Businesses are rushing to implement artificial intelligence left and right. From customer service chatbots to predictive analytics, there’s a lot AI can do to help businesses grow. However, there is one thing that can derail any project before it gets off the ground: bad data.
When it comes to AI, data is everything. Businesses often assume that once they implement an AI platform, the technology will figure things out. However, AI is only as good as the data behind it.
It’s difficult for businesses to conceptualize the importance of high-quality data due to the sheer quantity and variety of information they can collect. Business leaders need to understand the importance of data quality to ensure the best possible outcome. Properly filed, clean, timely data is pivotal.
Why Data Quality Slips
It’s important to note that AI doesn’t create anything new out of thin air. It doesn’t create knowledge; it’s a culmination of knowledge that already exists.
The output is only as good as the input it’s fed. If an LLM (large language model) is fed data that is largely inaccurate, incomplete, inconsistent and irrelevant, it will create an AI program that is all those things. This is why data must be in pristine condition before building can begin.
Poor data is often submitted due to human nature. Humans aren’t naturally detail-oriented, especially when something becomes routine. Humans are inclined to take shortcuts with repetitive and tedious tasks. If a task doesn’t feel meaningful in the moment, we find ways to quickly complete it — and accuracy slips.
Another reason is a lack of education. Oftentimes, people will submit data believing it’s up to par, but it’s actually missing key components or filed incorrectly. Additionally, people often input data thinking it’s timely and relevant when, in fact, it’s not.
Characteristics of High-Quality Data
For AI to work effectively, the data must meet a few nonnegotiable standards.
- The data must be accurate and represent facts. For example, in a senior care community, if a patient’s discharge date or diagnosis is wrong, the AI could potentially make false assumptions about the number of available beds. False entries can distort the entire model.
- All necessary fields and records must be completed. Missing fields can cause gaps in AI-driven planning or reporting.
- All data needs to be relevant. The data being collected should directly support the AI’s purpose. It’s important to not clutter AI models with noise; this can make the outcome messy. Developers should use only meaningful inputs and make sure the rest is cut.
- Timeliness is also an important factor. It’s important to remember that AI isn’t going to do the work completely. It’s meant to work with people and the systems already in place. If the data isn’t being updated or is outdated, the outputs can be irrelevant or misleading.
- Consistency is also pivotal. The same type of information should always be recorded in the same way, whether it be currency symbols or phone number formats.
Steps Companies Should Take Before Implementing AI
Before anything is built, businesses should have an introductory meeting with the software company and outline the initial steps. This will build structure and trust and set the project up for success.
- Establish data standards. Businesses should work with the software company to define exactly how they want the data to look and how it should be filed.
- Clean historical data. Businesses should take care to not get stuck training an AI system filled with inconsistencies and duplicates. This will make the process much more frustrating and can lead to a lot of wasted time.
- Educate the entire business. It’s important the businesses train all employees on how their daily contributions affect AI performance. For example, if a hospital inputs bed vacancies, all employees must know how the model can mess up and be catastrophic if that data isn’t input correctly.
AI success starts with clean, well-managed data. If input is flawed, the output will be, too. If businesses focus on data quality from the start, AI systems can be beneficial and scale businesses in incredible ways. The data just needs to be there for that to happen.
Ronak Bhavsar is the CEO and co-founder of Prama, which collaborates with businesses worldwide to develop AI platforms and products that drive business growth. Bhavsar has more than 15 years of experience in the IT industry.
