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KPIs should be central organizing principles for leadership investment in data and decision-making.
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As organizations begin adopting deep learning, leadership must ensure that artificial neural networks are accurate and precise to avoid negative impacts on business decisions that hurt customers, products, and services. A designed-centered approach helps address both these short-term concerns as well as the long-term concerns that machines might displace humans when it comes to business decision-making.
Automation can go far beyond cars. Self-driving company capabilities are closer than many leaders realize. And just as automobile manufacturers are rethinking the meaning of driving within the context of self-driving technology, business leaders are being forced to rethink an equivalent question: What does it mean to manage an enterprise once some of the work can be done autonomously?
Executives intent on exploiting AI to enhance processes or products tend to focus on having a strategy for AI. But creating strategy with AI can matter as much or even more. In a machine-learning era, enterprise strategy is defined by the KPIs that leaders choose to optimize —the measures organizations use to create value, accountability, and competitive advantage. AI can help determine what KPIs are measured, how they are measured, and how best to prioritize them.
The value of big data is being captured by large companies, but many small businesses are being left behind. One reason: Investors get more data from larger companies, so that’s where they place their bets. Startup and small business owners must think about their data as a new class of economic asset and understand their data helps investors assess them—which affects their ability to raise capital.
Companies are racing to apply machine learning to important business decisions, only to realize that the data they need doesn’t even exist yet. In essence, the fancy new AI systems are being asked to apply new techniques to the same old material. The result is a visible arms race as companies bring on machine learning coders and kick off AI initiatives alongside a behind-the-scenes, panicked race for new and different data.
MIT Sloan Management Review and MIT Sloan School of Management will chat on Twitter (#MITSMRChat) about the intersection of business and sports analytics. Participants will learn how insights from the sports industry can help companies in other industries excel at performance measurement.
Data is recognized as a business asset, but we lack efficient ways to price it, says MIT professor Munther Dahleh. In this Q&A, he proposes a marketplace that bases the cost of data on the financial value it generates.
Join the co-authors of “Using Analytics to Improve Customer Engagement” and special guest Teddy Bekele as they show how analytical innovators are gathering and sharing data to build loyalty and keep customers.
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.
Nearly 90 percent of senior marketers in a recent survey said an understanding of user journeys across devices and channels is critical to marketing success. No wonder: Leading brands are those that build a foundation of data and analytics to deliver personalized, relevant experiences throughout the customer journey. Determining and measuring the most effective customer engagements is something each organization must do through experimentation, which requires new skills, mindsets and processes.
The successful use of analytics in sports, both on the field and off, comes down to integrating analytics within an organization. Three strategies — collaborative analytics, a common language, and accessible technology — are key.
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.
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.
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.
Many organizations are finding success with IoT projects by starting small, considering the short- and long-term value of initiatives, and looking at alternative ways to investigate issues for the information they need.
Effectively managing and coordinating supply chains will increasingly require new approaches to data transparency and collaboration. Supply chains in coming years will become even more “networked” than they are today — with significant portions of strategic assets and core capabilities externally sourced and coordinated. Already, progressive companies are developing novel solutions to the dilemma of data transparency by using data “cleanrooms” and digital marketplaces.
Problems with data quality come from a lot of sources — short-term solutions, mergers or acquisitions, or even the mundane complications of living in a complex society. The “stench” that develops when data quality declines can create serious issues for data-driven business. If a foul odor is emanating from your data, one solution might lie in refactoring analytics processes.
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