Unleashing the Power of Data in an AI World

If AI is the engine, data is the fuel. Artificial intelligence (AI) and predictive analytics are powering faster, better decisions in industries as diverse as vaccine development and viticulture. Discovering previously hidden patterns and connections helps to boost profits, reduce business risk, match supply to demand, and benefit various industry verticals.

But if the AI model is trained on subpar data – too sparse, not diverse, biased, or of poor quality – it can make erroneous predictions that cause harm to the business, individuals, and society.

Our whitepaper is intended for executives and technology leaders in the private and public sectors. It covers some of the most common data mistakes that organizations make when adopting AI and presents seven steps to get data right, along with relevant case studies.

Common data mistakes when adopting AI

The accuracy of predictions from AI depends on the quality of the data used in the modeling process. AI without good data is like an engine without fuel. It gets you nowhere. In our work with companies around the world, we commonly see the following mistakes around data that lead to erroneous predictions:

1. Too small a data set
2. Non-diverse sample
3. Confirmation bias
4. Unpredictable circumstances (owing to no one’s fault)

Seven steps to steer clear of data mistakes

1. Having clear business objectives
2. Establishing a shared dictionary for business teams and data scientists
3. Identifying relevant datasets
   • Augment internal data sources with relevant third-party data.
   • Be granular.
   • Avoid bias.
   • Ensure the data reflects the diversity of the population.
4. Looking for skewness and outliers
5. Data cleansing and protection
6. Running the model and adding human brain power
7. Validating the model with goodness-of-fit tests

Download the complete whitepaper.

AI is helping to positively change business, government, education, and quality of life. But created by humans, it can also inherit our flaws and biases. To mitigate that risk, pay attention to the data you use to train your models.

Harness the power of data and AI to make your organization truly data-driven.