Datactics has made plans for 2019 that will move selected solutions into the public cloud, extend implementation of innovative technologies, create a data quality clinic, and add a matching engine for sanctions screening to its open data projects. The company is also working with clients on data-quality-as-a-service.
The company’s commitment to cloud technology, initially private cloud solutions, is aimed at increasing performance and reducing costs, and allows Datactics to scale its data profiling, cleansing, scoring and matching capabilities for data quality and regulatory compliance on demand.
Alex Brown, pre-sales R&D manager at Datactics, comments: “Cloud solutions provide instant matching for purposes such as looking at end of day reference data from vendors and exchanges and carrying out symbology matching and data quality checks.”
Moving into the pubic cloud and working with Amazon Web Services (AWS), Datactics has already spun up an instance of its Legal Entity Identifier (LEI) Match Engine and is developing additional public cloud solutions.
Stuart Harvey, CEO at Datactics, explains: “Our experience is that banking clients are adopting cloud technology rapidly and need cloud-enabled solutions to run in-house, typically from their own private cloud. The Datactics LEI Match Engine and imminent Sanctions Match Engine, which will support banks’ AML activities, are public cloud, live data demonstrations of our technology. They complement the local, on-premise, private cloud mainstay of our deployments in data quality and matching open and proprietary data sources.”
The Sanctions Match Engine adds to Datactics’ open data projects, which already include Refinitiv PermID and LEI matching engines, and will consume, cleanse normalise and match sanctions data from multiple organisations including the US Office of Foreign Assets Control (OFAC).
Datactics is also planning to introduce additional innovative technologies, including machine learning and other strands of artificial intelligence (AI), to automate workflows, address issues including the resolution of data errors, and support business cases requiring data matching and reconciliation. An early automation programme provides machine-assisted data remediation. In terms of business use cases of machine learning and AI, Datactics pre-sales manager Luca Rovesti, suggests Know Your Customer (KYC) compliance, data migration and deduplication.
The company’s machine-assisted data remediation service is being developed in collaboration with a team from Ulster University and will be the first element of a Data Quality Clinic that Datactics plans to release this year. The company is also working with the university on data discovery, which could be used for regulatory reporting, risk data aggregation and data feed onboarding.