Analytics Automation Services
Data Preparation & Cleansing Services
Analytical outputs become difficult to trust when source fields, joins and cleansing decisions are left implicit. Macrofix delivers data preparation services around approved rules, visible exceptions and tested datasets. We profile the included sources, define transformations and reconcile outputs so your reporting or analytical team can understand how its data was prepared.
Discuss Your Data Preparation Project
Make Analytical Data Rules Explicit and Testable
We begin with the intended dataset and the decisions it must support. A reporting feed, reconciled transaction set or prepared analytical table needs clear source boundaries, field definitions and an agreed record grain. The engagement identifies where data can be standardized and where a business owner must resolve an ambiguity.
For the wider delivery program, explore analytics automation consulting. Repeated execution and recovery are scoped through workflow automation, while downstream reports sit within business intelligence services.
Data Preparation From Source Profile to Validated Dataset
Choose a source-quality assessment, focused preparation pipeline, spreadsheet-transformation redesign or reusable dataset engagement. Scope can include profiling, type and format rules, joins, calculations, exception outputs, quality tests and handover. The proposal identifies sources, fields, rules and datasets included. Source-system correction, enterprise master data, scheduling and dashboard development are separate unless stated.
Source Assessment and Data Profiling
We review source structures, types, completeness, duplicate patterns and representative value distributions before designing transformations. Profiling coverage is explicit: a preview or sample does not establish full-dataset quality. Microsoft Power Query profiling guidance illustrates this distinction with sample-based and entire-dataset profiling options. The method and coverage are selected for your environment, data volume and acceptance requirements.
- Source, field and output inventory
- Types, completeness and value-profile review
- Duplicate and source-change patterns
- Explicit sample or full-dataset coverage
Cleansing, Standardization and Exception Rules
Cleansing rules define permitted formats, type conversions, missing-value handling and duplicate treatment. Business owners approve whether a record is corrected, excluded, retained with a flag or routed for review. We preserve the meaning of identifiers, dates and numerical values rather than applying convenient defaults without approval. Ambiguous or conflicting source records are documented instead of silently assigned a new business meaning.
- Approved type and format conversions
- Missing-value and duplicate-treatment rules
- Business decisions for ambiguous records
- Correction, exclusion or review exception paths
Joins, Transformations and Analytical Dataset Design
Transformation design makes table relationships, keys, filters, calculations and output grain explicit. We test join behavior for unmatched records and unexpected multiplication, and agree aggregation rules before creating the dataset. Reusable preparation logic is organized for the selected tool or code environment. A prepared analytical table is not automatically a complete enterprise warehouse or master-data system.
- Keys, joins and unmatched-record behavior
- Filters, calculations and aggregation rules
- Explicit analytical output grain
- Checks for unexpected join multiplication
Quality Checks and Reconciliation
We agree quality thresholds and reference evidence for the included fields and outputs. Checks can cover completeness, uniqueness, valid ranges, record counts and business reconciliation where relevant. Exception outputs identify records that require correction or review. Passing a technical check does not prove every source record is accurate; acceptance considers the agreed business rules and documented limitations.
Reusable Preparation Logic and Handover
Handover documents source assumptions, transformation steps, rule ownership and output definitions. We aim for meaningful structure and reusable components rather than an opaque chain of edits; Microsoft Power Query best practices provide one reference for types, documentation and modular preparation. Runtime connectivity, refresh behavior and change procedures are scoped for the selected environment. Recurring operation is a separate arrangement unless included.
Agree Your Data Preparation Blueprint
The blueprint records source tables or files, fields and data grain, cleansing decisions, join and calculation rules, exception paths and accepted outputs. We identify whether Power Query, Alteryx, SQL or another supported approach fits the existing environment. Platform-specific work is available through Alteryx consulting and Power BI services. Tool availability, licensing and source permissions are confirmed before delivery.
- Approved sources, fields and output datasets
- Profiling method and coverage requirements
- Cleansing and standardization decisions
- Join, transformation and calculation rules
- Quality thresholds and exception ownership
- Validation, handover and operating boundaries
Assign Source, Rule and Dataset Owners
Source owners approve data use and manage upstream quality issues. Business-rule owners approve cleansing and calculation decisions. Preparation owners maintain transformations and tests, while dataset consumers approve output requirements. The handover records who reviews exceptions and who must approve a rule change, so an analytical correction does not quietly become an unauthorized source-policy change.
- Source owners manage upstream quality and data use.
- Business owners approve cleansing and calculation rules.
- Preparation owners maintain transformations and tests.
- Dataset consumers approve outputs and exception handling.
Our Data Preparation Delivery Process
We assess the sources and intended output, profile representative data, then agree preparation rules with accountable owners. Development applies those rules in the selected environment. Validation reviews expected outputs and exception cases before controlled release, documentation and consumer walkthroughs. Expansion to additional sources follows the same rule and acceptance process.
- Assess sources and intended analytical outputs.
- Profile data and agree transformation rules.
- Build preparation logic in the selected environment.
- Validate records, reconciliations and exceptions.
- Release with rule documentation and consumer handover.
Validate the Rules, Records and Output Dataset
Acceptance checks agreed field types, formats, joins, calculations and record-level exceptions. We reconcile relevant totals and counts with reference evidence, inspect unmatched or duplicated joins and review profiling coverage. Tests include representative source changes where in scope. Remaining quality limitations, correction responsibilities and output definitions are documented before the dataset is released.
- Types and formats match approved definitions
- Joins and calculations preserve the intended grain
- Outputs reconcile with reference evidence
- Exceptions and profiling coverage reviewed
- Rule owners and remaining limitations recorded
Data Preparation Services FAQs
The agreed scope can include source profiling, cleansing and standardization rules, joins, transformations, calculations, exception outputs, quality checks, reconciliation and handover. We identify the included sources, fields and datasets. Source-system correction, master data, scheduling and dashboard development are separate unless explicitly included.
No. Some issues require business judgment or changes in the source system. We agree which records can be corrected, excluded, flagged or routed for review, and identify the owner responsible for each decision. Ambiguous data is not silently changed to an assumed value.
A sample can reveal patterns and support development, but it does not establish the quality of every record. We make profiling coverage explicit and agree representative or full-dataset validation requirements. The selected tools, data volume and acceptance criteria determine the practical approach.
Potentially, subject to approved access, supported connectivity and compatible business definitions. We assess identifiers, record grain, field types and join behavior before combining sources. A connector alone does not resolve inconsistent customer IDs, conflicting calculations or unmatched records.
The approach depends on the existing environment and requirements. Power Query, Alteryx, SQL or another supported tool may be suitable. We consider source connectivity, maintainability, deployment and licensing before selecting an approach; a new platform is not assumed.
Only when explicitly scoped. Preparation delivery can include reusable logic and operating documentation, while scheduled execution, failure recovery and recurring support require stated runtime and ownership arrangements. A validated dataset does not automatically include a continuously operated refresh service.
Plan Your Data Preparation Engagement
Tell us which sources need preparation, how the output will be used and which quality or reconciliation issues cause rework. Include field definitions, current transformations, dataset volume and known exceptions where available. We will shape an assessment or delivery scope with approved rules, validation evidence and maintainable dataset handover.