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Abstract
As the world is becoming more digital, connected and intelligent, there is a growing desire among companies to become more data-driven. The unstoppable appetite for higher speed, sound and image resolution in this digital revolution is accompanied by an exponential increase in the volume of data. The key challenge, however, is how to use such data to serve our goals better. It is unwise to rely solely on technologies to cope with this challenge; rather, organisations must strike a balance between people and technologies. Technologies are here to help humans to take decisions, not take responsibility for decision-making. Championing this transformation in organisational culture is not easy and it is essential to lead by example. This article provides a foundation for building a data-driven culture and serves as a guideline for datadriven champions and other individuals who want to make such a change. It provides a generic framework on how data can play a critical role in research, problem solving and decision-making; provides an overview of common challenges; and suggests mitigations for daily practice.
The full article is available to subscribers to the journal.
Author's Biography
Shu Wang is Director Business Transformation, Six Sigma Master Black Belt at Royal Philips. Shu is a datadriven decision-making thought leader and established data-driven problem solver who specialises in combining cutting-edge technology, data, advanced analytics and industrial experience to bring the best-in-class digital intelligence and insights.
Ulrike Krisch is Senior Global Sub-Business Category Lead within Philips AVENT, responsible for strategy, marketing, technology, brand and life cycle management. She holds a PhD in economics and specialises in consumer behaviour. Ulrike has held marketing roles across Europe and Asia and brought numerous physical and digital consumer propositions from idea to market.
Citation
Wang, Shu and Krisch, Ulrike (2019, February 6). A foundation for building a data-driven culture. In the Applied Marketing Analytics: The Peer-Reviewed Journal, Volume 4, Issue 3. https://doi.org/10.69554/JQLJ4548.Publications LLP