3 reasons why data scientists are important to companies

The future of data science

Data and analytics have become the key catalysts of organisations’ digital transformation efforts in the current volatile world. It’s been predicted that by 2022, 90% of corporate strategies will include information as a critical asset, and analytics will be seen as an essential competency. And by 2023, data literacy will become a necessary driver of business value, so the need for greater senior leadership at the data science level will become critical.

Still, there are companies that struggle to see the potential of big data and data science. And while many have recognised it, they find making the cultural shift, or committing to advanced analytics, difficult. These days though, having a data scientist shouldn’t be considered a luxury, especially as companies accelerate their digital transformation in a tricky pandemic-environment.

Having a dedicated data scientist who is an integral part of the business, and can extract the most valuable and relevant insights, will fuel business and digital transformation endeavours.

The future of data scientists

Data scientists are in high demand – in 2020 LinkedIn listed data scientists as the #3 position with an annual growth rate of 37%. That’s certainly good for them, but how can a data scientist make an impact on your business?

Three top reasons why a data scientist is beneficial to your company:

1. A data scientist who acts as a strategic partner to upper management improves decision-making by tracking, measuring, and recording performance metrics and assigning value to key performance indicators.
2. A good data scientist with a background in statistics and data modelling can identify opportunities and recommend actions that are based on current trends and will help improve your business’s overall performance, engage customers better, and ultimately increase profitability.
3. Your data scientist can improve audience targeting that increases conversion and profit margins by applying customer analytics from Google Analytics, customer surveys, and other data sources to demographic data, building concise personas and determining the optimal time and