Standardize domain values of all systems to business relatable values. Keep lists of values, hierarchies, code lookups, and mapping tables consistent to ensure all reference data is clear and easily understood.
Reference data is critical for any business as it provides a standardized language for different systems to communicate and for data to be understood across the organization.
Data scientists rely on high-quality reference data to perform accurate analysis, while data engineers use it to design and maintain the underlying data infrastructure.
By centralizing reference data in one system, your organization can reduce costs linked to maintaining multiple versions of the same data. You can easily identify the impact of changes on other entities and produce consolidated reports in less time.
Leveraging reference data enables you and your team to make more informed decisions and improve overall data quality.
Cornerstone of data governance initiative
Our reference data management solution offers a centralized system for reference data, ensuring data quality and clear lookup tables for reporting purposes.
Reduces costs associated with maintaining reference data in multiple systems by consolidating all data into one system.
Easily identify and address the impact of changing reference data content on other reference data entities.
Produce consolidated reports easily using the centralized storage of reference data, eliminating the need for redundant IT implementations across multiple systems.
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Create a single point of truth of shared business domains in your company to prevent inconsistent reporting. Standardize different reference data representations across data domains. Easily view historical versions of your reference data. Set up validation rules to restrict the values you can enter into the data.
Assign source-specific reference data codes to standardized business domains. Any change in the definition of reference entities or data content is kept and can be audited.
Assign responsibilities for defining reference entities and their properties as well as maintaining and visualizing reference data content, ensuring its transparency. Benefit from versioning and auditing to ensure the traceability and transparency of reporting results.
Easy data imports based on defined mappings. Import data from Excel or CSV files, as well as from database tables. Accurity supports relational and NoSQL databases through native connectors. Accurity supports REST API integration to facilitate efficient and flexible data imports.
Thanks to the awareness Accurity helps spread about data quality, we no longer encounter the data quality issues that we fixed in the past. Data quality rules have become enshrined in the data entry process. I firmly believe that if we had rules over all our data, there would be no bad data quality in our reporting whatsoever.
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