Making Big Data Small

Making Big Data Small

Aretec Employs Proven Software Development Quality Management Processes to Develop Production-Ready Data Science Solutions


An Aretec client had significant challenges with respect to data standardization, performing analytics, and deriving actionable information from data analytics. They were ingesting, and were expected to process petabytes of inconsistent, incompatible, and conflicting data. Analysts and regulators found themselves spending more time inefficiently working through and organizing the data, rather than extracting valuable insights out of existing data sets.

The client required a solution to transform disparate data sets. This required overcoming:
• Transferring/importing large data sets
• Unstructured data
• Inconsistent data
• Incorrect (bad) data
• Data mapped to wrong fields
• Complex data
• Duplicate data
• Irrelevant data
• Incomplete data
• Conflicting data


Aretec developed a custom advanced analytics platform solution that speeds up the process of refining raw, disparate data into a data set that has been validated and reconciled to its source, allowing users to focus on the actual data analysis and performing higher quality analytics. Our solution was designed with the user in mind, allowing them to modify and monitor data as it’s being processed, adapt the software as data input changes, and create custom analytic queries. Our platform generates standard analyses, custom visualizations, and reports that are easily understood by client executives and analysts that may not have deep experience with quantitative research, statistics, or other analytical methodologies.


• Full set of analytic reports without the requirement of having expertise in database querying
• Dependable advanced analytics
• Allows both novice & expert users to take full advantage of the platform
• One application with end-to-end data ingestion, transformation, and data analytics
• Data quality checks
• More time to analyze and less time to compile the data
• Easy exporting of data and analysis to other tools