Analytics as a Service | Asset Lifecycle Management Solutions

Analytics as a Service

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Our Analytics as a Service (AaaS) is an innovative platform for rapidly deploying statistical analytics and business intelligence (BI) for a client’s existing asset lifecycle data (CapEx and OpEx). This platform uses deep industry expertise, statistical models and connected BI dashboards to deliver optimal time-to-value for clients enabling faster, smarter business decisions. Once implemented, value can be generated quickly by the predictive models and BI visualizations used to develop insights, explain hidden trends and identify blind spots within the data. BI visualizations and dashboards are readily accessible to your organization, and users can be quickly trained for in-house configuration when necessary.

Our Platform includes the following components:

  • Dynamic and interactive self-service dashboards
  • Analytics software with targeted statistical models
  • Cloud data warehouse
  • Business Intelligence software user licenses
  • Platform hosting and user administrative support

The implementation timeline for our AaaS Platform starts at 6 weeks and consists of scoping, design, develop and deploy phases.

The innovative Analytics as a Service (AaaS) platform can rapidly implement full-spectrum data science solutions that are reliable, scalable, and allow clients in asset intensive industries to make smarter decisions faster, thereby improving performance. The key benefits of this solution are:

Statistical problem solving

  • Identify and quantify the variables that truly matter to understand and control performance
  • Accelerate decisions with timely, accurate, transparent and actionable information

Data management

  • Manage the single source of truth for data in one repository system
  • Enable benchmarking capabilities based on historical data
  • Improve information accessibility, control and distribution both internal and external to the organization
  • Easily access historical data snapshots for review and analysis

Automation

  • Automated data collection, consolidation, and cleansing
  • Automated report generation and distribution, in adherence to reporting cycles

Reporting / Dashboarding

  • Consolidate and control formal reporting from a single platform
  • Improve ability to identify exceptions and / or potential corrective actions
  • Equip analysts to configure ad-hoc reports quickly and accurately
  • Reduce effort required for report development
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