We create and provide insights into data to understand the “Why” and then reveal
the necessary actions our clients can take with that information.
Data is a company asset just like Capital assets, Human resources, Intellectual property and Patents and copyrights. Like other valuable assets, data needs to be protected, managed and kept in good operating condition in order to support the business. At Datasignis we have developed our own methodologies to support our clients with the following Data Management functions:
Value Add
Datasignis goes beyond just assisting our clients in extracting the correct data, we create and provide insights into their data to help them understand the “Why” and then reveal the necessary actions our clients can take with that information.
Our value is in guiding our clients through data resistance towards fixing the root cause and eventually gaining complete trust in their data.
What Sets us Apart
We understand the importance of technology and technical requirements, however we place emphasis on the bigger picture such as our Clients strategic objectives, their data and the capabilities needed to deliver those objectives.
Our Offering
Datasignis provides solutions that support whichever tools are in place, with data-driven activation, and ensuring that our client’s data is at the right level before they make a move on the technical considerations.
- Focusing on Quick-wins
- Ensuring our client’s data is usable
- Encouraging our client to develop comprehensive data taxonomies that bring clean, usable and easily understood data to the forefront
- Assisting our clients in refraining from the “big-bang” approach in data acquisition, but rather defining specific requirements and collecting the data accordingly
- Support our clients in creating the right team for a data project
Data Governance
- Data asset
- Data governance
- Data steward
Data Architecture, Analysis and Design
- Data analysis
- Data architecture
- Data modelling
Reference and master data management
- Data integration
- Master data management
- Reference data
- Data steward
Data Quality Management
- Data cleansing
- Data integrity
- Data enrichment
- Data quality
- Data quality assurance
Data Security Management
- Data access
- Data privacy
- Data security
Data warehouse management
- Data movement (extract, transform, load)
- Data warehouse assessment
- Data warehouse logical data model review
- Data warehouse physical database design
- Data warehouse environment tuning
- Data warehouse capacity planning
- Data art design and build