4 steps to digital transformation in the workplace

Let’s dive straight in.

1. Augmented Intelligence

Organisations should not wholly seek to replace human decision making with algorithms and data; rather, they should strive to use augmented intelligence.

Augmented intelligence refers to the use of data-driven insights and recommendations to assist humans to make improved decisions. Clear guidelines are needed as to when to use data science, what data to use/not use and how to use data science. This clarity will also ensure the transparent and ethical use of data.

2. Holistic Approach to Data Science

Organisations should focus on developing a holistic approach to data science. This includes a combined application of applied mathematics, technology, business acumen, design thinking and problem solving for maximum benefit.

These should not be treated as separate skill sets. Design thinking approaches, such as Human-Centred Design (HCD), help bring empathy into the data science discipline.

Design thinking, problem-solving and business acumen also help to bridge the gap between analysts and executives, as they enable them to speak a common language. Universities also need to align their courses to develop students’ skills to ensure graduating data scientists are industry-ready.

3. Transparent Solutions

Organisations should focus on developing solutions that can be explained and backed by human judgement. Using descriptive, diagnostic, predictive and prescriptive analytics in tandem helps create transparency and achieve better business outcomes.

Visualisation helps businesses understand their data and data-driven decisions better. Exploratory data analysis helps explain the drivers used in data science models. Further, it also helps ensure that the drivers explain causation (and not just correlations) that do not make business sense.

4. Experimental Mindset

When embarking on the data science journey, companies should adopt an experimental mindset rather than a big bang approach.

As Whit Andrews advised, ‘Don’t fall into the trap of primarily seeking hard outcomes, such as direct financial gains, with AI projects. It is advised that data science projects should begin with a small scope, such as process improvements or customer satisfaction.

Data scientists need a childlike curiosity to ask the right questions from the data and experiment with various possibilities.

Additional Resources

Gartner. (2018, 13 February). Garner says nearly half of CIOs are planning to deploy artificial intelligence [Press release]. Retrieved from https://www.gartner.com/en/newsroom/press-releases/2018-02-13-gartner-says-nearly-half-of-cios-are-planning-to-deploy-artificial-intelligence

TEDxIEMadrid. (2017, 19 July). The most important skills of data scientists Jose Miguel Cansado TEDxIEMadrid (Video file). Retrieved from https://www.youtube.com/watch?v=qrhRfPY4F4w

If somebody tortures the data enough (open or not), it will confess anything.
Paolo Magrassi