Managing with Analytics at Procter & Gamble

Thomas H. Davenport; Marco   Iansiti; Alain  Serels

Case | HBS Case Collection | April 2013

Managing with Analytics at Procter & Gamble

by Thomas H. Davenport, Marco Iansiti and Alain Serels

Abstract

Senior management at P&G has put a strong emphasis on using data to make "better, smarter, real-time business decisions." The Global Business Services (GBS) organization has developed tools, systems and processes to provide managers throughout P&G with direct access to up-to-date data and advanced analytics. In addition, GBS has embedded analysts within the business units to work alongside leaders and managers in driving real-time information-based decision making. Equipped with the tools provided by GBS, Alan Torres, vice-president of North America Fabric Care, must finalize the forecast for P&G's laundry detergent sales. Results for the two months since introducing concentrated powder laundry detergent in select retailers saw a surprising jump in sales of over 10%, but would the trend continue as the concentrated detergents were introduced across North America?

Keywords: analytics; Data Management; Forecasting; information technology; Shared Services; Procter ＆ Gamble; Laundry Detergent; Information Management; Forecasting and Prediction; Information Technology; Mathematical Methods; Consumer Products Industry; North America;

Language: English

Format: Print

20 pages

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Citation:

Davenport, Thomas H., Marco Iansiti, and Alain Serels. "Managing with Analytics at Procter & Gamble." Harvard Business School Case 613-045, April 2013.

In summer 2020, Stephane Bancel, CEO of biotech firm Moderna, faces several challenges as his company races to develop a vaccine for COVID-19. The case explores how a company builds a digital organization, and leverages artificial intelligence and other digital resources to speed its operations, manage its processes and ensure quality across research, testing and manufacturing.
Built from the ground up as such a digital organization, Moderna was able to respond to the challenge of developing a vaccine as soon as the gene sequence for the virus was posted to the Web on January 11, 2020. As the vaccine enters Phase III clinical trials, Bancel considers several issues: How should Bancel and his team balance the demands of developing a vaccine for a virus creating a global pandemic alongside the other important vaccines and therapies in Moderna’s pipeline? How should Moderna communicate its goals and vision to investors in this unprecedented time? Should Moderna be concerned it will be pegged as “a COVID-19 company?”

Working Paper | HBS Working Paper Series | 2020

Time and the Value of Data

Ehsan Valavi, Joel Hestness, Newsha Ardalani and Marco Iansiti

This paper investigates the effectiveness of time-dependent data in improving the quality of AI-based products and services. Time-dependency means that data loses its relevance to problems over time. This loss causes deterioration in the algorithm's performance and, thereby, a decline in created business value. We model time-dependency as a shift in the probability distribution and derive several counter-intuitive results.
We, theoretically, prove that even an infinite amount of data collected over time may have limited substance for predicting the future, and an algorithm that is trained on a current dataset of bounded size can attain a similar performance. Moreover, we prove that increasing data volume by including older datasets may put a company in a disadvantageous position.
Having these results, we answer questions on how data volume creates a competitive advantage. We argue that time-dependency weakens the barrier to entry that data volume creates for a business. So much that competing firms equipped with a limited, but sufficient, amount of current data can attain better performance. This result, together with the fact that older datasets may deteriorate algorithms' performance, casts doubt on the significance of first-mover advantage in AI-based markets.
We complement our theoretical results with an experiment. In the experiment, we empirically measure the value loss in text data for the next word prediction task. The empirical measurements confirm the significance of time-dependency and value depreciation in AI-based businesses. For example, after seven years, 100MB of text data becomes as useful as 50MB of current data for the next word prediction task.

Keywords: economics of AI; Machine learning; non-stationarity; perishability; value depreciation;

Citation:

Valavi, Ehsan, Joel Hestness, Newsha Ardalani, and Marco Iansiti. "Time and the Value of Data." Harvard Business School Working Paper, No. 21-016, August 2020. View Details

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The broad adoption of contact-tracing apps would greatly help combat the spread of COVID-19. But a number of barriers—especially privacy concerns—have hindered progress in many countries that can’t or won’t mandate adoption. A solution is to start with small communities where they would be instantly useful (families, religious communities, workplaces, schools, bars and restaurants, beaches, hotels, trains, airplanes), get everyone to download and use it, and then scale from there. To achieve that, the app should be designed so it is instantly valuable to anyone in the targeted community who downloads it. Managing users’ expectations is also important.

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Managing with Analytics at Procter & Gamble

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