Data & Analytics

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 Fundamental Flaw in 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.

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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.

How Big Data Is Empowering AI and Machine Learning at Scale

Big Data is moving to a new stage of maturity — one that promises even greater business impact and industry disruption over the course of the coming decade. Organizations are now combining the agility of Big Data processes with the scale of AI capabilities to accelerate the delivery of business value.

Participant Questions From the Recent Data and Analytics Webinar: Round 2

On March 15, 2017, MIT SMR held a webinar to share insights from our report, “Analytics as a Source of Business Innovation.” Many participants asked questions during the webinar that we didn’t have time for, so we decided to answer them in blog format instead. This post is the second set of responses.

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Research Findings: Analytics as a Source of Business Innovation

Sam Ransbotham and David Kiron, co-authors of the 2017 MIT SMR Data & Analytics Research Report, “Analytics as a Source of Business Innovation,” shared the findings and insights from their research into the changing landscape for companies looking to embed data and analytics into their strategies, processes, and operations.

Questions and Answers About Analytics as a Source of Business Innovation

On March 15, 2017, MIT SMR held a webinar to share insights from our report, “Analytics as a Source of Business Innovation,” which summarizes our findings about the increased ability to innovate with analytics and its benefits across industries. Many participants asked questions during the webinar that we didn’t have time for, so we’ll answer some of them in blog format instead.

Analytics as a Source of Business Innovation

The 2017 Data & Analytics Report by MIT Sloan Management Review finds that the percentage of companies deriving competitive advantage from analytics increased for the first time in four years. Incorporating survey results and interviews with practitioners and scholars, the report finds that companies’ increasing ability to innovate with analytics is driving a resurgence of strategic benefits from analytics across industries. The report is based, in part, on MIT SMR’s seventh annual data and analytics global survey, which includes responses from 2,602 business executives, managers, and analytics professionals from organizations located around the world.

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AI and the Need for Speed

  • Blog
  • Read Time: 4 min 

AI is rapidly changing how organizations make decisions, serve customers, increase quality, and reduce costs. But the pace of change may be too fast for managers to effectively manage processes, react to new problems, and learn from data whose usefulness has a shorter and shorter lifespan.

The Flood of Data From IoT Is Powering New Opportunities — for Some

IoT promised, and delivered, a data deluge. But is the data any good? Survey results from MIT SMR’s recent internet of things research suggest that it is — but the most value goes to those who got into IoT early and have years of experience under their belt. The message to those considering IoT projects: Don’t wait.

How to Monetize Your Data

Companies can monetize their data by improving internal business processes and decisions, wrapping information around core products and services, and selling information offerings to new and existing markets. Adopting any of these approaches, however, requires management commitment to specific organizational changes and targeted technology and data management upgrades.

IoT and Developing Analytics-Based Data Products

Coauthors Thomas H. Davenport and Stephan Kudyba discuss the many ways for organizations to monetize data, including selling “data products” directly to consumers. A seven-step model shows the way real-life companies are developing those products and services.

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