In most ways, Internet of Things analytics are like any other analytics. However, the need to distribute some IoT analytics to edge sites, and to use some technologies not commonly employed elsewhere, requires business intelligence and analytics leaders to adopt new best practices and software.
Analytics-as-a-service offerings are based on broad categories and attract a range of players with different backgrounds. The urgency to transform into a digital business and to compete more effectively in the global market is forcing buyers to become more information-driven.
Digital transformation is vital for business growth, but working out what to do and how to do it remains difficult for IT and business leaders. At its heart, the digital revolution is about consumer behavior and business opportunity, not technology. The major changes in how consumers use technology, and to where technology is applied, represent a clear and present opportunity to generate new outcomes for modern businesses.
Data and analytics leaders increasingly believe that data science is a critical component of their analytics and business intelligence (BI) modernization plans, and are clamoring to incorporate new. Technologies such as predictive analytics, machine learning and deep neural nets sit at the Peak of Inflated Expectations on related Gartner Hype Cycle.
The pharma and life sciences industr is faced with increasing regulatory oversight, decreasing Research & Development (R&D) productivity, challenges to growth and profitability, and the impact of digitazation in the value chain. The regulatory changes led by the far-reaching Patient Protection and Affordable Care Act (PPACA) in the United States are forcing the pharma and life sciences industry to change its status quo. Besides the increasing cost of regulatory compliance, the industry is facing rising R&D costs, even though the health outcomes are deteriorating.
In the age of Big Data, algorithms give companies a competitive advantage. Today’s most important technology companies all have algorithmic intelligence built into the core of their product: Google Search, Facebook News Feed, Amazon’s and Netflix’s recommendation engines.
Data Sciences and analytics technology can reap huge benefits to both individuals and organizations – bringing personalized service, detection of fraud and abuse, efficient use of resources and prevention of failure or accident. So why are there questions being raised about the ethics of analytics, and its superset, Data Sciences?