Does data need science?
There is a huge skills gap in the data landscape. Europe needs 346,000 more data scientists in 2020 for the 47% of EU organisations struggling to fill data science roles.*
Financial Services is primed to take advantage of what data science can deliver in a wide variety of use-cases like risk analytics, customer behaviour analytics, fraud detection, customer data management, algorithmic trading, natural language services – to name just a few.
However, when an estimated 70% – 85% of data science projects fail, only 20% of analytics insights deliver business outcomes, and 80% of AI projects “remain alchemy, run by wizards”, it is clear that data science is no guarantee of business success.**
Often the problems faced by organisations don’t need data science to solve. Just because your business holds unstructured data, does it need to be analysed? When Data Scientists spend up to 80% of time on “data cleaning” in preparation for data analysis, statistical modelling, & machine learning it is clear that data science/analytics aren’t yet ready for self-service at the vast majority of companies.
Companies who find success with data science have a very clear idea of what they want to do with data. They think very hard about how to collect it, how to store it, how to process it, what kinds of analyses they want to perform on it, what they want to do with the results, and how to communicate the results to stakeholders.
There is a wide world of data professionals who understand that focus on the data ‘fundamentals’ i.e. quality, structure, accessibility is essential to business outcomes. Many organisations are missing these fundamentals.
So, in an environment where the skills are difficult to find and expensive to hire, ROI is hard to realise, and the data to be studied is dirty or non-existent – Does data really need science?
*Oreilly Media, 2019, Evolving Data Infrastructure
** Gartner, 2019, Gartner Top Strategic Predictions for 2020 and Beyond
Come to the Data Kitchen to discuss:
• What does Data Science really mean in the Financial Services context? Is there a common definition?
• How does data engineering improve the speed and quality of data scientists’ work?
• Are there best practices to delivering ROI in data science in Financial Services?
• Are traditional data roles being superseded by ‘scientists’?
Why should you come?
Learn from experienced FS practitioners about how they have delivered business initiatives with data as an asset
Share ideas on how you can position yourself in the dynamic world of data
Network with like-minded people from FS, Fintech, VC’s, and Data-Innovators and gain insights on career paths
Whether you are a recent graduate, a businessperson, a technologist, a data scientist or just work on data initiatives, come along to meet like-minded people.
Introducing The Data Kitchen
Food | The Kitchen is where people gather to eat, drink and spend time with family. But it is also a place of work. You can do both at the Data Kitchen.
Data | The Data Kitchen is a community of people interested in, or working with, data in Financial Services. Every event focuses on a different theme related to data as an asset or data as a liability and related innovative topics within Financial Services.
Community | We aim to provide a warm, conversational atmosphere to share ideas, learn and network over drinks and hot food. Our events are free and open to everyone.
Insight | The events are a great opportunity to listen to fascinating insight on the modern developments in data-innovations, AI, DLT in the industry through experiences of FS and Fintech professionals.
Tickets are available on a 1st come, 1st served basis.
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