Building Data Lake and Reporting Platform for a Wealth Management Company

Background:
Our client is a national financial advisory firm with nearly 40 years of experience based out of the USA. They provide integrated services including financial planning, investment management, tax, estate, and insurance, all managed by a single team. They offer clients a unified in-house team, a hand-picked advisor, an institutionalized-grade portfolio, and an independent national fiduciary legally obligated to always act in their best interest.

Objectives:
The project leveraged Azure platform powered by Synapse analytics to accomplish these objectives, aiming to:

• Migrate existing data and workloads to Azure Synapse Analytics
• Enable advanced analytics capabilities (real-time data ingestion, data warehousing, big data processing)
• Improve data management, integration, and processing efficiency
• Enhance scalability, security, and performance of data analytics operations

Challenges:
The project faced several significant challenges that impact the overall workflow and data integration process. One of the key issues is accessibility; there are frequent difficulties in accessing critical systems, such as Salesforce, and a reliance on their personnel for data validation, particularly for sources like Salentica, which further complicates the process

Another hurdle is the dynamic nature of data sources. The data schemas are constantly changing, with daily structural updates creating added complexity for data integration. This makes it increasingly difficult to maintain consistency and accuracy across systems.

The creation of data pipelines has also proven challenging due to a lack of domain knowledge. This gap in understanding has hindered the establishment of effective pipelines between disparate data sources, limiting the flow and accessibility of necessary information.

In addition, there are ongoing data quality issues that need attention. The presence of null values, special characters, and other inconsistencies in the data disrupts the reliability and accuracy of reporting, making it harder to trust the datasets for decision-making.

Compounding these technical issues is the stakeholder complexity. The project involves a large number of stakeholders, each with varied requirements. Managing their expectations and effectively prioritizing tasks has become difficult, leading to potential misalignments and delays.

On top of these operational challenges, there are issues related to data governance. Tracking the inventory of published reports has proven to be difficult, and ensuring proper data governance across multiple departments requires additional oversight and coordination.

Finally, the implementation complexities are significant. There is insufficient information on how to implement linked services for data sources with extensive and complex data. Additionally, creating parent datasets for each data source or department is a prerequisite for beginning any meaningful reporting activities, which adds further layers of complexity and delay.

These challenges collectively impact the project’s efficiency, accuracy, and ability to meet stakeholders’ needs, necessitating focused efforts to address each of these issues systematically.

Solution:
The project involved a comprehensive approach to resolving several data integration and reporting challenges. Initially, access and integration issues were addressed by collaborating with the Salesforce team to obtain necessary credentials and configure access, ensuring full team participation. A well-defined data load strategy was established, differentiating between full and incremental loads, with separate pipelines created to handle the volume and complexity of data.

To manage the dynamic nature of the data, custom scripts and pipelines were developed to adapt to daily schema changes. Continuous stakeholder engagement was prioritized, using data model documentation to gain domain expertise and maintain clear communication. For data mapping and relationships, ER diagrams were created to clarify relationships between data source tables, ensuring accurate reporting.

A strong focus on data quality management led to the implementation of data profiling, the creation of a data dictionary, and the generation of detailed reports on data quality metrics. Automation and monitoring were enhanced by developing automated scripts for quickly retrieving published Power BI reports and integrating alert mechanisms for pipeline failures. Additionally, error handling and auditing mechanisms were established to ensure data integrity by comparing source and destination data and logging any pipeline failures.

To optimize Power BI usage, workshops were conducted across departments, templates for proper reporting structures were developed, and write-back functionality was implemented using Power Automate. Data flow optimization efforts included creating a comprehensive DataMart and reducing the time required to create parent datasets.

Technology:

Impact:
Implementing the Azure Data Lake Platform for our client resulted in a transformational upgrade to their data management and reporting capabilities. By addressing complex challenges and leveraging advanced technologies, the project successfully centralized data across the firm, improved data insight generation, streamlined data management, enhanced governance, and enabled advanced analytics capabilities. The solution’s scalability and efficiency improvements positions our client for continued growth and data-driven decision-making in the future.

About Neutrino Advisory

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