Data Management

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.


Types of data

Structured and Unstructured data

Database development

  • Define data requirements
  • Develop Data Dictionary
  • Relational Database Scheme
  • Database Development


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


  • Join data across different data sources
  • Harmonize and migrate data


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


  • Organized data
  • Efficient information retrieval
  • Accurate and usable data
  • Flexible data processing
  • Reliable Analytics


  • Banking
  • Official authorities
  • Tourism and Travel
  • Healthcare
  • Retail
  • Insurance
  • Technology