Until recently, large financial services organizations on boarded data science teams, placed them next to the delivery organization and gave them tools for experimentation. It was as if every organization had to demonstrate for themselves that their data combined with statistical modelling had potential to generate business insights. When the derived insights failed to translate into actual, running models continuously delivering business value, it generated frustration among business leaders. The recent interest in applied machine learning tooling, including MLOps and DataOps, is a recognition that there is a commercial potential to harvest by remedying this executive frustration.
The lesson is that hiring a group of data scientists is no guarantee for commercial success. At minimum, they must engage in Applied Machine Learning and carefully balance any experimentation or research conducted outside that paradigm. Applied Machine learning is an engineering discipline aimed at deploying machine learning systems that solve particular real world problems.[1] Opposed to this stand Machine Learning Research, which experiments in the pursuit of better algorithms or explores the frontiers of industry application. The key principle of Applied Machine Learning is to think execution before experimentation — think of how to execute before what to execute.[2] …
10 times the value of Data Science?
Consider the experience when you join an organization as a data scientist in 2020: you become part of a team of like-minded professionals and have time to set up your own ‘lab’ at your desk with the help from your assigned buddy. A few days later your manager introduces you to your project, the team and its folders. The next milestone is a few months out in the future.
Now consider the experience when you join an organization as a data analyst in 2020: you become part of a team with lots of different profiles. Day one, your manager introduces your predecessor’s reports and asks you to work on updating the immediately as they are due by the end of the week. …
Imagine you own a café and your ambition is that customers leave your establishment with their batteries recharged, stress released or full of optimism for the day. That is what you are selling.
However, on the menu you have coffee, tea, homemade yoghurt, chia biscuits — perhaps even yoga and massage. That is what you are also selling.
So when do you use the first list (battery recharge etc.) and when do you use the second list (coffee etc.)? And what has this got to do with a data platform?This short article describes how the understanding of the data platform products and services developed during the first 1,5 years of the data platform’s life in Danske Bank. The main take away is that more than one level is needed: both a detailed practical catalogue and high-level utilities or capabilities. …
A Centre of Excellence can be a major accelerator for data science & AI to become a natural part of banking enterprise-wide and thereby pave the way for digital transformation. read.how.
Data science and AI in banking
By now, writing in 2020, all major banks have run numerous experiments with data science and AI for years. Most have run virtual assistant experiments, investing considerable effort to deliver an experience that is more ‘human like’ than regular chatbots can provide. Most have built data science teams as part of innovation hubs or departments with a more traditional structure.
Success stories are beginning to surface. Nonetheless, common for transformative initiatives of this type is that in banking, they stumble into the challenge of having to integrate with structures built before data science and AI[1] became part of common vocabulary. Even recent digital applications in the banking sector are often not analytics friendly either: They come in combinations of cloud-based, social and mobile, but do not invoke advanced analytics. Between the infrastructure transition to cloud and the change of user interface to mobile, the world of banking is still very traditional in regards to data structures, processes and mind-sets.
This will change over the coming years, and not just organically. There are major transformative efforts in all areas of banks and capable, determined leaders, which will transition the banking ecosystem towards being much more analytics-friendly and make data science and AI differentiators in the industry. Customers are also demanding new agile digital services and products from their banks. …
