Thought Leader

Machine Learning: An enabler of business strategy and innovation.

The purpose of this keynote was to bring the audience to the realisation that ML solutions to help leverage a customer-centric sometimes referred to as a customer-obsessed strategy approach to doing business. Listening to the voice of customers plays a prominent role in a customer-centric business strategy. But with the business environment’s increased complexity and dynamism for a customer-centric business to thrive in its value delivery, there is a growing need for personalization and continuous evolution of business decisions to align with changes in customer needs. This could be challenging, particularly in organizations with a large customer base. In response, this talk presents how advanced analytics and machine learning techniques have fostered operational efficiency and business effectiveness in large organizations. Specifically, this keynote highlighted how tree-based machine learning methods have been employed in understanding and prescribing solutions to complex and evolving operational business problems. Furthermore, it presented insights into how uplift modelling has improved response rates and returns on marketing spends in large-scale targeted campaigns. Underpinning this talk is a discussion of the leadership approach that informed these innovations.

Nordic Data Science and Machine Learning Summit 2019 is the leading annual event bringing together the data science community in the Nordics to share ideas and discuss ways to harness the full potential of data science and machine learning.

Beyond data science: Unlocking business values

This session demonstrated how well aligned data science projects unlock business value. It had its core on the use of advanced analytics techniques, guided by the business strategy and process, to improve business effectivity and efficiency. Importantly, it emphasised how ensuring that the data science methodology clearly aligns with the business problem and process. Furthermore, the method answers the business problems, indeed, the problem dictating the solution. In particular, the case project leveraged a tree-based method, together with uplift modelling to enhance the effectiveness of marketing campaigns, thereby delivering strong Return on Marketing Investment.

Link to talk: https://www.youtube.com/watch?v=AqZiN2HICOU&list=PLLzugMe2NdR-cvHQhHn_cHBLYdqNhSh82&index=3&t=8s

Innovation Process’ explanation of the association of Servant Leadership and Employee Wellbeing:  An application of Dynamic Structure Equation Modelling (DSEM)

Employee innovation is beneficial to organisations as it is seen as a source of competitive advantage (Kaufman & Sternberg, 2010), efficiency (Henker et al., 2012), and be critical to the survival of businesses (Zhou &Shalley, 2003). As such leaders are looking for ways to make their followers more innovative. Innovation as processes involved in the generation and implementation of ideas are novel and beneficial (Anderson et al., 2014; Amabile & Pratt, 2016; Rosing et al., 2018)

The question becomes, how is innovation beneficial to the employee? The relationship between innovation and well-being has presented contrasting findings (Dolan & Metcalfe, 2012; Gonzalex-Roma & Hernandez, 2016). This presentation aims to establish under what conditions innovation as an integral process beneficially changes well-being. Using the DSEM, we demonstrated that in an environment with servant leadership, employees are more able to implement their creative ideas which over time improves their wellbeing. Further, we saw that the case was the reserve when employees are not implementing their ideas.

This book is for data scientists, data analysts, and data enthusiasts looking for a practical guide to building and deploying robust machine learning models using DataRobot. Experienced data scientists will also find this book helpful for rapidly exploring, building, and deploying a broader range of models. As such, it enables readers:

• get well-versed with DataRobot features using real-world examples;
• use this all-in-one platform to build, monitor, and deploy ML models for handling the entire production life cycle;
• make use of advanced DataRobot capabilities to programmatically build and deploy a large number of ML models.