The importance of data management process is enforced during recent years not only due to the application of innovative practices in data lifecycle but also due to the accelerated use of big data. INESIS expertise in data management stems from the long-term experience in working with various types of data, originating from different data systems and data domains. However, as new demands on data collection, storing and processing data arise we constantly monitor the evolutions in data management to be able to apply the best fitting approach to business needs.
METHODS
Types of data
Structured and Unstructured data
Database development
- Define data requirements
- Develop Data Dictionary
- Relational Database Scheme
- Database Development
Accessibility
Can access virtually any data source and easily integrates into any computing environment.
Data processing
- Manipulate data from different data sources, formats and structures make it hard to bring the data together.
- Automatic conversion of data formats independently from the origination DBMS or flat file format.
- Familiar with solving issues related to the data file format of the exported data that will be used for SAS analysis.
Cleansing and validation
- Data Profiling
- Data transformation
- Generate auxiliary numeric variables based on textual description
- Data corrections/harmonization
- Data deduplication by variable or record
- Outlier detection analysis
- Assess relevance for data analysis
Data visualization
- Descriptive Analysis
- Monitor data Quality
Integration
- Join data across different data sources
- Harmonize and migrate data
Metadata
Document data elements
Create data Glossary and Classifications
- Generation of metadata files according to international metadata standards
- Metadata management
Structural Metadata
- Titles
- dimension code and values
- unit of measures
Reference Metadata
- describe statistical concepts
- describe methodologies used for the collection and generation of data provide information on data quality
Metadata Standards/Templates
- SDMX (Statistical Data and Metadata eXchange)
- ESMS (Euro SDMX Metadata Structure)
- ESQRS (ESS Standard Quality Report Structure)
Big data platforms
Hadoop, Microsoft Azure
Database Management Systems
Microsoft SQL Server, MySQL, ORACLE, SQLite, Microsoft Access database
Non-relational NoSQL databases
BENEFITS
- Organized data
- Efficient information retrieval
- Accurate and usable data
- Flexible data processing
- Reliable Analytics
APPLICATIONS
- Banking
- Official authorities
- Tourism and Travel
- Healthcare
- Retail
- Insurance
- Technology