You can use Digazu for very different projects, it's not just about Machine Learning or Artificial Intelligence. Digazu shines if you have a lot of data pipelines, but some of our users are solely using it for its data lake integration, or others to enable real-time reporting.
Digitisation leads financial services to innovative solutions. But highly sensitive services must be secured to prevent criminal intrusions. Machine learning shines in that field, analysing big chunks of transactions and detecting potentially fraudulent behaviour.
The main challenge in fraud detection use cases remains the ability to combine high-velocity data from the payments, with data coming from operational systems such as user profiles, history of payments...
By using Digazu you will significantly reduce the time to implement all the necessary steps needed for this task: collecting data, building data pipelines, developing your fraud detection model and deploying it as a scalable real-time microservice. In an all-in-one secured and controlled environment.
In order to transform data into actionable insights, you typically need to combine data coming from many sources, analyse the past, predict the future, and then make sure that the new insights are shared with all interested stakeholders in due time. Digazu naturally offers all functionalities needed in this process, from data collection, historization, combination, analysis and distribution.
AI-powered anomaly detection models lead to faster and better decisions. But the challenge is not specifically to build the model, it is how you deploy this model in production, and how you feed this model with data. When dealing with different data sources generating high volumes of data, performance is always an issue. Digazu is designed to address that. We simplify the process of connecting your streams and models, and we deliver a blazing fast access to your data flow thanks to our very efficient way of caching the transformed data.
Customer segmentation is crucial for many industries. It draws on complex differentiators to divide customers into different segments that underpin personalized marketing campaigns, product and service choices, and price setting. With Digazu you can apply machine learning to customer behavioral and demographic data to generate accurate segmentation in real-time, helping each business to make the most profitable decisions for its audience and users.
Many solutions exist in this field. But when your project is a bit too advanced or too custom you will lose a lot of time to find workarounds. Digazu is the ideal missing piece to connect all your streams in a unified pipeline to then distribute to your favourite analytics platform.
Aggregating hot and cold data is a big technical challenge. Very efficient tools exist such as Flink or Spark, but building a robust solution with these tools from scratch is time-consuming, risky and requires a lot of expertise.
Digazu is the perfect solution for these challenges. We help you unify your data, in a fast, predictable and reliable way.
There are many pitfalls on the way to implement a data lake properly:
- How to collect data from heterogeneous sources?
- How to uniformize data structures?
- How to manage data history and schema changes?
- How to make sure that your data lake will scale with your future needs?
With Digazu, all these problems are solved using the best-in-class design. You can benefit from data in your data lake in just a few days.
It is always a nightmare to make data from your legacy systems available and to combine it with data from your new tech infrastructure, to build integrated dashboards, data science models or 360° views. Digazu facilitates the data engineering needed to build a one-stop-shop for data, abstracting the technology of the data source systems. This can even be the starting point of a migration allowing you to finally deprecate your old legacy systems.
Having a clear and exhaustive view on your data flows is a must for productivity and efficiency, but also for regulations such as GDPR. With Digazu, you have one central place to manage your data flows, making it simple and easy to know which data is used by whom and for which purpose.
Everybody claims they have a scalable solution. But our approach is based on our years of experience on data-centric architectures and on high volume data science projects. It is the core strength of Digazu: be able to scale regardless of the amount of pipelines and the size of these ones.