Datactics software profiles, scores, cleans and reformats data for regulatory compliance and business growth, working in tandem with our automation and scheduling tools to free up your data scientists from spreadsheet hell and “data janitor” preparation work.
Our tools put comparable and alternative item matching at your fingertips, without the need for coding or developer-level knowledge to build new projects or amend rule sets. The automation tools dramatically reduce manual labour and increase revenues by providing immediate access to accurate and standardised data.
Our highly-visual studio used by client subject matter experts for creating, documenting and auditing data quality and matching rules,this product ships with hundreds of out-of-the box rules relating to data validation and quality. It features a drag-and-drop user interface and employs our highly sought-after fuzzy-matching capabilities which have benefitted from significant years of development.
DQM is a cutting-edge web application that enables automation by allowing users to schedule and execute data quality processes which have been designed using FlowDesigner. It is where projects are deployed, scheduled and implemented and contains full user hierarchies and audit trails. Features built-in connectivity to multiple third-party sources spanning financial services (e.g. Bloomberg, DnB, BvD, Thomson Reuters, Companies House etc.) and address-based data (Royal Mail Postal Address File, Gazetteer, AddressBase etc.)
Our software provides a framework for firms that helps business users (Data Analysts / SMEs) to quickly build and implement business rules without the need for programming or development. This framework enables firms to measure, monitor and react to the levels of data quality that exists in multiple areas across the enterprise, through easy integration with off-the-shelf data visualisation tools such as Qlik®, Tableau®, or PowerBI®.
Data Quality Clinic (DQC) is the Datactics environment for resolving data quality issues.
DQC has been designed and built to be the last step of an holistic Data Quality measurement process:
1) Identify issues in the data through running RegMetrics projects;
2) Identify which elements can be automatically resolved through deterministic rules (if any);
3) Anything which cannot be resolved automatically is fed into DQC, where subject matter experts can review the information.