Data Management

What We Offer

Previse’s data management services are designed to allow its customers to treat data as an enterprise asset that can be used to enable stakeholders to execute operations and make informed decisions about their enterprises. These services include:

• Data Architecture Strategies
  Data architecture strategies are a set of guidelines and best practices for designing, building, and maintaining data systems and infrastructure. These strategies help organizations to effectively manage and use their data by providing a clear and consistent framework for organizing, storing, and accessing data.
• Inventory and Documentation (Metadata, Data Dictionaries, ICD’s), Service Level Agreements
  Inventory and documentation is the process of keeping track of the data assets and their relationships. This process helps organizations to understand what data they have, where it is stored, how it is used, and who is responsible for it.
  Service level agreements (SLAs) are agreements between a service provider and a customer that defines the level of service that the service provider will provide and the customer’s expectations. For data management, SLAs can be used to define the availability, performance, and reliability of data systems, as well as the responsibilities of the service provider and the customer in maintaining and managing the data.
• Data Modeling – Conceptual, Logical, Physical and Canonical
  Data modeling is the process of creating a visual representation of the data and the relationships between data elements to understand the data requirements of an organization.
• Data Translations and Transformations (ETL/ELT)
  Data translations and transformations are processes that convert data from one format or structure to another. They are essential when working with data from different systems or applications that use different data formats or structures. These processes ensure that the data can be understood and used by the target system or application and are usually performed by ELT (Extract, Load, Transform) and ETL (Extract, Transform, Load) tools.
• Data Ingestion, Validation and Curation (Error Management)
  These are critical processes in data management. Data ingestion involves the process of bringing data into a system or application. Data validation ensures that the data is accurate, complete, and consistent before it is loaded into the system. Data curation (error management) is the process of identifying and correcting errors in the data, such as missing or incorrect values, to ensure the data is accurate and reliable. These processes work together to ensure that the data is of high quality and can be used effectively by the organization.
• Data Refreshment Strategies
  Data refreshment strategies are a set of guidelines and best practices for updating and maintaining the data in a system or application. These strategies ensure that the data remains accurate, up-to-date, and relevant. Common strategies include incremental updates, full refreshes, scheduled updates, and real-time updates, depending on the requirement and the use case of the data. These strategies help organizations to keep their data updated and relevant, to support their business processes and decision making.
• Operational Data Stores, Data Lakes, Data Warehousing
  Operational Data Stores, Data Lakes, and Data Warehousing are different types of data storage and management systems. Operational Data Stores (ODS) are used to store current and historical data used for operational reporting and analysis. Data Lakes are used to store raw and unstructured data, allowing for flexible and scalable data storage and analysis. Data Warehousing is used to store and manage large amounts of data for reporting, analytics, and decision-making. Each of these types of systems have their own specific use cases and benefits and can be used together to support an organization’s data management and analysis needs.
• Data Dissemination
  Data dissemination is the process of making data available to different stakeholders within an organization, such as employees, partners, and customers. This process involves distributing data through various channels, such as reports, dashboards, APIs, and data feeds. Data dissemination ensures that the data is easily accessible and actionable for those who need it and allows for better decision-making and improved performance. It also enables organizations to share their data with external parties, creating new business opportunities and fostering innovation. Data dissemination can be done through automated, scheduled or on-demand processes, depending on the use case, and the data dissemination can be done in a secure and controlled way to ensure the data is protected.
• Data Governance and Stewardship
  Data Governance and Stewardship are critical practices for managing and using data within an organization. Data Governance is the process of establishing policies, procedures, and standards for managing data and ensuring compliance with legal and regulatory requirements. Data Stewardship is the process of ensuring that data is accurate, complete, and protected. This includes identifying who is responsible for data management and ensuring that the data is being used in a way that is consistent with organizational goals and objectives. Together, data governance and stewardship help organizations to ensure that their data is accurate, protected and used effectively, to support decision-making and drive business success.

All our services are designed to help businesses effectively manage, store, and analyze their data, making it more valuable and actionable, and enabling them to drive business growth and success.