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.

Sports analytics first proved its case on the field and in the front office, but as the practice spreads into business operations, the industry is addressing adoption challenges found in many sectors. At the MIT Sloan Sports Analytics Conference, speakers from teams and leagues discussed how they are using analytics to boost revenue, and how they’re managing transitions in culture and strategy.

In Part 1 of our eight-part video series, we explore the real power of the digital thread, which lies not just in a “cradle-to-grave” virtual rendering of the manufacturing process, but also in the possibility of taking the lessons learned from analyzing product performance and applying them to future generations of the manufacturing process and product design.

David Harcourt of Yum! Brands Inc. shares his research and experimentation with HR analytics and shows how to align strategic talent management goals with HR analytics projects and activities.

MIT SMR’s 2018 report “Using Analytics to Improve Customer Engagement” surveyed more than 1,800 managers and interviewed executives at global companies and found that better customer engagement is linked to superior use of data and analytics.

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.

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.

Poor communication between managers and technical experts is an obstacle to technology innovation that literally has been present for centuries. To overcome these issues, leaders need to absorb three key lessons about how to manage the inherent tensions between defining technical requirements and achieving valuable business outcomes.

Until companies start to invest seriously in security, internet-enabled devices will offer far better ROI for thieves than for the companies seeking to build IoT capacity. But the benefits ultimately will make the effort worthwhile.

Identifying the optimal prices for products was once a time-consuming process. That’s changing as businesses start to take advantage of advances in machine learning, increases in computing speed, and greater availability of data.

Companies handling large volumes of data face a greater chance of erroneous linkages creeping into their analytics. Three key habits of data managers can reduce the risk of missed data connections.

To gain competitive advantage from supply chain analytics, companies need to reduce the time it takes to act on the insights those analytics generate.

While Big Data allows personalized service, collecting detailed data about real people (rightly) often raises concerns. But can analytics be used to improve security?

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.

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.

Companies have yet to apply analytics to human resources — but that’s about to change. And lessons learned in applying analytics to customer-focused areas can help avoid mistakes in strategic workforce decisions.

Managers can guard against data deceit affecting their decision making by asking questions about the quality of their data, the strength of their models, and the assumptions behind their analysis.

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.

The ability of artificial intelligence to sift through mountains of data and identify patterns — and problems — in real time is its key value for business. Using AI to predict failures and take action to prevent them will become commonplace in the very near future. But it can also offer insights into human behavior to help managers improve customer service and employee relations.