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Now is the time for organizations to revisit their fundamentals of metrics, measures, and monitoring in the service of strategy.
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Because of its sharp focus on measurable outcomes, the study of sports analytics brings many of the most critical issues in management into high relief. Through the lens of sports, there is a great deal to learn about leadership, performance management, decision-making, innovation, and, most of all, managing with data. MIT SMR’s sports analytics podcast, Counterpoints, is a great entry point to the playing field of data-driven management practice.
The future of work will entail thinking not just analytically, but also algorithmically — so companies need to retrain workers for writing code, not formulas. Organizations that manage to make code the natural language for diffusing analysis across their organizations can often grow and innovate faster than their peers.
According to a 2019 NewVantage Partners survey, fear of being disrupted is a leading factor for executives making heavy investments in AI and big data. While many are already seeing measurable results, companies that invest in their people and processes in tandem with technology may see the highest adoption.
Instead of diluting brand identity through an endless pursuit of personalization, organizations should take time to understand their customers and what they value.
The 2018 Data & Analytics Global Executive Study and Research Report by MIT Sloan Management Review finds that innovative, analytically mature organizations make use of data from multiple sources: customers, vendors, regulators, and even competitors. The report, based on MIT SMR’s eighth annual data and analytics global survey of over 1,900 business executives, managers, and analytics professionals, explores companies leading the way with analytics and customer engagement.
An infographic based on the 2018 Data & Analytics Report by MIT Sloan Management Review illustrates how companies can better engage with customers using analytics.
Data has become a key input for driving growth, enabling businesses to maintain a competitive edge. Given the growing importance of data to companies, how should managers measure its value? An increasing number of institutions, academics, and business leaders have begun tackling the valuation problem to help organizations realize more value from their data.
Bad data is the norm. Every day, businesses send packages to customers, managers decide which candidate to hire, and executives make long-term plans based on data provided by others. When that data is incomplete, poorly defined, or wrong, there are immediate consequences: angry customers, wasted time, and added difficulties in the execution of strategy. Getting in front on data quality is crucial, and presents a terrific opportunity to improve business performance.
Analytics teams are often underfunded, misunderstood, and starved for talent. Extracting business value from data depends on nurturing the development and effectiveness of these teams — not just in terms of finding talent, but also in terms of getting leaders up to speed on how to use the insights analytics teams produce.
Plummeting data acquisition costs have been a big part of the surge in business analytics. We have much richer samples of data to use for insight. But more data doesn’t inherently remove sampling bias; in fact, it may make it worse.
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.
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.
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.
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.
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.
Analytics requires structured data, but we may be introducing errors in the processing we use to produce it. An alternative: Do more up-front structuring before we even collect the data so that less processing is needed.
In a video panel and Q&A, MIT SMR editors discuss key insights from a recently completed series of in-depth case studies on how prominent organizations are using data and analytics to transform their operations. They review Intermountain Healthcare, GE, Nedbank, and the City of Amsterdam’s efforts to become more data driven. This set of diverse organizations offers a unique perspective on the challenges and opportunities associated with becoming a data-driven organization.
South African finance leader Nedbank is using data and analytics as a way to help the bank’s clients better understand their business. And the more data-oriented the bank becomes, the better able it will be to turn its developing prowess on itself. As the bank dives deeper into analytics, the same data it’s using for clients can help Nedbank better understand its own organization, employees, suppliers, and more.
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