Implementing Master Data Management in a small company or startup and a large organisation is two completely different things. While in small organisations, a handful of people can take care of the data, in large companies the number and complexity of departments put data at risk of staying siloed, which may lead to great difficulties for extracting value from it. Martin Treder, Information Domain Owner at Boehringer Ingelheim, who has spent more than 15 years working on end-to-end data management and data governance in large organisations, will be sharing some insider tips on Implementing Master Data Governance in Large Complex Organisations at the Data 2030 Summit. Curious to know more about it, we invited Martin to discuss the difference between small and large organisations, the common challenges and his words of advice for successful management of enterprises’ Master Data.
Hyperight: Hello Martin, welcome to the 5th edition of the Data 2030 Summit. To start with, please tell us what has been going on in your professional life since the last time we had the opportunity to see you on stage at the last Data 2020 Summit 2019. Where were you in 2019 and where are you now?
Martin Treder: Since I spoke at Hyperight’s Data 2020 Summit in Stockholm, I have published two management books about handling data in organisations, I have worked as an independent management consultant, and I finally joined Boehringer Ingelheim, one of the world’s 20 biggest pharmaceutical companies.
Hyperight: In the previous edition, you talked about establishing data governance in big corporations. This time, you will be presenting on Implementing Master Data Governance in Large Complex Organisations. What are the biggest differences between large organisations and smaller companies/startups when it comes to master data management and governance?
Martin Treder: In bigger organisations, you simply have more stakeholders to consider. The bigger the organisation, the more difficult it is to bring each manager’s targets in line with the company targets. Explaining to them what is best for the entire organisation is not sufficient. This is particularly true for the management of a shared asset such as data.
Hyperight: What are the challenges that big organisations face with Master Data Management?
Martin Treder: First of all, Master Data is cross-functional by nature. Data about raw materials is relevant for production, research and Workplace Safety. Deep knowledge about a customer is equally important for Sales, Finance and Customer Service. But the idea to align between all of these functions, and possibly between different country organisations within a company, jeopardised each manager’s target to deliver quickly and on budget. That is at least a frequent perception.
Secondly, while managers in smaller organisations tend to rely on the company’s handful of data people to take care of all the data, many departments in big organisations are convinced they don’t need a central data office at all. And, in fact, they can often manage “their” Master Data effectively. But the result is not compatible with other departments’ Master Data, findings are not comparable, and sharing of information is difficult.
Hyperight: One of the key points that attendees will have an opportunity to hear during your presentation is how to successfully orchestrate a corporate Master Data flea circus. Without revealing too much from your talk, could you give us a brief overview of what the Master Data flea circus means?
Martin Treder: Unlike an anthill, data folks in an organisation won’t get self-organised. Without strong governance, harmonisation of data will not happen. Data will stay in silos, being of limited value and even becoming dangerous through ambiguity.
Hyperight: As a last point, what would you advice big organisations just starting out with their master data management? What are the key considerations that they should bear in mind?
Martin Treder: I would always start with systematic stocktaking of current Master Data activities. Hardly any bigger organisation does not do MDM at all. MDM activities can often be found across the organisation, unaware of each other, and set up to meet very specific operational needs.
While trying to understand your current MDM landscape, you would focus on determining good people and good practices. A savvy Head of MDM would not necessarily centralise these people and processes but work towards a federal model: Centralised data governance as part of the strategic management, and local execution as part of local operations. Here, the word “local” stands for different organisational units, such as a specific department, a production site or a country organisation.
At the same time, internal marketing is crucial. Most people don’t see the relevance of MDM to their company’s success. Without the workforce’s broad support, however, MDM will not gain the necessary momentum to help shape the direction of the organisation.
Hear the latest methodologies, strategies and tools used by organizations discussed by the brightest minds in the Data Management community. The Data 2030 Summit focuses on the fundamental pillars of a modern data management strategy: Data Governance & Data Quality, Cloud or Multi-Cloud enabled infrastructure and DataOps (+Master Data Management) wrapped in a three-day extensive programme.