Legacy Data Modernisation and Cloud Migration

Helping a data-driven investment firm convert 45 million records stored in 17,000 Excel sheets into a modern cloud database infrastructure, ready for AI/ML integration – achieving over 40x increase in revenue.

Key Services

Hyper-personalisation
Behavioural Data
Consumer Insights
Knowledge Discovery
Gamification
Smart UI/UX

Key Markets

Personalised Nutrition
Personalised Health
Digital Health
Digital Biotech

Objectives

A mid-size investment firm had been using Excel sheets to store and organize their commodities investment data for several years. However, as their business grew and the amount of data they needed to manage increased, it became clear that their current system was no longer efficient. The Excel sheets were difficult to organize, search, and share, and the firm was missing out on opportunities for advanced analysis and insights that could be gained from their data.

To address these issues, the firm decided to migrate their data to a modern cloud database infrastructure. They engaged our team to help them with building and managing large-scale database systems.

Nebuli’s Solution

The first step in the process was to assess the current state of the firm’s data. We worked with the firm to identify which data was most important and how it was being used, as well as any challenges or bottlenecks they were experiencing with their current system and workflows.

Next, we developed a plan for migrating the data to the cloud. This involved designing a new database schema to organize the data in a more logical and efficient way, as well as creating ETL (extract, transform, load) processes to move the data from the Excel sheets to the cloud database.

We applied the following services:

Developed a data strategy that focused on capturing and organizing data in a way that was conducive to machine learning and artificial intelligence. This included designing the database schema to support these types of analyses, as well as identifying which data was most relevant and important for these purposes.
Worked with the firm to define the key business problems they wanted to solve using augmented intelligence frameworks, and developed machine learning models to address these issues.
Ensured that the data was cleaned and preprocessed in a way that allowed the machine learning models to perform effectively. This involved developing ETL processes to transform the data into a suitable format, as well as implementing data quality checks to ensure that the data was accurate and reliable.
Integrated the machine learning models into the firm’s existing systems and processes, enabling them to be used in real-time decision making.
Developed ongoing processes for monitoring and improving the performance of the machine learning models, including implementing techniques such as model retraining and hyperparameter optimisation.
We worked with the firm to define key performance indicators and metrics for measuring the success of the machine learning models, and tracked these over time to ensure that the models were delivering the desired results.

Outcomes

Once the new database was set up, we worked with the firm to integrate it with their existing systems and processes. This included developing APIs (application programming interfaces) to allow different systems to access and update the data as needed, and setting up automated processes to keep the data up to date and accurate.

The results of the migration were significant. The firm was able to access and analyze their data much more quickly and easily, which allowed them to make more informed investment decisions. They were also able to use their data to develop new products and services, which helped to drive revenue growth. In total, the firm saw a over 40x increase in revenue after completing the migration to the cloud.

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