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Design and implementation of the model and tools necessary for data management.
Data management is a topic that frequently comes up among business leaders across all sectors, including financial services. BIL Manage Invest (BMI), the investment fund manager of Banque Internationale à Luxembourg (BIL) bank, is one of these players.
Context
Alain Bastin, BMI’s CEO, has clearly understood how critical data quality can be in this field of activity and that data management therefore represents one of the key elements supporting business growth. BMI has partnered with Micropole Luxembourg, a leader in the field of data management, to design and implement the model and tools necessary for managing its data.
Alain Bastin, CEO and head of direct supervision of IT activities at BMI, explains the reasons why such an initiative is a game-changer:
In recent years, the sector has seen more and more regulations come into effect, resulting in an increase in the number of reports intended for regulatory authorities. It has become evident that these activities represent a regulatory risk if the data is not correct, and accomplishing these activities represents a pure cost that cannot be passed on to our clients or our investors.
Meanwhile, competition has become fierce and growing the business while controlling costs is not an option, but a necessity for the continuation of our activities. This requires a comprehensive review of all processes and workflows and ensuring that they are all scalable with limited modification to the operational configuration.
Why Micropole?
Alain Bastin also mentioned the reasons why BIL Manage Invest decided to collaborate specifically with Micropole for its data management project:
In the past, BMI had already worked with Micropole on the implementation of a portfolio management system and on that occasion, Micropole was able to demonstrate its listening skills and its knowledge of our sector.
We also knew that Micropole had been working with the BIL group for several years. We naturally contacted them to discuss our data challenges. We were impressed by Micropole's expertise and its ability to quickly understand our needs and propose a model with tools adapted to the size of our company, while taking into account our growth projects.
It was also important for us to keep the project human-sized, that is, ideally, to collaborate with a single company. Micropole has the full spectrum of necessary expertise: business analysis, data architecture, data model design, data integration, front-end, final reporting with dynamic dashboards and automated reports, not to mention the tools and concepts that allow us to implement robust long-term data governance.
Challenges
The challenges that Micropole had to face during the data management project at BMI
Quentin Pirmez, Director at Micropole Luxembourg, recalls some of the challenges that the Micropole team had to face during the data management project at BMI.
1. See it big but start small
As in any other project, when it comes to data management, it is obviously important to set ambitious goals. But having ambitious goals does not mean diving headfirst into a titanic project, hoping to revolutionize everything at once.
Today, many data management projects fail for this reason and because people bite off more than they can chew. But just because many projects end in failure doesn’t mean it’s impossible to achieve ambitious goals. There is no “panacea” because data management covers many areas and there is no immediate apparent solution. However, it is possible to find the solution through careful analysis.
The strategy we adopted at BMI was to focus first on one of the essential elements of the value chain: the client. Since the client is the starting point of many operational processes, agreeing on a clear definition of “what is a client” for BMI was an essential condition for identifying the relevant information to collect and model throughout its lifecycle. As soon as the client was clearly defined and modeled, we were ready to move on to the following elements: funds and other specific functions (such as portfolio management, risk management, compliance, legal, financial aspects, etc.).
Each of these business cases was broken down into small deliverable elements (such as the enterprise data model, data dictionary, data flow, reporting, etc.) in order to provide added value through quality data.
This Chinese proverb illustrates well how we approached data management: “A journey of a thousand kilometers always begins with a first step.”
2. UNDERSTANDING BUSINESS PROCESSES HELPS IDENTIFY RELEVANT DATA TO COLLECT
While business processes are the “engines” of companies, data could be considered as the “fuel” that allows engines to produce value. The two are closely linked and it is impossible to consider one without the other when it comes to data management. Even with the best “engine,” without “fuel” or with poor quality “fuel,” you will go less far and less fast than the competition.
At Micropole Luxembourg, we have experience with this type of project, particularly in the fund sector. Thanks to the expertise we have acquired during our various assignments, we were able to quickly understand and analyze BMI’s operational processes in order to easily identify the relevant information to collect, model and integrate.
Project
3. Preparing the ground for good data governance practices
Data governance is often neglected because it is considered useless, too abstract or complicated. A lack of effective data governance guarantees only one thing: the existence of poor quality data. Without data governance, it is difficult to evolve the enterprise data model over time and guarantee good data quality. As we said previously, given that data is the “fuel” of business processes, poor data quality will also result in less efficient operational processes and lower service quality.
One of the data governance tools we implemented at BMI is the data dictionary which describes the content, format and structure of data. This tool allowed us to gradually move toward writing individual policies on data quality, access, security, confidentiality and use, as well as defining roles and responsibilities for implementing these policies and monitoring their compliance.
Setting up this type of tool may seem like a waste of time at the beginning of the project, but we quickly realized that without these tools, it is very complicated to ensure the sustainability of the model and data quality.
4. “Privacy by design”
Today, it is impossible to start a data management project without being concerned about data protection and more particularly without guaranteeing compliance with data protection regulations (such as GDPR).
At BMI, we implemented the “Privacy by Design” principle from the design of data processing. For example, all personally identifiable information (PII) is isolated so that it is easy to manage individual rights such as access rights, the right to erasure, the right to data updates…
As part of data protection, we also ensured data security by limiting access to data based on the user’s role level. To do this, we built an access management matrix that defines which user role can access certain types of data. This matrix ensures that data is secure and only accessible when needed.
5. Technology must support the business process
The use of cutting-edge technological tools in a project is common practice. There are as many software solutions as there are domains in data management (tools for data governance, data modeling, data integration, data storage, data quality, data reporting…). Some tools cover several areas of data management, while others only address a specific aspect.
Therefore, it is not easy to find your way through this jungle. Often, the choice of tool focuses on a specific need and will address that need, but not necessarily the entire value chain. This is not necessarily a bad thing, but in some cases, you will then have to spend more time and money trying to adapt the software to the problem instead of focusing on understanding the problem.
The fact is that technology is an excellent lever for effectively addressing data management problems, but the choice of such a tool must be made in an informed manner, taking the time to compare prices, advantages and disadvantages of each tool. Software without an understanding of the underlying model cannot work long-term. At BMI, we analyzed existing tools and recommended the best choice in terms of implementation timeline, cost and maintenance.
Conclusion
Data management and governance projects have a bad reputation because they are known to be costly and the initial results are not always tangible. With BMI, we nevertheless showed that it is possible to adopt an agile approach for this type of project with concrete deliverables that are quickly usable and progressively improve the execution of certain business processes while controlling costs.
We always thought about the fuel and designed the model around relevant data and business processes. This allows us to integrate quality data that can be used to establish accurate reports for management and the outside world. The goal is to continue exploiting data to produce new knowledge that will increase the productivity of each business branch in terms of analysis and activity monitoring.
It is obvious that data is vital for an organization’s daily operations, but it also constitutes an asset in the sense that organizations must invest in it to create value and remain competitive.
To conclude the story Quentin Pirmez reminds us of the importance of the people working behind the scenes:
The implementation of a data strategy through this type of project is only possible thanks to strong mobilization of the Business & IT teams.
I would like to take this opportunity to thank the entire BMI team who dedicated a lot of time and energy to this project.
And a special thanks to Nesrine André, BI Consultant at Micropole Luxembourg, and to Florian Hognon, Application Software Engineer at BMI, for their diligent work on business analysis and development, which made this project a success.
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