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Business Analytics

A methodical approach for KPI reporting, visualization, and insights discovery.

Not every business problem needs a data science or a machine learning model to deliver value, at least to start off. The first tranche of value is realized through a methodical investigation of data along dimensions of relevance to the decision context in hand.

How does Tiger Analytics help you through Business Analytics?

business analytics consulting services

KPI Reporting & Visualization

KPI reports and dashboards for tracking and alerting on pre-defined performance indicators, either before or after a data science exercise.

business analytics consulting companies

Insights Discovery

A structured, iterative approach of drilling the data on dimensions of relevance to discover patterns of interest through both visual and tabular analyses.

Our Approach

Risk Advanced Analytics
  • Stakeholder expectations and Dashboard objectives
  • Business context and constraints
  • Tools and technology choices
  • Internal and external data sources
  • Envisaging outputs/dashboard
business analytics consulting services
  • Exploratory data analysis
  • KPIs used in industry, best practices
  • Analyses and KPIs to address business problems
  • Stakeholder sign-off on KPIs
  • Refresh frequency decisions
risk analytics
  • Map data sources to target database
  • Data cleansing and consistency checks
  • Data transformations and KPI construction
  • Load data into the target database
supply chain analytics companies
  • Mock-ups and wireframes
  • Revise with stakeholders
  • Build dashboard prototype
  • Conduct User Acceptance Testing
  • Release dashboard and schedule for updates
+ Business Requirements
Risk Advanced Analytics
  • Stakeholder expectations and Dashboard objectives
  • Business context and constraints
  • Tools and technology choices
  • Internal and external data sources
  • Envisaging outputs/dashboard
+ Analytics Design
business analytics consulting services
  • Exploratory data analysis
  • KPIs used in industry, best practices
  • Analyses and KPIs to address business problems
  • Stakeholder sign-off on KPIs
  • Refresh frequency decisions
+ Data Processing
risk analytics
  • Map data sources to target database
  • Data cleansing and consistency checks
  • Data transformations and KPI construction
  • Load data into the target database
+ Dashboards
supply chain analytics companies
  • Mock-ups and wireframes
  • Revise with stakeholders
  • Build dashboard prototype
  • Conduct User Acceptance Testing
  • Release dashboard and schedule for updates

Case Studies

Data Engineering and Science

Automated Performance Insights On 25,000 Merchant Partners Worldwide Every Month

How to Become a Data Engineer

Text Analytics And NLP Models To Detect Industry Trends From Social Media

Data Science vs Data Engineering

Shipment Forecasting Application Enables Accurate Trade & Inventory Planning

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