15 Mar 23

Solidatus and Corlytics join forces to ease burden of data regs tracking

Move enables data practitioners to view Corlytics’ digitized regulatory content in the context of their own data and systems, helping them assess likely impacts and plan for change

London and Houston, 15th March. Solidatus, a data management solution that empowers organizations to connect and visualize their data relationships, and Corlytics, a supplier of regulatory risk intelligence that combines innovative technology with deep legal and industry expertise, have today announced an ambitious partnership. That partnership will empower users who are customers of both companies to map regulations from across the globe to their entire data estates, matching them with their systems, policies, processes and people.

The combined solution sees digitized regulatory content from Corlytics automatically pulled into Solidatus regulatory data catalogs and linked to data lineage models where they can be mapped to companies’ processes, policies, controls and systems. This will enable better-informed decisions and allow firms to show that they’re compliant.

Corlytics Founder and CEO, John Byrne said: “This brings huge synergies to the day-to-day work of data practitioners. Combining the added value of Corlytics’ content with Solidatus’ lineage models across firms’ IT infrastructures gives users the unique ability to map their compliance with regulations holistically, tracing lineage across all internal control systems. This means they can more easily identify the impact of regulatory change and demonstrate full compliance with regulations, ultimately delivering ‘front-to-back’ compliance.”

Solidatus CEO and Co-Founder, Philip Dutton said: “Increasing rapid change in the regulatory landscape places exponential pressure on data governance and compliance practitioners. It also forces organizations in heavily regulated industries to spend a disproportionate ratio of funds on regulation and fine avoidance, which in turn strangles their innovation and agility. Our partnership with Corlytics provides end-to-end transparency from regulatory obligation, through implementation to regulatory evidence. It enables automatic and instantaneous impact assessment of regulatory change on an organization, streamlining compliance processes and accelerated execution all the while providing an immutable record of compliance.”

You can read more about what this means for data practitioners in this new blog post: How combining software platforms can help ensure your regulatory compliance.

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For more information please contact:

Solidatus press office

Corlytics press office

About Solidatus

Solidatus is an innovative data management solution that empowers organizations to connect and visualize their data relationships, simplifying how they identify, access, and understand them. With a sustainable data foundation in place, data-rich enterprises can meet regulatory requirements, drive digital transformation, capture business insights, and make better, less risky and more informed data-driven decisions. We provide solutions to several key areas of endeavor, including: governance and regulatory compliance; data risk and controls; business integration; environment, social, governance (ESG); and data sharing. Our clients and investors include top-tier global financial services brands such as Citi and HSBC, healthcare, and retail organizations as well as government institutions.

About Corlytics

Corlytics structures, rates and visualises regulation.

To be able to action and provide assurance on never-ending regulatory change requires firms to know what’s relevant, where to focus and what’s emerging. That’s where Corlytics is helping compliance, risk, legal and audit teams globally. We monitor, assess, map and align regulatory changes in order to alert relevant areas of the business. We take a risk-based approach to prioritising developments and predicting what’s ahead.

We combine innovative technology with deep legal and industry expertise. Our data science technology combines supervised statistical learning methods with novel linguistic feature engineering techniques using natural language processing.

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