How companies and organizations can gain an analytics edge in 2023 and beyond
Data drives efficiencies, increases profitability, and bolsters innovation. Yet analytics skills remain outside the wheelhouse of many business leaders, who don't always have a clear view of what data sources and analyses are necessary to formulate the insights they are seeking.
In a new series, " The Analytics Edge," published by MIT Sloan School of Management's Ideas Made to Matter, MIT Sloan faculty, alumni, and industry experts share practical tips for developing and cultivating a strong analytics practice designed to give companies and organizations a distinct advantage for the future.
While data scientists may be in demand, data literacy starts with leaders who trust and understand data enough to make their best business decisions. For optimal success, leaders must also drive literacy efforts throughout their organization to create a culture of trust in data. MIT Sloan data experts share how best to move forward.
Featuring: Barbara Wixom, principal research scientist, the MIT Center for Information Systems Research; and Rama Ramakrish n an, MIT Sloan professor of the practice, data science and applied machine learning.
Try this data framework for analytics advantage
Dimitris Bertsimas, associate dean of the Master of Business Analytics degree program at MIT Sloan, has developed an approach to help nontechnical business users to gain confidence using data to improve their decision-making. Bertsimas, a professor of operations research, lays out an analytics framework designed to help businesspeople determine which analytics approach is best suited for their application while improving their ability to leverage big data for better business outcomes. The framework is built upon data, models, decisions, and value, with an emphasis on decision-making.
In-demand data and analytics skills to hire for now
Hiring managers from Netflix, Pfizer, Comcast and MFS Investment Management share their "must-have" lists when it comes to hiring for data and analytics roles. In this article, they discuss essential technical skills and soft skills, and finding the elusive "super unicorn" data scientist.
Featuring: Trace Hawkins, senior vice president of strategic analytics at Comcast; Jonathan Lowe, data science lead for Pfizer's PGS Operations Insights; Nadine Kawkabani, global business strategy director at MFS Investment Management; and Yichen Sun, (MIT SM '13), data science manager at Netflix.
What is synthetic data — and how can it help you competitively
Technological research and consulting firm Gartner estimates that 60% of the data used in artificial intelligence and analytics projects will be synthetically generated by 2024. Indeed, synthetic data offers the opportunity to test new ideas and develop new products without putting personal or health data at risk, preserving the correlations among data variables. This article explores MIT research into the topic and how to apply it to your company. It also provides access to a free Synthetic Data Vault to guide organizations in building their own synthetic data sets.
Featuring: Kalyan Veeramachaneni, principal research scientist at the MIT Schwarzman College of Computing; Ali Jahanian, former AI research scientist in MIT CSAIL and senior applied scientist at Amazon.
How to build an effective analytics practice: 7 insights from MIT experts
From designing intelligent decision processes to tapping the full power of deep learning, faculty members who teach in MIT Sloan's Master of Business Analytics degree program share mistakes to avoid, strategies to adopt, and technological developments that excite them most that can be applied to your business practices.
Featuring: Dean Eckles, MIT Sloan associate professor and affiliate faculty at the MIT Institute for Data, Systems & Society in the Schwarzman College of Computing; Daniel Freund, MIT Sloan assistant professor, operations management; associate professor of operations management; Alexandre Jacquillat, MIT Sloan assistant professor, operations research and statistics; Retsef Levi, MIT Sloan professor, operations management; Jordan Levine, MIT Sloan lecturer, operations research and statistics; Rama Ramakrishnan, MIT Sloan professor of the practice, data science and applied machine learning; and Karen Zheng, MIT Sloan associate professor, operations management.