Do You Have Good Data? 3 Tests To Determine The Answer
- Posted by GM, Digital Solutions
- On August 3, 2021
- Artificial Intelligence, Compliance, Data, Machine Learning, Results
Data is absolutely essential when it comes to measuring the progress of AI projects. Without trustworthy data, we don’t know whether we’re hitting key milestones or tracking towards our goals. We lose sight of how our investments are supposed to add value to the business and result in throwing money aimlessly at different problems.
Good data, on the other hand, gives us the metrics we need, to know if we’re moving in the right direction. It’s through data that we are also able to refine our hypothesis and keep tabs on the viability of our endeavors. Ultimately, high-quality data lowers AI project risk, improves predictability, and keeps us accountable to what our investments produce.
But how do we know if we have good data?
Although the question seems simple enough, it’s not. Many business leaders mistakenly believe that having “good” data means having a large volume of it. In reality, good data is that which is useful, usable, and available for AI projects. If your data doesn’t teach you more about the problem you are trying to solve, it’s not helping you achieve your goals.
With this background in mind, let’s explore three easy tests you can use to determine if the data you have will work for your AI ambitions.
#1 — Is Your Data Accurate, Reliable, And Well-Structured?
For data to be useful, it has to be accurate (of course). You must be able to analyze your data confidently, trusting that it has the potential to reveal valuable insights about your operations or customers.
If your data is fraught with inaccuracies and errors, you’ll either make poor business decisions or have trouble trusting your results. You won’t be able to test different prototypes or identify patterns without questioning the validity of your metrics. Overall, inaccurate data is unreliable, which significantly hinders your ability to increase predictability across high-risk projects.
Data also has to be well-structured for it to be useful. If your data requires major scrubbing and preparation before you can use it, it may not be worth the time or effort. As we’ve mentioned already, AI projects are inherently risky. You can’t afford to waste resources structuring data when you need as much capacity as possible to bring AI pilots to market.
#2 — Does Your Data Follow The Rules?
Leaders often overlook how important compliance is in the data management realm. If your data is sourced by companies that gather the information in illegal or unethical ways, you shouldn’t use it. And if you obtain data from people without getting their consent first, you could be putting your business at risk.
The consequences of getting caught for failing to comply with privacy laws far outweigh the benefits of using poorly sourced data. And this includes sourcing information from people outside of the United States. To be safe, always know the latest rules and data regulations in every market you’re in, and adapt your workflows accordingly.
#3 — Can You Process Your Data?
The third test you need to pass evaluates whether you have the IT capabilities needed to process your data. You must have the right architecture and tools to turn numbers into meaningful information. Otherwise, you’re just hoarding digital data that has no value to your business.
In the context of AI projects, proper architecture includes having repositories, pipelines, and visualization dashboards capable of handling any volume of data. Your methods of data collection and storage should be cost-efficient, allowing you to gather as much as you need without having to worry about the financial implications.
Your infrastructure should also be highly secure to keep bad actors out and configured so that only the people who need the data can access it. Sticking to these protocols minimizes privacy risk and ensures that your team has the freedom to process data effectively.
Maximize The Predictability Of AI Projects
Beyond validating the quality of your data, there are many other ways to maximize the predictability of AI projects. To learn more about how you can mitigate risk and set your organization up for AI success, download our new eBook today.