90% of the worlds data has been created in the last ten years, with that number increasing exponentially with Raconteur suggesting that by 2025, 463 exabytes of data will be created every single day.
90% of the worlds data has been created in the last ten years, with that number increasing exponentially with Raconteur suggesting that by 2025, 463 exabytes of data will be created every single day. That’s both a huge opportunity but also a challenge for organisations to harness it, gain insights from it and ultimately use it to better serve customers and impact the bottom line.
How do organisations know where to start? And how do you prevent data becoming ‘dark data’ which you just store (at a significant cost) and derive no insights or meaningful decisions from? Furthermore, once you have identified your data sets, it’s not like drilling for oil where just finding it creates value, you must dive into your data, analyse it and make sense of it. You are also attempting to get these insightful analytics all whilst running a business area, developing new product offerings, servicing customers, assessing cost save opportunities and pivoting revenue models in light of the pandemic. Who are the superheroes who can help you do all of this? Enter data scientists.
Data science encompasses everything from data sourcing, cleaning and analysis, feature engineering, modelling, and prototyping to full blown engineering. The right data scientist can help you understand big data, machine learning and artificial intelligence. You may ask yourself why are many of these terms and their real-life use cases for financial services still mysterious to many?
Firstly, because there is a real art in explaining the ‘so what’ of data science and making it real and relevant to specific situations. This is something we are passionate about at Delta Capita. The goal of our data science team is NOT to confound our clients with terms and lecture them on ‘art of the possible’ use cases, but instead to continually demystify the different terms and to make them real and relevant to actual client problems and opportunities.
And secondly because it can require a significant amount of time and effort to learn about an entire new technique in enough depth to be able to think around it and understand its viable applications. Couple this with phenomenon of some individuals feeling ‘too senior to learn’ and its unsurprising that some of the more advanced tech is underutilized. In my opinion, this mindset is a flawed one anyway, because in any space linked to digital such as data, cyber, technology if you’re not learning everyday then you are already out of date.
Even if your day to day doesn’t fundamentally require in depth knowledge AI, you may just find knowing a bit more about it is enriching to unexpected conversations. We also know at a societal level it is being implemented in more and more of our everyday lives. When LinkedIn offers smart replies to a message, when Amazon makes a product recommendation to you, when Alexa remembers the specific version of a song you want to play and when uber matches you with your driver. The ability to question AI systems and fully understand why certain predictions are being made will become increasingly important to diminish theconcerns around AI ethics. Given that AI is becoming progressively and silently embedded into many of our interactions, why not learn a little more about it?
If you want to chat more about Big Data, AI & Machine Learning and real-life applicability in Financial Services, please email us at firstname.lastname@example.org. Our data science team would love to have a conversation about how it is used in financial functions such as KYC, cybersecurity, fraud detection, risk, regulatory compliance, and customer experience. We are also passionate about model explainability and inherent bias and trust challenges so we can help you navigate your data science journey.