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Leadership and Big Data

This article provides examples of senior managers advocating for analytics in their companies.

As Kotter (1996) points out, leadership is a fundamental factor for any change initiative, such as transforming an organization into a data-driven organization. 

The question of how can leaders transform and propel a data-driven culture can be answered in many ways. During my research on this topic, I found several indicative examples in specialized literature.

An example that sparked a lot of attention is Gary Loveman, CEO of Harrah. Gary often asks the questions: “Do we think it is true? Alternatively, do we know?”. (Davenport, 2006). The questions suggest that decisions at Harrah are not taken based on hypotheses or gut feeling, but rather at the interplay between what we think and what we know, based on data.

Ruben Sigala, the Chief Analytics Officer at Caesars, a company operating under Harrah, presents the transformation undertaken by Harrah to becoming a data-driven company in an interview with MIT Sloan Management Review.

Ruben describes Gary, the CEO, as pivotal in transforming Harrah. With a background in economics, Gary has instilled an analytical culture across the enterprise by being consistent about the importance of leveraging analytics. His focus on analytics helped the company successfully identify itself with a data-driven company and transform itself into one. Although the transformational journey was challenging, some of the important elements that led to the success and fame of Harrah and the leadership of Gary, are:

  • The centralization of the analytics functions – the centralization of functions was supported by an internal restructuration – from many stand-alone entities to one integrated enterprise;
  • During the transformation, transparency and communication about the undergoing changes and the impact it had on the different stakeholders, as well as ensuring that everyone is on board was vital;
  • Analytics units have been built for each area, such as revenue management, finance, marketing, labour, as well as an advanced analytics unit;
  • New employees have been provided with specialized training, based on the unit they are joining;
  • Many employees, although not specialized or working with analytics, have done rotation in analytical functions;
  • Before starting new analytics projects, the company does small live experiments on how marketing affects customer behaviour. There are also larger scale experiments internally, focused on improving operations and processes;
  • Although not detailed in media and literature, Ruben names partnerships focused on advancing analytical capabilities as a key element of their success;
  • Tackling ambiguity in relation to data in an open way – expressing inconclusiveness and constraints of data when there is the case, but making sure to bring the analytics’ perspective on any question they need to answer.

Other examples of CEO’s advocating for analytics found in literature, are Barry Beracha, Patrick Byrne and Jeff Bezos.

CEO Barry Beracha from the Sara Lee Bakery Group had a sign on his table to summarize his organizational philosophy: “In God we trust. All others bring data.” In this way, he would reinforce the message visually to anyone visiting his office. Beracha was known by employees as a “data dog” because he would ask for data to support any hypothesis, exhibiting a behaviour aligned to his philosophy. (Davenport, 2006).

Patrick Byrne, CEO of Overstock.com described his company as being an analytics company (Watson, 2014). Although Overstock.com is an internet retailer company, the CEO attached the image of his company to that of an analytics company, where although they are selling goods, their success and profit comes from the extensive usage of data.   

Jeff Bezos, CEO of Amazon is very well known for saying: “We never throw away data.” (Davenport and Kim, 2013). This shows that Jeff is supporting and encouraging data collection practices. Although data collection might seem like a small activity, its success depends on the appropriate Big Data IT Infrastructure, data cleansing strategies, and collection processes.

McAfee and Brynjolfsson (2012) advise senior managers that wish to lead a Big Data business transformation to start with the following two techniques. The first technique is to make a habit out of asking what does the data say and question the reliability of the data, which will motivate employees to do the same. The second technique is to allow themselves to be overruled by the data, as it can be motivational for employees and for shifting the organizational culture to see that senior management trusts data more than their intuition. 

The questions asked by the CEOs named above and their behaviours might seem small, but their impact extends towards internal processes for collecting and analyzing data, as well as towards an analytical mind-set that is imposed as a way of taking decisions for their company and employees.

In I4L we are researching tools and methods for leaders to instill a data-driven mind-set as well as an understanding of the capabilities that support a data-driven company.

Bibliography

 

  1. Davenport, T. (2006). Competing on analytics. Harvard Business Review
  2. Davenport, T., and Kim, J., (2013) Keeping Up with the Quants: Your Guide to Understanding and Using Analytics. Harvard Business School Press, Boston.
  3. Kotter, J.P. Leading Change. Harvard Business School Press, 1996.
  4. McAfee, A., & Brynjolfsson, E. (2012). Big data – the management revolution. Harvard Business Review, 60-69.
  5. Merchand, D. A., & Peppard, J. (2013, January – February). Why IT Fumbles Analytics. Harvard Business Review.
  6. Sigala, R. (2013, July 30). A Process of Continuous Innovation: Centralizing Analytics at Caesars [Interview by R. Boucher Ferguson]. MIT Sloan Management Review. Retrieved June 2, 2017, from http://sloanreview.mit.edu/article/a-process-of-continuous-innovation-centralizing-analytics-at-caesars/
  7. Watson, H. (2014). Tutorial: Big Data Analytics: Concepts, Technologies, and Applications. Communications of AIS, 34, 1247–1268.

 

 

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