Apgar Consulting provides support to automate readiness actions to improve the productivity of data scientists and take better advantage of modern analytics platforms.
Do not let your data lake turn into a data swamp
Data cleaning and classification is a fundamental step in reducing your analysis time and improving the relevance and confidence of your reports.
Apgar Consulting benefits from its experience in term of master data management, metadata and data integration to define efficient and industrialized data preparation strategies. Due to the amount of data explosion transmitted into data lakes, data scientists spend most of their time ordering, classifying, and cleaning the volumes of data received.
Based on self-learning algorithms and on the knowledge of the essential Company data stored in the Repositories and in the data dictionaries, Apgar Consulting assists you automate readiness actions to improve the data scientist’s productivity and take better advantage of modern analytics platforms.
Choice of solution
Depending on your functional and technological context, we guide you to different solutions:
- Stand-alone solution dedicated to data preparation
- Solution integrated in a data integration platform
- Solution integrated in a “modern analytics” platform
- Solution integrated in a data science & machine learning platform
We define the architecture of your preparation area and identify the data repository that will enable the automation of its operation.
Integration of the solution
Our skills related to data preparation areas make Apgar Consulting a major player for new platforms implementation and integration within our clients’ ecosystem.
We have experienced analysts to review self-learning models, automate classification and clean-up actions.
We complete the metadata governance processes necessary to ensure the proper operation of the solution (tag reference, ontologies, data lineage, context identification, …).
Feedback to operational systems
Apgar Consulting integrates data preparation solutions with data quality monitoring systems so that actions are processed to fix root problems and improve the operation of business processes by following these steps:
- Data Profiling is done on the initial data of Operational Systems
- Data Quality Metrics are defined
- Further Data Analysis is performed on the data based on the defined Quality Metrics
- Identification of defected data and suggest the best solution to fix it