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Creating a Data-Driven Culture: How Culture Impacts the Success Or Failure of Advanced Analytics and AI Featuring Thomas H WEBINAR SUMMARY Creating a Data-Driven Culture: How Culture Impacts the Success or Failure of Advanced Analytics and AI Featuring Thomas H. Davenport JANUARY 27, 2020 SPONSORED BY WEBINAR SUMMARY Creating a Data-Driven Culture: How Culture Impacts the Success or Failure of Advanced Analytics and AI PRESENTER: Thomas H. Davenport, President’s Distinguished Professor of Information Technology and Management, Babson College; Co-founder of the International Institute for Analytics; Fellow of the MIT Initiative for the Digital Economy; Senior Advisor to Deloitte Analytics MODERATOR: Gardiner Morse, Senior Editor, Harvard Business Review Overview Although organizations worldwide have spent trillions of dollars on hardware, software, and data science talent to advance analytics and artificial intelligence (AI), many firms still lack strong capabilities in these areas. To derive benefits from analytics and AI, organizations need a culture that values data and evidence-based decision making. Nurturing a data-driven culture requires working on multiple dimensions. Effective interventions include educating employees, reinforcing behavioral norms, communicating success stories, and promoting executive-level buy-in. Context Tom Davenport discussed the issues associated with data-driven cultures, as well as implications and solutions. He presented examples of firms that have embraced data and AI. COPYRIGHT © 2020 HARVARD BUSINESS SCHOOL PUBLISHING CORPORATION. ALL RIGHTS RESERVED. 2 Created for Harvard Business Review by BullsEye Resources www.bullseyeresources.com HBR WEBINAR CREATING A DATA-DRIVEN CULTURE: HOW CULTURE IMPACTS THE SUCCESS OR FAILURE OF ADVANCED ANALYTICS AND AI Key Takeaways Organizations with data-driven cultures understand how to use data and analytics throughout the businesses. These companies utilize data in decision making, performance monitoring, and product development. They constantly seek new ways to measure the organization and its environment. Sophisticated analytics and AI are at the heart of how companies with data-driven cultures compete. These organizations also recognize the limits of data and analytics. They acknowledge failures and learn from them. And, they conduct frequent experiments. Examples of organizations with data-driven cultures include Capital One, Progressive Insurance, Procter & Gamble, Marriott, Merck, Google, Amazon, Facebook, Ping An, and Radius Finance Group. Several sports teams have also embraced data, such as the Oakland A’s, Houston Rockets, and New England Patriots. Developing a data-driven culture is difficult, but without it organizational performance lags. Surveys by NewVantage Partners suggest that data-driven cultures are rare. Research shows: • Business adoption of big data continues to be a struggle, with 73% of firms citing this as an ongoing challenge. • Only 38% of firms report having created a data-driven organization. • Just 27% report success at building a data culture. • The vast majority (91%) cite people and process challenges, not technology, as the biggest barriers to becoming data driven. Unfortunately, the research shows that companies aren’t improving or are even getting worse. This is discouraging, since companies without a data-driven culture make inferior decisions, engage in less innovation, and lose talent to competitors. A McKinsey study found that organizations are 65% to 70% less likely to be high performers if they don’t have a C-suite data leader, broadly accessible data, and rapid testing and learning from failures. “Companies spend huge amounts of time and money on analytics and AI technologies. Yet, they don’t invest in data-oriented cultures.” ——Tom Davenport COPYRIGHT © 2020 HARVARD BUSINESS SCHOOL PUBLISHING CORPORATION. ALL RIGHTS RESERVED. 3 Created for Harvard Business Review by BullsEye Resources www.bullseyeresources.com HBR WEBINAR CREATING A DATA-DRIVEN CULTURE: HOW CULTURE IMPACTS THE SUCCESS OR FAILURE OF ADVANCED ANALYTICS AND AI Educational, behavioral, marketing, and executive-level interventions can help companies develop a data-driven culture. Four interventions that may help organizations build a data-driven culture are: 1. Educational. This focuses on educating employees about data and what can be accomplished using it. For example, TD Bank developed the Data and Analytics Academy, with over 700 managers participating. 2. Behavioral. These interventions are often preceded by knowledge and/or attitudinal changes. They are observed in key venues like meetings. At Intel, for instance, employees regularly ask colleagues to provide data to support a statement or decision. 3. Marketing and communications. These interventions demystify analytics expertise and publicize organizational successes internally and to customers. Organizations with data-driven cultures are unafraid of discussing failures and what can be learned from them. 4. Senior executive examples and provocation. Culture begins at the top, so examples set by C-suite executives are important as organizations adopt a more data-driven philosophy. Data-driven cultures exist across a variety of sectors. Examples of companies with strong data cultures in very different sectors are: • Capital One. This financial services firm has had an “information-based strategy” since its inception. Capital One conducts hundreds of thousands of tests, and its entrepreneurial culture focuses on developing new, data-based products and services. Data is used heavily for credit decisions and in all aspects of customer interactions and operations. Today, Capital One has embraced AI. • Amazon. The company routinely pursues ambitious, data-intensive projects like Go stores and autonomous drones. It uses data for continuous improvement initiatives such as demand forecasting, product search, product and deal recommendations, and fraud detection. Some managers have signs outside their office doors stating, “In God We Trust, All Others Bring Data.” While Jeff Bezos has driven Amazon’s data-driven culture, he also acknowledges that many important life decisions are made based on “intuition, taste, and heart.” • TD Bank. This firm has a tradition of “running the numbers.” Recently, however, it has turned its attention to “AI and analytics first.” Its wealth management business launched an experiential technology and data program, with tours to Cambridge, Silicon Valley, Israel, Montreal, and other technology hotbeds. TD Bank has upgraded its analytical talent, with seven job families, 64 jobs, and 1,900 employees focused on data and analytics-related work. It also gained AI talent through the acquisition of startup Layer 6. COPYRIGHT © 2020 HARVARD BUSINESS SCHOOL PUBLISHING CORPORATION. ALL RIGHTS RESERVED. 4 Created for Harvard Business Review by BullsEye Resources www.bullseyeresources.com HBR WEBINAR CREATING A DATA-DRIVEN CULTURE: HOW CULTURE IMPACTS THE SUCCESS OR FAILURE OF ADVANCED ANALYTICS AND AI • Eli Lilly. This drug company recently hired a new chief data and analytics officer, tasked with creating a data culture. Although Lilly already had a science-driven culture, the company was less focused on big data and AI. The organization has generated early successes by partnering with early adopters and forming cross-functional teams with diverse backgrounds. It has developed a “marketing approach” for analytics and AI to help create advocates and ambassadors. While these initiatives are still early, they show signs of significant progress. Data-driven cultures can be cultivated at the functional level. The journey to becoming data driven can begin in individual departments that embrace a data- driven culture. A data-driven HR team, for example, may use analytics and AI to predict employee performance or attrition, or the impact of workforce changes. HR could also apply AI tools to extract key information from resumes and LinkedIn profiles, use chatbots to answer common employee questions, and develop learning programs with an intelligent learning management system. Organizations that embrace data and AI must be cognizant of ethical issues. Some companies and organizations that rely heavily on data have gone too far. It is essential to realize that just because something can be done with data, it may not necessarily be a good idea. Companies with data-driven cultures must pay specific attention to ethical data governance and use. Issues like data privacy, bias, and model transparency must be addressed—especially for customer data. Microsoft is a great example of a company that uses data ethically. The firm has created a chief AI ethicist role. Other companies taking a similar approach include Salesforce and Mastercard. A data culture requires leadership and boldness. Having studied the data journey at multiple companies, Davenport offered the following recommendations: 1. Have strong data leadership. Support from the CEO and chief data officer sends a powerful message. It is also essential to convey that data is everyone’s responsibility. 2. Constantly work to build a data culture. This is not a one-time announcement; it is an ongoing journey. At the same time, organizations must strike a balance, focusing on the ethical use of data. 3. Exercise humility. Create a culture where everyone understands that it is impossible to know the answer to a question or a decision until they see the data. 4. If all else fails, consider going elsewhere. Without executive-level buy-in, it is unlikely a company will ever have a strong data-driven culture. If this is the case at your organization, you may need to
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