Modernize Your Data Architecture to Deliver Oracle as a Service

Much like project management and home improvements, Data Governance sounds a lot simpler than it actually is. In a nutshell, Data Governance can be explained as “managing data with guidance.” In general, the perceived utility of these programs increases with the specificity of desired data and processing improvements. Whether restarting or starting your Data Governance programs, it is critical to be guided by a periodically revised Data Strategy that links support for organizational strategy to specific operational data improvements. Understanding these and other aspects of governance is necessary to eliminate the ambiguity that often surrounds the implementation of effective Data Management and stewardship programs.

In the aftermath of Roe v Wade, the FTC shared a blog post declaring its commitment to crack down on illegal data usage & data sharing by software organizations.

As data and analytics environments become increasingly complex, organizations can no longer afford to perform many operations manually. According to TDWI research, automation (in general) is one of the top three priorities for analytics. We see automation occurring throughout the data and analytics life cycle. Automation increasingly leverages embedded AI/ML algorithms (i.e., infused in the software) to help perform tasks such as profiling and cleansing data, identifying sensitive data, data mapping, surfacing insights, or building machine learning models.

Fewer patients, preventative screenings, and primary care visits. Lower revenues. Worsening population health outcomes. The healthcare industry is facing serious challenges to its business model as consumer behavior shifts to other ways to meet their healthcare needs or a complete failure to seek care until conditions become acute. The need to meet consumers where they are and engage with them on their terms has never been more urgent.