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Reltio Connected Data Platform

If you’re in pharma, biotech or life sciences, watch this video to see how you can unlock the power of data to accelerate your R&D efforts, streamline trials, get products to market sooner, and meet regulatory requirements faster (Sunshine Act, IDMP, HIPPA, GDPR etc).
Join this webinar to hear from Marcie Stoetzel, Senior Data Governance Program Manager at Seagen as she shares Seagen's journey of building data products as a critical pillar of Seagen's enterprise data strategy. She is joined by Sushant Rai, VP, Product Management at Reltio, to discuss the foundational role of trusted, interoperable data in building data products, data product thinking and best practices regarding how to drive adoption within the enterprise.
Discover how Blue Cross North Carolina is modernizing its enterprise data ecosystem and building data products for its private data marketplace supporting multiple personas—including those developing AI-ML solutions—in a secure, reusable framework. You will find out how you can tap into the power of ML and the cloud to unify, standardize, and enrich your core business data with greater efficiency, scalability, and agility. The webinar will also highlight how industry-specific prebuilt components can accelerate your time to value to mere weeks and reduce costs, while pretrained ML-based matching supercharges your data team's productivity.
Tired of your rigid, costly legacy master data management system? Join this webinar to explore the modern MDM revolution. You will see a live demo of transformative MDM capabilities that unlock agility, scalability, and cost-effective ways to unify data. You will gain insights into the importance of industry-specific MDM SaaS solutions and the power of ML-driven matching for unparalleled speed and data accuracy.
For the financial services sector the shift to true data-driven customer-centricity can propel faster growth, strengthen risk management, and enhance customer interactions. However, most banks are held back by data silos, inflexible legacy IT applications and systems, and a shortage of relevant skills. In the aftermath of recent bank collapses, data-driven decision-making and customer understanding are also being prioritized as risk management strategies.
For the financial services sector the shift to true data-driven customer-centricity can propel faster growth, strengthen risk management, and enhance customer interactions. However, most banks are held back by data silos, inflexible legacy IT applications and systems, and a shortage of relevant skills. In the aftermath of recent bank collapses, data-driven decision-making and customer understanding are also being prioritized as risk management strategies.
Master data management (MDM) is an essential element of any enterprise’s attempt to maintain an authoritative system of records for decision support, data quality management, regulatory compliance, and other purposes. It relies on pipeline processes that validate, combine, match, merge, and enrich similar entities from multiple sources to provide a single authoritative record of each unique entity.
Good data is like good water: best served fresh, and ideally well-filtered. Data Management strategies can produce tremendous procedural improvements and increased profit margins across the board, but only if the data being managed is of high quality. Determining how Data Quality should be engineered provides a useful framework for utilizing Data Quality management effectively in support of business strategy.
The pace of change in digital customer experience is head-spinning; the same applies to technology evolution. From last decade’s multichannel engagements, we have arrived at AI-driven personalization at scale. Life sciences organizations are under constant pressure to modernize their technology architecture to enable a frictionless customer experience and deliver next-generation engagements.
When your core data lives in silos, and you cannot trust its accuracy, your efforts to become customer-centric and drive profitable growth are blocked. In today’s complex data environment, only modern master data management can tackle the new requirements to rapidly unblock your path.
It is clear that Data Management best practices exist and so does a useful process for improving existing Data Management practices. The question arises: Since we understand the goal, how does one design a process for Data Management goal achievement?
WEBINAR Forrester TEI Study: How Modern MDM Delivered 366% ROI Using trusted and timely data is vital to improve operational efficiencies for cost savings and drive better customer experience for revenue growth. Yet, when business…