global IT team to migrate these calculations into the company’s cloud architecture. Initially, the cloud work shared similarities with my previous experience, as the logic remained based on data extraction, transformation, and loading, but now performed within a cloud infrastructure. The common ground was the methodology: we applied SQL and standards to structure data architectures within the data management team before transferring them to IT, specifically transforming the data to optimise its subsequent use.

Once the project was established, I began collaborating on initiatives for other departments and agencies. I used the same cloud development framework, while incorporating nuances and exceptions to optimise data for internal applications, dashboards, and file exports. My role shifted from defining what to develop to executing projects based on the needs identified by business stakeholders. I evolved into a role focused on supervision, control of solutions, and their integration with other applications. This was the exact role I had been preparing for years prior, albeit with a higher degree of organisational maturity, making this my most integrative experience in data management.

The Future of Data: New Challenges:

As I have gained experience, the challenges have become increasingly complex. We now face constant architectural migrations, the need to balance standardised projects with more experimental ones featuring different data structures, and the requirement to be highly reactive to the business's evolving direction. These factors, combined with internal reorganisations and the emergence of new architectural elements like generative AI, new data providers, and shifts in corporate strategy, must all be managed under security standards and cloud cost controls.

A Look to the Future:

From my early days with VBA, Access, and SQL Server to the cloud infrastructure of today, these years have been an exercise in constant growth and consolidation. Through the various corporate environments I have been part of, I have evolved my understanding and approach to software, aligning myself with the state of the art in IT. I am very interested to see what the future holds with the growth of the cloud and the use of generative AI, but I am convinced that the best approach to this technological shift is to keep learning, keep moving, keep adapting, and never stop.