How Pon builds local Data Analytics capabilities across the globe through a centrally coordinated approach focused on business impact
Pon is a global diversified trading and service organization active in 9 different industry sectors, ranging from passenger cars, tires and bicycles to power generation and material handling. A common thread across all Pon’s businesses is that they are built on strong brands and operate with a strong amount of autonomy and entrepreneurial spirit. This thread traces back to Pon’s entrepreneurial roots and is also reflected in its core cultural values. For example, Pon employees are ‘trusted to act’ and share a ‘passion to perform’. Pon has yearly revenues of over 6 billion euro and is now operating more than 80 companies across the world.
Facing the future, Pon recognizes that its service and trading businesses will require a major step-up in Data Analytics capabilities to stay ahead of the competition. These capabilities will be increasingly at the heart of a deeper and a more fundamental understanding of (end) customers and markets. New insights can help Pon’s businesses to better anticipate customer needs, identify underserved segments or streamline operations to better align supply chains to markets.
In 2015, Pon designated the more advanced use of Data Analytics as a strategic imperative and an opportunity for major growth. However, to get ahead in this new Data Analytics game, Pon needed to supplement its traditional strengths with a new capability set: one that allows Pon to combine its entrepreneurial culture with hard facts and data-driven decision making. To build much deeper understanding of customers, to capture new market opportunities, to become smarter than the competition and to stay ahead in the game. In the words of David Turner, member of the Executive Board of Pon: “So much of running our businesses is based on our gut. To actually mix our gut with insights from our own data to make better decisions to run our business… that’s a major competitive advantage.”
To set the ambition to grow their Data Analytics at scale is one thing, the question for management was how. First of all, Pon’s headquarters is historically kept lean in line with its strongly decentralized operating model. The corporate core has a select focus on a few value-adding activities, and building a heavy group function is simply not an option. Secondly, the diversified nature of the business would make it all the more difficult to drive impact from a central analytics unit. Uncovering opportunities requires intimate knowledge of the local businesses as well as regular interaction with business counterparts. A central analytics outfit would have to battle constant “not-invented-here” syndromes with the business. As such, Pon’s management realized that capabilities needed to be built locally in the business.
“Our business leaders generally have exceptional commercial instinct, which has been partly the basis of our strong growth record over time. However, I firmly believe we could be even more successful if we more systematically analyze our data to supplement intuition with hard facts.”
Ton van Dijk, CIO Pon Holdings
This however created another set of challenges. How to create buy-in from business leaders across the globe to embrace the analytics imperative? How to ensure that a common way of working and competency level is developed? How to set standards for the use of technology and benefit from economies of scale?
Centrally coordinated capability building program
In order to realize the Data Analytics ambitions in a way that is coherent with the Pon organization, Pon decided to collaborate with MIcompany to launch the Pon Analytical Academy. The Analytical Academy is a one-year program with classes of up to 15 participants from across different business units, regardless of sector or geography. This approach allowed Pon to take central control over the nature and pace in which the analytics capability would be built, while empowering the local units to embed those critical capabilities in the business. Three key design principles were identified to further address the central-local dichotomy.
Firstly, the program aims to develop a standard skillset for participants from across diverse business units. They all learn the same techniques and way of working, and are trained to leverage Pon’s native technology infrastructure. Secondly, there should be a strong stimulus to drive real business impact. Thirdly, the program should drive the creation of a Pon-wide analyst community much like any other critical business function like Finance.
“This course is very special and very unique because we are gathered here, 15 Pon colleagues from different parts of the world, who basically are very alike with similar issues and difficulties.”
Kasper Moller (Participant and Intelligence Manager Supply Chain)
Design Principle 1: Company-wide development of common skill set
The craft of Data Analytics is long and well known to some, but relatively new to many. The ambition and drive of capability building in a global organization bears the risk of spurring fragmentation and diversification in standards and methodologies. Therefore, central orchestration is an important success factor which allows to set a common standard across the group. In the Pon Analytical Academy, all analysts learn the same analytical methods, working principles and terminology. This allows them to set best practice standards from the start, rather than having people figure things out for themselves in different ways.
Besides learning various analytical methods and working principles, Participants are also trained to work on Pon’s native technology for data storage, analysis, modelling and visualization. Not only are these critical skills to learn, this way Pon creates a global standard for data & technology and a user base for the infrastructure which has been erected centrally. For example, the amount of users of the Big Data platform went from 15% to 93% within the group and has aided the push of central data deployment with twice as many business lines having their first datasets in this platform after the program. As such the program helps to bring fragmented data sources of the individual business units ‘online’ for analysis, and simultaneously creates a user base to actually to put it to use.
Another important analyst skill involves the ability to effectively interact with the business along all the required steps of the analytical process. It starts with a proactive and critical attitude – not always an easy feat for many analysts. It is not a trivial habit to double down on the real business question and after thorough analysis presenting the findings in such a manner that actual business actions come as a result. Participants learn these skills through practice in a simulated class room environment, taking up an analytics project in their business, as well as the many business presentations which are integral to the program.
Design Principle 2: Create momentum and a mindset shift through new business insights
The second design principle heralded a bold ambition for the program, which was to create real business impact right from the start. This principle served two equally important goals: to show the critical skills for any data analyst yield an impact, and to create enthusiasm and momentum amongst leadership to embrace the Data Analytics opportunity. Throughout the program, participants worked on real business cases from one of the business units at a time, with actual Pon data. Rather than performing theoretical exercises to learn certain statistical techniques, the class was challenged to put theory into practice and develop actionable advice to the business using the newly acquired technique. Participants were constantly taught to think in terms of business impact, rather than analysis output only. The diverse nature of the group helped in this respect by combining fresh perspectives with deep knowledge of the business under investigation. For example, the group analyzed the rate at which sold cars return to dealers on periodic basis for maintenance and repair. After-sales service is an important revenue and profit driver for Pon Automotive. The team performed cohort analyses on sold cars, following the after-sale revenue streams from the moment of car purchase. As can be seen in figure 1, the percentage of sold cars that return to dealers for service differ substantially by brand. This insight points to a potential opportunity for brands to learn from each other in order to capture more service revenues. Leadership from across the business units was involved in this process, either by proposing business questions for investigation or by attending the class presentations. This allowed the class to develop insights which were highly relevant for the business. Business leaders who attended the class presentations were pleasantly surprised by the tangible output and took away some actionable insights. This resulted in tremendous enthusiasm and momentum around the merit of Data Analytics across the organization.
“We can do the analysis, but we really need to convince people of the opportunities and implications and this program really helps with that.”
Joan Chan (Participant and Analyst Pon Automotive)
Upon completion of the 4 boot camp modules, the class had identified total business growth opportunities in the double-digit millions of revenue (see figure 2).
Working on real business cases proved to be not only relevant to business stakeholders, but also highly stimulating to the participants. Case work becomes more relevant and easier to relate to. Moreover, the prospect of uncovering hidden business opportunities from the data creates a sense of excitement amongst the participants, which in turn works its magic on the learning curve.
Design Principle 3: Sustain momentum through building a connected analyst community
To build on the momentum created by the flywheel of impact, Pon had decided to invest in the creation of a connected analyst community. A more connected analyst community would allow for more flexible career paths, stimulate learning and expertise sharing across business units, and increase the employee retention of this special talent pool. Pon decided to structure the training program in short bursts over time, where people would meet face-to-face. These bursts were translated to various modules, with each module taking one week and combining hard and soft skill training. Each week culminated in the presentation of a real-life business case with new insights to business owners. This created an intensive ‘pressure cooker’ learning environment, where analyst got to know each other much better. It also simulated a real business environment, where outcomes of analytical work really matter to drive business decisions.
To build on the momentum that was created during the Academy, Pon decided to organize regular events for the analyst community such as a yearly analytics conference to facilitate interaction and exchange of best practices. Especially in this strongly diversified company with high local autonomy, management found it essential to have a strong cross-business unit community on analytics in a similar fashion as is often done with other functions like Finance and IT. Investing in the connection of the community paid off immediately. Analysts started sharing data and output files for cross-checks with each other and shared best practices. Some even undertook analytical projects together across different business units. The enthusiasm of solving analytical puzzles together resulted in a participant-lead hackathon to solve a critical question from one of the participating businesses.
All in all, a strong analyst community was built to keep the momentum put in motion through the impact cases and class room trainings. Having a pressure-cooker approach with new learning topics and new insights every training week subsequently pushes the participants to new heights.
Leveraging the Academy program as catalyst for transformation
Different companies have different governance principles and will require different models for building capabilities and organizing Data Analytics. Regardless of the chosen model, a centrally orchestrated approach for capability development is highly recommended. Centrally orchestrating local capability development can bring together the best of multiple worlds: economies of scale, standardization in way of working and close connection to the business. But perhaps most importantly, it is a way to put a transformation into motion towards a more analytically savvy organization without the need for rigorous control – an organic but centrally guided process. Through smartly focusing each module to deliver concrete business insights and impact, the program has moved beyond a set of training modules and has sparked Pon’s transformation to become more data-driven.
Finally, building on this momentum through targeted community initiatives creates an spill-over effect that can encompass the entire business from the bottom-up. Pon has made a major step towards capturing the benefits from Data Analytics while maintaining their entrepreneurial strength and values. There is no central HQ department for Data Analytics, but there is something much more valuable. A group of excited analysts, that work together to help and learn from each other, each to make a difference in the operating unit they work for.