Data & Analytics

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A Data-Driven Approach to Identifying Future Leaders

  • Research Highlight
  • Read Time: 9 min 

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.

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.

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.

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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.

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.

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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.

How Analytics and AI Are Driving the Subscription E-Commerce Phenomenon

  • Blog
  • Read Time: 6 min 

Box subscription companies are growing dramatically, using a high level of personalization and artificial intelligence algorithms to keep customers satisfied and eager for more. Their astute use of social media and influence marketing has also contributed to their startling success.

Why Your Company Needs Data Translators

When it comes to putting data to use, communication — or rather, lack of it — between the data scientists and the executive decision makers can cause problems. The two sides often don’t speak the same language and may differ in their approach to and respect for data-based decisions. Given these challenges, organizations may need to call upon a “data translator” to improve how data is incorporated into decision making processes.

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Free Webinar, Dec. 1: IoT and Developing Analytics-Based Data Products

On Dec. 1 at 11 a.m. EST, join MIT SMR coauthors Thomas H. Davenport and Stephan Kudyba in a free, live webinar, where they will discuss their recent article, “Designing and Developing Analytics-Based Data Products.” The authors will look at the ways in which the internet of things, market forces, and evolving technology are changing how companies plan the development of data products. This new product category requires a reworking of the traditional phases of product development.

Why Big Data Isn’t Enough

There is a growing belief that sophisticated algorithms paired with big data will find relationships independent of any preconceived hypotheses. But in businesses that involve scientific research and technological innovation, this approach is misguided and potentially risky, as spurious correlations and “noise” may lead analysts astray.

Showing 1-20 of 284