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Utilize your data and extract valuable business insights to make sure your company is making better data decisions and realizing business goals.
After merging two banking systems, each of them keeps maintaining its transactional database for a certain time. But unified reporting is an immediate need, even before two database systems are merged.
The AIDA platform can help to implement unified data access through BI and reporting tools while collecting relevant data from both the original databases systems.
After the two systems are merged, the platform can keep collecting data and start being used as an analytics platform. Over time, additional data sources (e.g. social networks) can be added. It will enable the data science team to start experimenting and developing AI-based models for advanced analytics (e.g. trends detection and prediction).
A single database system is being used for both transactional and analytic tasks. As the system grows, the number of transactions and total data amount increases, leading to a point where analytics and reporting tasks impact the performance of the transactional part.
The AIDA platform can help, in the first phase, offloading the analytic tasks from the transactional database. The historical data from the existing database could be streamed to AIDA and, after catching up, can start receiving new (real-time) data. The analytics and reporting queries can be redirected to AIDA, leaving the original database system for transactional work only.
In the second phase, more advanced analytics and machine learning algorithms can be easily developed on the already existing platform.
The online betting platform serving tens of thousands of players daily
Customer support has its hands full, but, majority of support tickets are
Chatbot successfully responds to ~40% of all queries
Reduced load of human operators
Expand to other markets with ZenDesk integration
No data – how do you make sound predictions based on air?
No expertise, no data team, but, can we match our expertise with their domain
knowledge to create a new solution?
A collaborative PoC project turned feature, added to product Machine-learning
instead of traditional forecasting, worked around the cold start
Big Telco – lots of business users (group accounts)
Clear customer segments + Rule-based churn + Next Best Offer, but, we
cannot track them all!
10% more precise predictions – decrease in campaign costs
Complex behavior of churners picked up by the model, not rules