Thursday 23 June 2022

Micropole Luxembourg supports BIL Manage Invest with Data Management

 

Data management is a topic that frequently comes up among business executives in all industries, including the financial services industry. BIL Manage Invest (BMI), the investment fund manager of the Luxembourg-based bank Banque Internationale à Luxembourg (BIL) is one of those actors.

 

Alain Bastin, the CEO of BMI, rightly captured how critical quality of data can be in this area, and how data management is one of the key components that support business growth. BMI partnered with Micropole Luxembourg, a leading company in the field of data management to design and implement the model and tools for managing data. 

 

Why partner with Micropole Luxembourg? 

 

Alain Bastin, CEO and directly supervising IT activities at BMI, describe the reasons why such an initiative is a game-changer:


"For some years now, the industry has seen more and more regulations coming into force, which naturally goes with more reporting to the regulators. It appeared obvious that those activities represent a regulatory risk if data is not accurate, and performing those activities is a pure cost that cannot be reinvoiced to our clients or investors."


"In the meantime, competition is fierce, and making the business grow while controlling the cost is not an option but necessary to continue our activities. This requires a complete review of all processes and workflows and ensuring all of those are scalable with limited modification of the operating setup."


Alain Bastin also mentioned the reasons why BIL Manage Invest decided to work especially with Micropole for its data management project:


"BMI has worked with Micropole in the past with the implementation of a portfolio management system, where Micropole demonstrated its aptitude to listen and its knowledge of our industry."

 

"We also knew that Micropole worked with the BIL group for several years. We naturally contacted them to discuss our challenges with data. We were impressed by Micropole’s expertise and ability to rapidly understand our needs and propose a model with adapted tools that suit our company size, keeping our growth plans in mind."


"It was also important for us to keep the project at human size, meaning that, ideally, we would partner with only one company. Micropole covers the full spectrum of expertise: the business analysis, the data architecture, the design of the data model, the data integration, the front-end, the end reporting with dynamic dashboards and automated reporting, and last but not least, the tools and concepts for us to implement in the long-term robust data governance."

 

The challenges Micropole had to deal with during the data management project at BMI

 

Quentin Pirmez, Director at Micropole Luxembourg, thinks back to some challenges Micropole’s team had to face during the data management project at BMI.

 

Think big, start small


As in any other project, in data management, it is of course important to set ambitious goals. However, having ambitious goals doesn't mean rushing headlong into a gargantuan project with the hope of revolutionizing everything at once.

 

A lot of data projects fail today for that reason and by biting off more than you can chew. However, just because many projects are failures doesn't mean that it is impossible to achieve ambitious goals. There is no «silver bullet» because data management covers many subjects and there is no immediate apparent solution. However, it is possible to discover the solution through careful analysis.


The strategy we adopted at BMI was to first focus on one of the key elements of the value chain, the client. The client is the starting point of many business processes, so agreeing on a clear definition of «what a client is» for BMI was a prerequisite to identifying the relevant information to collect and model throughout his lifecycle. Once the client was clearly defined and modeled, we were ready to move forward on the next topics: the funds and other specific functions (e.g. portfolio management, risk management, compliance, legal, finance, etc.).


Each of those business cases was split into small deliverables (e.g. enterprise data model, data dictionary, dataflow, reporting, etc.) to deliver frequently added value through quality data.

 

A Chinese proverb that could meet the way we approached data management is:

“A Journey of a Thousand Miles Begins with a Single Step.”

 

Understanding business processes help identify the relevant data to collect


While the business processes are the “engines” of companies, data could be considered as “fuel” allowing engines to produce value. The two are intertwined and it is not possible to consider one without the other when you deal with data management. Even with the best “engine”, without “fuel” or with poor quality “fuel” you will go less far and slower than the competition.


At Micropole Luxembourg, we have experience in this type of project with a focus on the fund industry. Thanks to the expertise we have accumulated during our various missions, we were able to quickly understand and analyze BMI's business processes to easily identify the relevant information to be collected, modeled, and integrated.

 

Setting the scene for good data governance practices


Data governance is often neglected because it is considered unnecessary, too abstract, or complicated. The lack of effective data governance guarantees only one thing: the existence of poor data. Without data governance, it is difficult to evolve the enterprise data model over time and ensure good data quality. As previously said, data is the “fuel” of business processes, so poor quality of data also means less effective business processes and less quality of service. 


One of the data governance tools we put in place at BMI is the data dictionary describing the content, format, and structure of the data. This tool allowed us progressively to move into the redaction of individual policies for data quality, access, security, privacy, and usage, as well as roles and responsibilities for implementing those policies and monitoring compliance with them.


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 durability of the model and the quality of the data.

 

Privacy by design


Today, we can’t start a data management project without taking care of data protection and especially by focusing on compliance with data protection regulations (e.g. GDPR).


At BMI we implemented the principle of Privacy by Design from the conception of data processing. For example, all personal identifiable information (pii) is isolated in such a way that it's easy to deal with the rights of individuals like rights to access, right to erase, right to update...


As part of data protection, we also took care of data security by restricting access to data depending on user role level. To this end, we built an access management matrix defining which user role can access certain types of data. It ensured that the data was secure and only accessible as required.

 

Technology must support the business process 


The use of trendy technological tools in a project is common practice. There are as many software programs as there are areas 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 deal with a specific issue.


As a result, it is not easy to find one's way through this jungle. Often, the choice of tool focuses on one specific need and will tackle that need but not necessarily the full value chain. It’s not necessarily bad, but in some cases, it could lead to more time and money spent trying to adapt the software to the problem instead of concentrating on understanding the problem.


The fact is that technology is an excellent lever for dealing with data management issues efficiently, but the choice of such a tool must be made in an enlightened way by taking the time to compare the prices, pros, and cons of each tool. Software without an understanding of the underlying model can't work in the long term. At BMI, we have analyzed the existing tools and recommended the best choice in terms of implementation time, cost, and maintenance.

 

Conclusion


Data management and data governance projects get bad press because they have a reputation for being expensive and the first results are not always tangible. Nevertheless, with BMI we have shown that it is possible to adopt an agile approach to this type of project with concrete deliverables that are quickly usable and gradually improve the execution of certain business processes while controlling costs. 


We always thought about the fuel and made the model around the relevant data and business processes. This allows us to integrate data quality that can be used for accurate reporting for executives and external use. The goal is to continue to leverage data to produce new insights that will increase each business line's productivity in terms of analysis and activity follow-up.


Of course, data is vital for the day-to-day operations of an organization, but it is also an asset in the sense that organizations should invest in it to get future value and stay competitive.


To conclude, Quentin reminds us that: 


"The implementation of a data strategy through this kind of project is only possible thanks to a strong commitment of the Business & IT teams. I would like to take this opportunity to thank the whole BMI’s team for devoting a lot of time and energy to this project."

 

"Also, a special thanks to Nesrine André, BI Consultant at Micropole Luxembourg, and Florian Hognon, Application Software Engineer at BMI, for their hard work on business analysis and development making this project a success."

 

Interested to see what data management can mean for your business?

Contact Quentin Pirmez directly here.