Data & Analytics

Showing 1-20 of 302

Five Management Strategies for Getting the Most From AI

A global survey by the McKinsey Global Institute finds that AI is delivering real value to companies that use it across operations. C-level executives report that when they adopt AI at scale — meaning they deploy AI across technology groups, use AI in the most core parts of their value chains, and have the full support of their executive leadership — they are finding not just cost-cutting opportunities, but new potential for business growth, too.

advertisement

Big Data and IT Talent Drive Improved Patient Outcomes at Schumacher Clinical Partners

  • Interview
  • Read Time: 8 min 

Changing consumer expectations, new regulations, and an influx of patient data has created a perfect storm for health care organizations like Schumacher Clinical Partners to rethink how they leverage digital tools to better serve their patients and providers.

Accelerate Access to Data and Analytics With AI

  • Blog
  • Read Time: 5 min 

Detailed and data-rich insights won’t help your company if your employees don’t know where to find them — but that’s a problem AI can solve. Machine learning can enable faster organizational learning by helping each employee quickly understand what others in the organization understand — forming a knowledge distribution network.

The Coming Consumer Data Wars

  • Blog
  • Read Time: 4 min 

With tough new EU regulations on data security coming in 2018, global companies will soon be faced with a choice: Protect consumers’ data and reap the rewards of having access to it, or face the competitive consequences of consumer distrust. But companies caught unprepared for the change may lose the privilege of keeping consumers’ data altogether.

advertisement

When Jobs Become Commodities

Most of us view our jobs as specialized or somehow differentiated, but the world of business and management increasingly feels otherwise. For many organizations today, the next big driver of job commoditization is automation driven by smart machines. Simply put, if a job is viewed as a commodity, it won’t be long before it’s automated. The key for workers whose jobs have traditionally seemed safe: Highlight the tasks that require a human touch.

Faster Results From Supply Chain Analytics

  • Opinion & Analysis

In this webinar, MIT SMR authors Melissa Bowers, Adam Petrie, and Mary Holcomb discuss the phases of Analytics Insight Cycle Times, present case studies for actual success, and steps supply chain executives can take to reduce cycle times and to ultimately make supply chain analytics a transformational and competitive resource in their organizations.

The Fatal Flaw of AI Implementation

Many managers are excited about smart machines but are struggling to apply machines’ limited intelligence. Indeed, computers can process data just fine, but to generate competitive advantage from machine learning applications, organizations must upgrade their employees’ skills. Companies will also need to redesign employee accountabilities to empower and motivate them to deploy smart machines when doing so will enhance outcomes.

Ethics Should Precede Action in Machine Intelligence

As analytics and big data continue to be integrated into organizational ways and means from the C-suite to the front lines, authors Josh Sullivan and Angela Zutavern believe that a new kind of company will emerge. They call it the “mathematical corporation” — a mashup of technology and human ingenuity in which machines delve into every aspect of a business in previously impossible ways.

Balance Efficiency With Transparency in Analytics-Driven Business

Algorithms are affecting many aspects of daily life, but most people have no clarity as to how they work — even in the companies that create and use them. But individuals and organizations need to carefully consider what this lack of transparency means when it comes to fairness and honesty in commercial interactions and decide where to draw the line on data ethics.

advertisement

Romantic and Rational Approaches to Artificial Intelligence

Organizations have made rapid gains in their ability to generate big data sets, but the ability of managers and executives to develop insights from that data has lagged behind. Data processing by artificial intelligence offers the prospect of speeding things up — but it also risks expanding the gap, as managers lack understanding of how AI reaches its data-based conclusions.

A Data-Driven Approach to Identifying Future Leaders

Many executives believe they are good at identifying leadership talent. However, when asked how they make their decisions, they often cite intuition or “gut” instincts. Social science research, on the other hand, suggests that individuals are often prone to cognitive biases in such decisions. Rather than just relying on the subjective opinions of executives, some companies are using assessment tools to identify high-potential talent.

Showing 1-20 of 302