The launch of the Data Lake in Hungary and Romania in 2018 marked the first milestone of the digital transformation journey for MOL Group’s Consumer Services Business Unit. Fast forward to 2025, the Data Lake now covers 8 markets, serves +1,500 internal business colleagues with extensive Business Intelligence solutions, and has transformed key business processes in sales, marketing and retail category management through advanced analytics and AI/ML tools. This transformation has delivered measurable business benefits worth millions of US dollars, growing exponentially each year.
However, achieving true business adoption and integration of advanced Business Analytics solutions has been a complex and challenging journey. As we expanded the market and systems coverage of the Data Lake, building on the billions of granular data points, we started to pilot a number of custom built analytics and AI/ML solutions in the business. The valuable lessons we learned from both our successes and failures, have been integrated into our Digital Factory data organization and analytics portfolio strategy, as well as our way of working with business partners and clients. We believe sharing our lessons can benefit those who lead the transformation of businesses and cultures with data, analytics, and AI.
One of our most important lessons learned is that we can do much more with our data, technical capabilities, and talented team than the business can absorb at once. It takes time and a joint learning curve collaboratively with the business teams to progress through the analytics maturity stages (see Sidebar). Advancing in analytics maturity is essential for creating lasting business impact and fostering strong, long term partnerships. The higher an analytics solution (and business organization) is in analytics maturity, the higher the monetary value that can be unlocked, using advanced models, AI, and machine learning solutions.
It’s not a surprise that it’s tempting for data teams and data scientists to jump early into developing advanced models and apply AI and Machine Learning techniques. This is not only exciting from a professional and technical perspective, but, given the granularity, scale, and automation such solutions can provide, their business-building potential is also much higher in the longer term, when compared to the impact of dashboards, reports, or one-off deep-dive business analyses.
However, it’s not smart to move too early to such advanced solutions. As shown by our own example (Figure 1), most of our relatively early trials with bringing advanced analytics and AI solutions to the business failed. That’s because the business partners and decision-makers, who were meant to action the outcome of such complex and oftentimes black-box models, were not ready to accept these advanced tools. The lack of understanding and trust in data is a key barrier to adopting advanced analytics models in business processes and decision-making.
This is why the second step of analytics maturity, the diagnostics stage, is so utmost important and one cannot skip in their analytics maturity journey. The essential role of diagnostics is to build a common, data-based understanding of the key metrics and the key business drivers, and their impact on business outcomes. This is done through conducting deep-dive business analyses and building intuitive and easy-to-understand analytics tools, focused on key business challenges and opportunities, that lead to formulating actionable, data-based insights, narratives and recommendations.
As a result, the broader business organization gradually becomes comfortable and gains trust in the organization’s own data, and most importantly: experiences how the data effectively can be used to explain business trends, uncover untapped opportunities, and turn insights into actions for business growth. A business-oriented data organization, with strong business domain understanding, possessing a strong mix of technical and consulting skills is essential to do this right.
As a result of refocusing our data team’s effort on diagnostics solutions, we managed to build a common understanding of the data and metrics that matter for the business, and developed custom analytics tools that transformed business processes and delivered measurable growth. Over time, this built the necessary data acumen and trust in the data, and the business naturally became open to adopting more advanced analytics solutions – as illustrated by the successful implementation of the predictive and prescriptive tools once we created the strong foundations with the diagnostics stage.
At Digital Factory, we develop and nurture a portfolio of custom built analytics tools to address various business opportunities for MOL Consumer Services Retail business and the MOL Move loyalty app (Figure 2). Our strategy involves deploying analytics tools MVPs at the lowest possible level of analytics maturity that generate immediate business impact, scaling the tools across markets to quickly harness their potential, and then evolving the analytics maturity by deploying advanced AI/ML features, thereby enhancing value delivery. By now, we operate a mix of custom built advanced analytics tools across 8 markets, ranging from sales performance management through gastro products optimization to fully automated hyper-personalization and campaign measurement.
This approach has led to exponential growth in the monetary impact of Digital Factory’s analytics solutions for MOL Consumer Services over the past years. Our journey continues, and we invite anyone interested in accelerating their analytics maturity journey to get in touch with Digital Factory’s Data Analytics hub.
