Delivering Trusted AI with DataRobot and Microsoft

Few things bring down the value of data faster than confusion and uncertainty about what it is, where it came from, and whether it is good quality data. Yet as more users seek to access and interact with data and reports for business intelligence and analytics and as data sources become larger and more varied, confusion and uncertainty spread fast. Executives, managers, regulatory administrators, and other key personnel cannot rely on their reports, KPIs, and dashboards. Users cannot even find reports that the organization is producing. Instead, users spend more of their time trying to locate data and reports and correcting mistakes than they do applying data insights to solve business problems.

The recent GDPR regulations have changed the way companies handle the data they collect and store, making it imperative for data scientists to explore innovative ways to crunch and catalog data while ensuring that the company adheres to these new rulings that seek to establish complete data security.Join Big Data Expert Ronald Van Loon, and Simplilearns Chief Product Officer Anand Narayanan in a live video chat to understand.

Pharma has had access to Big Data for quite some time now, but the data is only useful if you know what to do with it. That’s something pharma companies are just starting to figure out. This webinar will help you get up to speed faster by discussing ways pharma companies can harness the astonishing amount of data they collect to build a lasting competitive advantage.

Data tends to pile up and can be rendered unusable or obsolete without careful maintenance processes. Reference and Master Data Management (MDM) has been a popular Data Management approach to effectively gain mastery over not just the data but the supporting architecture for processing it. This webinar presents MDM as a strategic approach to improving and formalizing practices around those data items that provide context for many organizational transactions: its master data. Too often, MDM has been implemented technology-first and achieved the same very poor track record (one-third succeeding on-time, within budget, and achieving planned functionality). MDM success depends on a coordinated approach typically involving Data Governance and Data Quality activities.