Humans have inherent flaws in the way that they make decisions. Data Science is not affected by these flaws and can be used to help humans make better or quicker decisions. Data Science can be difficult to comprehend: how can we simplify the outputs without introducing bias?
Keynote Presentation: Performance Enhancing Development in Data Science Organizations: Tips to Improve Productivity, Efficiency, and Results
Effectively running a data science organization takes more than the knowledge of generative adversarial networks or knowing the ins and outs of Spark. For data science organizations to run successfully, they must have efficient relationships throughout the organization, utilize well-integrated systems/tools, and be constantly finding ways to increase efficiency, productivity, and results. The data science… Read more.
Keynote Presentation: Putting the ‘Chief’ into ‘Chief Data Scientist’ – The Evolution of the CDS Role
Where we came from, where we are, and our future landscape. Using analysis to improve performance. Developing what we’ve learned into actionable recommendations for sustainable data science.
The first 90 days and beyond – identifying short terms wins and long term goals Structuring for success – what form should your function take for best results? Cementing your place within the C Suite – how can you avoid the perception of being a short-term, fashionableappointment?
What impact does the reporting line of a CDS have on the role? To achieve the greatest success, should a CDS report to the CEO, CMO, CIO, or elsewhere? Are we seeing a convergence as to where CDSs should report, or do different organizational needs produce different optimal structures?
How can we best enable disruptive innovation through the use of Machine Learning? What innovative approaches can be used to identify the right data-set with the right signals to train algorithms? Taking off the training wheels – assessing progress on unsupervised machine learning.
How can data science increase the impact of marketing, and what is realisitically achievable? Sharing examples of successful collaboration between data science functions and marketing teams. Being seen as an ally, not a threat – how can we overcome territorial stance?
How can internal educational programmes enable self-service Data Science? Understanding how to speak the right language and translate the power of Data Science to various business lines. Identifying and empowering internal advocates or ‘friends of data’.
Creating transparency through incorporating textual analysis. Utilizing “more data” to make better decisions – avoiding simply expanding the haystack. Improve high volume decisions with your tool bag.
Creating new winning products based on business goals and meeting customer demand. Developing and improving current product offers by applying data science to generate insights. Creating an internal culture of no-fail, experimentation, and testing.
How can a Chief Data Scientist explain their findings to non-data literate colleagues? ‘So what?’ – Moving from sharing insight to promoting action. Not just a flash in the pan – solving business problems to ensure the longevity of data science.
Discerning which team members are needed for your data science team. Engaging the best minds in the organisation for the greatest impact. Managing data for organisational concerns while not losing focus on the science.
Applying the scientific method in a business context – allowing for experimentation and “failure”. Perfection Impossible – what level of confidence do we need for our conclusions to be robust? Overcoming the HiPPO issue – following results, rather than the ‘Highest Paid Person’s Opinion’.