3 Steps To Data Preparation for Artificial Intelligence (AI)

In 2021, 86% of executives said that artificial intelligence (AI) will be mainstream technology at their companies - put to use on everything from commerce to customer service - and that number is sure to grow. If you're considering bringing AI into your organization, you need to have the right tools and mindset in place. Once the team is onboard building trust and engagement, you're ready to move forward with the right data preparation for AI. This three-step guide will help you evaluate and prep your data for strategic results.

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1. Make your data available, accessible, and aligned

Assess the best use cases for your data. Your organization should ask:

• Is your data available? Do you know where the data is?
• Is your data accessible? You likely know which systems contain your data. Can you access it both for model training and scoring?
• Is your data aligned? Do your data assets line up with the use cases you want to solve for?

When you can answer "yes" to all these questions, it's time to analyze whether your data has predictive potential.

2. Set goals for your data

Determine the outcome variable you'd like your data to accomplish. What exactly are you trying to predict? Quite often, it's an area of high value to the business, but a metric that's not being tracked.

Think about the most business-critical questions your sales team wants answered to drive topline revenue. Are they looking at the propensity for a customer to repeat a purchase within a timeframe? Or for a discounted quote to be approved at their first attempt? Each of these requires different approaches to AI.

To determine the best approach, we recommend labeling data accurately from the start. Even if your data's not perfect, the best artificial intelligence tools will still be able to guide you into actionable insights.

3. Keep your data ethical and protected from bias

The third question relates to how suitable your data is when deploying a predictive model to your user base in the wild. This specifically relates to ethical AI. Data in predictive AI models is notorious for implicit bias if it's not addressed from the onset.

If you don't address equitable AI, the potential for negative future implications is huge. AI-powered systems can perpetuate bias by amplifying discrimination present in historical datasets.Take, for example, financial service firms migrating from rules-based systems to algorithm-based, automated decision-making in loan approvals. These models carry an increased risk of inadvertent discrimination due to patterns in existing data. Once you've established how to mitigate risk, you can put tools in place that will help you predict the next best action for your particular task.

IDC estimates that 40% of the top 2000 public companies in the world will need to reinvent their strategies for responsible AI by 2025. At that pace, it can be easy to get lost in the frenetic changes. By anchoring your pivots back to the basics of collecting, aligning, and protecting your data, you will be sure that new choices are grounded in a framework you can trust.

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