Where to start with data science and AI methods
London Business School
Data science and AI offer a paradigm shift from trial-and-error, reactive, and approximate human decision making to evidence-based, proactive, and precise decision making processes. Nevertheless, senior leaders either don't know where to start, or worse, have been disappointed by the low return on investment associated with data analytics.
Develop an appreciation of what data science can and cannot do and organise your firm’s data science capability to become more competitive with Professor Nicos Savva through an application-driven, jargon-busting overview of the main data science methods.
The data-driven mindset
We all like to think data informs key commercial decisions. After all, the resources to do so are at hand: the amount of digital data is expected to reach 44 zettabytes in 2020, laptops have roughly 30 times more computing power than a decade ago and cloud computing usage is set to double in the next two years.
Still, something is holding businesses back. Too many continue to base their decisions on limited data, selectively interpreted to support management’s instincts or ambitions. It’s time for a new mindset, one that challenges old decision-making habits by continually asking:
- Are we making the most of the opportunities that data brings?
- Are we analysing all of the most important and relevant data that can inform decisions?
- Are we acting on the insights we generate?
How to use data analysis to challenge conventional wisdom and make better decisions.
Harvard Business Review
There is so much data available, but it is useless if you don’t know how to interpret it.
In this podcast Seth Stephens-Davidowitz explains how data can play an important role in challenging conventional wisdom and making better decisions.
He explains how to use data analysis to follow the data – wherever it leads.
Why data science matters and how it powers business value
It is becoming clear that there is enormous value in data processing and analysis—and that is where a data scientist steps into the spotlight. Here are some advantages of data science in business:
- Mitigating risk and fraud. Data scientists are trained to identify data that stands out in some way. They create statistical, network, path, and big data methodologies for predictive fraud propensity models and use those to create alerts that help ensure timely responses when unusual data is recognised.
- Delivering relevant products. One of the advantages of data science is that organisations can find when and where their products sell best.
- Personalised customer experiences. One of the most buzzworthy benefits of data science is the ability for sales and marketing teams to understand their audience on a very granular level.