Understand what your business data can tell you.

A business can have plenty of data without knowing what it can reliably answer. This check connects the available data to practical business questions and next steps.

• Data review
• Data quality
• Business questions
• Next-step planning

• Data and structure observations
• Important data-quality findings
• Business-question assessment
• Prioritized analytical next steps

Messy or unfamiliar data can block useful analysis. Choose the option that matches the data need: clean a coherent dataset, prepare it for a defined analytical purpose, consolidate compatible sources, or explore the data to understand its characteristics and notable patterns.

• Data cleaning
• Data preparation
• Data consolidation
• Data exploration
• Data visualization

• Data prepared according to the agreed data operation.
• Relevant data-quality, preparation or exploratory notes
• Summary of material changes, findings and limitations where applicable

Reports and dashboards solve related communication problems, but their output and presentation are different. Select the output you need and Averidi structures it around the agreed audience, questions and KPIs.

• Business reports
• Business dashboards
• KPI reporting
• Charts and tables
• Management reporting

• Business report or one dashboard, depending on the selected option
• Clear KPI and trend views appropriate to the selected output
• Supporting definitions, notes or interpretation

Repeated weekly or monthly reporting can consume valuable time. Our reporting automation service focuses on one stable reporting workflow that can be mapped, standardized, and automated where appropriate.

• Reporting workflows
• Repeatable transformations
• Automated outputs
• Validation points
• Documentation and handover

• Repeatable reporting workflow for the agreed process
• Workflow documentation
• Validation and handover notes
• Clear identification of any steps that should remain manual

Historical data can provide a structured basis for estimating future values when there is enough relevant history. The standard service focuses on one forecast target and the planning decision it needs to support.

• Sales forecasting
• Demand forecasting
• Revenue forecasting
• Forecast evaluation
• Scenario and sensitivity interpretation

• Forecast for the agreed target and horizon
• Forecast visualization
• Method, validation and assumption notes
• Business interpretation and planning implications

Predictive modeling helps businesses estimate outcomes before they happen. For example, historical data can help identify customers who may be at risk of churning, allowing the business to act before they leave. Understanding what is likely to happen can help a business act earlier and stay a step ahead.

• Classification
• Regression
• Model evaluation
• Feature analysis
• Prediction interpretation

• Predictive model for the agreed target
• Model performance evaluation
• Method and validation notes
• Interpretation of important factors where appropriate
• Business-use implications and limitations