Data management

Data plays an important role in the current digital transformation. Since data and the availability of tooling opportunities to analyse data (for example machine learning, Artificial Intelligence (AI)) is progressively growing, it becomes increasingly more important to have clear governance on data availability and quality of data. This is a necessary condition for making solid investment decisions and our license to operate. Additionally, we use this approach complying with laws and regulations as well as supervisory requirements. Advanced data analytics is also the basis for technological developments and innovation. Lastly, it can ensure efficient business operations by minimising hidden costs from data issues.

There are several projects in PGGM Investments that focus on getting ‘grip on data’. For data availability, the 'data delivery infrastructure' (DDI) project and 'datahub' project play a central role. The DDI provides a PGGM Investments-wide platform with technical resources to support general data governance, access and delivery within PGGM Investments. The datahub (GoldenSource) on the other hand plays a central role in gathering, validating, editing and saving external/market data. Through these initiatives, PGGM Investments aims to capture the full potential of data within the organization.

Datahub DDI

With the datahub we are automizing the validation process of external data. The process is exception based and there are no manual interruptions involved. We can easily manage the data for different purposes based on data-quality and data-ownership requirements.
For example: Static data of securities normally does not change in time, but in some circumstances it does. With the new datahub our whole security-universe is checked for changes on a daily basis. Only static data will be updated if there actually is a change. Also, we can timestamp these changes to match audit requirements.

DDI is a data platform where PGGM Investments data lands for general usage. With this data platform we can add value to data. Datasets that are created in a process can be used in several upstream processes.
For example:

Real estate data is used within Real Estate Front Office, but also used for portfolio monitoring, risk monitoring and client reporting. We created one dataset on this data platform, that can be used easily for all the processes. Also, we are able to implement solid data governance.
The data-owner gives access to users and can see how the data is used, which is automated with standard reports.
Implementation of data-lineage: We can follow the creation of data, where and how data is changed over time.

Datahub Datahub

DDI DDI

Real estate data is used within Real Estate Front Office, but also used for portfolio monitoring, risk monitoring and client reporting. We created one dataset on this data platform, that can be used easily for all the processes. Also, we are able to implement solid data governance.
The data-owner gives access to users and can see how the data is used, which is automated with standard reports.
Implementation of data-lineage: We can follow the creation of data, where and how data is changed over time.

Investment Analytics team

In September 2021 an Investment Analytics team was set up at PGGM Investments. The team will work on the development of investment models and related tooling, in close cooperation with the various investment teams in both Public Markets and Private Markets. Having a dedicated, centralized team within the investment organization to focus on investment modelling allows us to be more efficient in building, maintaining and sharing solutions across all the investment teams.

In the Investment Analytics team, we see investment knowledge meets model building and advanced analytics expertise. The Investment Analytics team will provide an impulse to the use of advanced analytics in investments, because the team will also serve as a centre of expertise for the use of AI and Machine Learning techniques and alternative data embracement in the investment process. These new techniques and data sources offer many opportunities across the various asset classes and will support us in our goal of reaching the best possible investment decisions for our clients, while at the same time improving sustainability.

Over the coming years, we expect the Investment Analytics team to play a central and crucial role in the development of our investment models. The number of possibilities of effectively applying machine learning techniques within investments is growing rapidly and significantly, and we expect this trend to continue in the upcoming years. Moreover, with the huge growth in available classical and alternative data sources, having an effective platform for analysis is a necessary condition to function properly. The Investment Analytics team will help us to embed the usage and knowledge of these new techniques throughout the investment organization.

Data Analysis in Responsible Investment: Michael Kurz

Michael Kurz (32) has been working for PGGM Investments for approximately 1 ½ years now. He is a financial economist, and has a PhD in Finance from Maastricht University. His research focused on empirical asset pricing, banking, and the interconnections between banks and capital markets. Before his PhD, Michael studied in Germany and obtained a MSc in Economics and a BSc in Economics and Business Administration.