Analysis & preparation

MakeBeacon examines available data, brings relevant sources together, and prepares a structured dataset for reporting or further analysis. The work starts with a defined question, so the resulting summaries and prepared data are useful for a specific decision or next step.

Useful analysis begins with knowing what question the data needs to answer. MakeBeacon works with your team to define that question, review the available data, and identify gaps, inconsistencies, or limits that could affect the result. The work can include combining relevant files or tables, defining measures and time periods, calculating agreed metrics, and summarizing patterns such as changes over time or differences between groups. MakeBeacon then prepares the data and documents the key assumptions so your team can use the findings in reporting or continue the analysis. This service covers data analysis and preparation. Dashboard design, ongoing reporting, and predictive modeling can be scoped separately.

Problems this solves

• Data is available but the business question is not yet clearly defined.
• Relevant information is spread across multiple files or tables.
• Teams calculate the same business measure in different ways.
• A report needs a prepared dataset with consistent fields and definitions.
• It is unclear whether a dataset is complete enough for the intended analysis.
• Decision-makers need a concise summary of patterns in historical data.
• Analysis results are difficult to reproduce because assumptions were not recorded.

What is included

• Definition of the analysis question and intended use.
• Review of available data sources and fields.
• Combining relevant datasets, where appropriate.
• Calculation of agreed metrics and summaries.
• Analysis by agreed time periods, categories, or groups.
• Identification of data gaps and limitations affecting interpretation.
• Preparation of a structured dataset for reporting or further analysis.
• Documentation of metric definitions, assumptions, and methods.

Typical deliverables

• Prepared dataset in an agreed format.
• Summary of findings relevant to the agreed question.
• Metric and field definitions used in the analysis.
• Notes on assumptions, data gaps, and limitations.
• Repeatable SQL queries or analysis scripts, when included in scope.
• Handover notes for the next reporting or analysis step.

Technologies & tools

• Python
• pandas
• SQL
• PostgreSQL
• Jupyter
• Excel and CSV data formats