15% Monthly cost reduction with a Modern Security Platform Transformation along with Machine Learning

Enabled real-time health analytics, processing data queries in minutes, dramatically improving operational efficiency and patient care responsiveness.

Technology

Cloud Computing Services (AWS)
Used for scalable and flexible data processing and storage.
Snowflake
Acted as the centralized data lake for all security and operational data.
Tableau
Employed in creating custom dashboards and reports to provide insights into customer security and operational efficiency.
Lambda, S3 and SNS
Enabled seamless and event driven data ingestion from various external sources.
Machine Learning Frameworks
Utilized Python-based frameworks like TensorFlow and scikit-learn for developing predictive models within the AWS environment.

Centralized Data Management	Description:By integrating its diverse data sources, it created a unified source of truth, enabling more accurate and timely operational decisions.
Custom Security Dashboards	Description:Advanced data visualization tools were used to develop custom dashboards for business users, allowing real-time monitoring of security metrics and faster insight generation.
Data Streaming and Ingestion	Description:The company implemented data streaming to ingest data from various sources, including local security devices, customer feedback, and third-party services, ensuring a holistic view of security analytics.
Machine Learning for Predictive Analytics	Description:Introduced machine learning models to predict potential security breaches and customer issues. These models analyze historical data and real-time inputs to forecast risks and suggest preventive measures.
Standardized Security Models	Description:Standard security models and explores were developed for key areas like alarm responses, customer incidents, and technician dispatches, enabling consistent service quality across branches.

Impact and Results

With the new Looker dashboards, time to generate security reports was reduced by 80%, freeing analysts to focus on strategic tasks.
Custom solutions and efficient cloud use led to a 15% monthly reduction in operational expenses.
Standardized reports and real-time dashboards enabled quicker responses to customer needs, improving safety and satisfaction.
Streamlined data processes saved significant time and effort for the analytics teams, who could now focus on developing new security solutions.
Machine learning models enabled predictive analytics, allowing the company to anticipate and mitigate potential security threats before they materialize.