Databricks

Offering you the opportunity to scale AI, we can set up cloud and modern analytics tools to scale the use of data science and AI throughout your business. Drive change to enhance customer and employee experience with a robust data system. Move your proof of concepts into production faster through expert Databricks consulting.

Through initial discovery workshops and implementation planning, we can get you on board started on the right path with Databricks.

Our advanced Proof-of-Concepts help you to evaluate the perks Databricks would bring to your business.

We tailor our strategies to your specific project requirements to ensure your Databricks implementation starts on the right track.

Reduce the implementation time to add value with Databricks quicker. By efficiently addressing your current data challenge and accelerating your data engineering processes, we effectively help you reduce the time and resources you require to implement.

Building ML models and moving them into production is hard to achieve. Databricks takes away some of the burden by streamlining the ML lifecycle from data preparation to model training and deployment at scale.

Business problem

Missing data, or data that is “not in good order” and needs to be corrected before processing, leads to claims, leakage, and inefficient processes in triaging claims to the right resource.

Solution

Enable triaging of claims and resources by leveraging big data processing and integrated ML and AI capabilities, including MLflow model lifecycle management.

Business Outcomes and Benefits

• Decrease in annual claims payout
• Increase in claim fraud detection/revention
• Improve efficiencies by 15%

Business problem

Actuaries are spending valuable time on low-value activities, which hampers agility and advanced analytical capabilities in pricing and underwriting, hindering improvements in risk and pricing modeling.

Solution

• Unified cloud-native platform
• Scalability for ingesting IoT data from millions of trips, expanding the customer base
• Reduced total cost of ownership compared to legacy Hadoop systems
• Usage-based pricing, leading to lower premiums for customers and reduced risk for insurance carriers, thereby lowering loss ratios
• Enables the creation of a digitally enabled, end-to-end underwriting experience.

Business Outcomes and Benefits

• Improve competitive position
• Decrease combined ratio
• 15% improvement in efficiencies

Business problem

Insurers need the ability to identify fraudulent activity and respond to new suspicious trends in near real-time.

Solution

Modernized approaches in insurance require full digital transformation, including the adoption of usagebased pricing to reduce premiums. Insurance providers now consume data from the largest mobile telematics providers (e.g., CMT) to obtain granular sensor and trip summaries for users of online insurance applications. This data is crucial not only for pricing but also for underwriting scenarios to mitigate risks for carriers.

Business problem

The inability to reconcile customer records across different lines of business limits real-time customer insights necessary for upselling and cross-selling. Siloed data makes it challenging to create accurate and comprehensive customer profiles, resulting in suboptimal recommendations for the next best action.

Solution

Databricks provides the tools needed to process large volumes of data and determine the next best action at any point in the customer journey.

• Eliminates data silos by unifying all customer data, including basic information, transactional data, online behavior/purchase history, etc., to create complete customer profiles
• Integrated data security ensures that security measures are incorporated at every layer of the Databricks Lakehouse Platform
• Delta improves data quality, providing a single source of truth for real-time streams and ensuring reliable and high-quality data for data teams
• Integrated ML and AI capabilities utilize AI to create self-optimizing ML models that determine the next best step for each customer
• MLflow model lifecycle management helps manage the entire machine learning lifecycle reliably, securely and at scale

Business Outcomes and Benefits

• Use AI, ML, automation and real-time data to gain deeper customer insights and understand their needs
• Improve competitive positioning
• Enhance the customer experience