ACMECONSULTINGFounded in 1990Reliable data is the foundation of useful analytics and AI. ACME helps you find data engineers, analysts, BI developers and AI specialists. The assignment may involve connecting data sources, improving reporting or testing an AI solution. We start with the question you want to answer and the available information, so technology, cost and evaluation follow business needs.
Shared definitions and information owners reduce differences between systems and reports. The consultant works with areas such as information models, data catalogues and lineage. Quality rules reflect intended use: how current and complete data must be, and who handles exceptions.
Integrations and data warehouses must accommodate changes in source systems. Tests, schema management and monitoring help teams detect errors. The choice between batch processing and streaming reflects how quickly information is needed, as well as cost and technical complexity.
Analytics starts with a business question and measures users understand. Definitions, comparison periods and data sources need to be shared. The consultant builds models and visualisations that explain what the figures cover, their limitations and who should act on them.
AI is tested against a defined task and a baseline. Generative solutions call for review of sources, permissions and answers; predictive models require accuracy to be weighed against the consequences of errors. Model and data changes are monitored after implementation, with named people responsible for assessment.
We agree the scope and deliverables with you.
An overview of sources, owners and quality issues linked to the business question. It identifies what to address before investing in a platform or AI pilot.
A data flow or decision-support tool with documented definitions, access and quality checks. The team can test its use before extending the solution.
Test cases and practices for monitoring quality, errors, cost and user benefit. For AI, this also covers when the model or way of working should be reassessed.
It depends on the use case. A defined application can sometimes begin with a few well-controlled sources. Understand access, quality and ownership first. The platform's scope should follow those needs and how the solution will be used and maintained.
A data engineer mainly builds reliable data flows and technical foundations. A data scientist more often works with analysis, statistical models and experiments. Many assignments need both, alongside a business owner who defines the question and assesses the value.
Define the task to improve and compare it with the current approach. Follow quality, time, error handling and user confidence, for example. Include review, operations and maintenance in the assessment rather than relying only on a successful demonstration.
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