Ever wonder why an AI project fails before going live? Often the solution lies in the data: AI projects can be ruined by poor data quality before the first model is trained. Inconsistencies or the lack of records can make helpful data unreliable. Businesses should always review their data before they can move on to more complex models.
Why Poor Data Quality Creates Problems Early
Often poor data quality begins before the AI team gets involved in a project. Companies gather data from websites, apps, spreadsheets, customer systems, sensors and more. Sources may be in a variety of formats.
Teams that go together without checking information cause confusion. It takes longer for the developers to clean the records than the time required to create the AI system.
How Bad Data in AI Affects Model Training
Bad data in AI can impact a model’s ability to learn patterns. A model can’t tell whether a record is for a real event in the business or a typographical error. It reads and understands what it is told.
For a business, imagine if they wanted to determine if there were any customers who were likely to abandon their membership. The model might incorporate the wrong relationship between events and customer behavior if the customer records are wrong.
Poor Data Quality Can Create Biased Results
Bad data in AI impacts fairness. When some information isn’t available about some groups, the model can learn from a partial image.
Use a hiring model that is based on historical employee data. If the model has been affected by recruitment trends of certain groups in the past, it is possible that the model will reflect these future trends. If the fields are missing, the problem will be more difficult to identify since teams will not know what is missing.
Good data checks do not eliminate all possible biases, but rather they help the teams identify gaps prior to model training.
The Cost of Fixing Data Problems Later
When teams find poor data quality after development has begun, it costs more. Data preparation, model training, testing and validation may need to be repeated by developers.
A team may also need to adjust data pipelines following detection of errors in source systems. This can cause project delays and add to development costs.
Poor data quality can be avoided by early checks. Samples can be reviewed, and data rules can be defined by teams.
Building Strong AI Data Management
Clear ownership is the first step in effective AI data management. There should be someone who knows where each of these is from, who maintains it and how teams should use it.
There should be accuracy, completeness, consistency, freshness and duplicate record rules established for teams. The rules are to evaluate if data is suitable for AI application.
For example, a customer data set can require phone numbers, locations, and new account data. Teams have the ability to perform automated testing prior to sending new data into an AI pipeline
How to Improve Data Quality for AI Projects
Data quality is critical to AI projects, and it’s essential that it’s checked at all levels. Teams should determine what data sets are required for the AI use case, as well as look at their fields, values, formats, and relationships.
Teams must remove duplicate records, fix obvious mistakes, be careful not to introduce inaccuracies due to missing data, and record significant changes. They must also make sure that the cleaned data set is still a representative sample of the actual business landscape.
Once the initial model launches, data preparation should continue. Teams should monitor regularly due to new records bringing in new errors.
Poor Data Quality Can Damage AI Accuracy
Unreliable examples can result in poor prediction accuracy if the data quality is poor. May lead to false alarms, misclassifications, or poor recommendations.
For instance, a fraud detection model may not detect fraud activity if the amount is missing or invalid in a transaction record. If the sales data from the past has missing information that can’t be explained by a forecasting model, the model may perform badly.
Model results should be compared with trusted business records, team. They shouldn’t assume that it was the algorithm that caused every output to be unusual when they see an unusual outpu
A Better Starting Point for AI Projects
Before creating an AI model, teams should consider basic questions. Where was the data collected from? Is it correct for the business process? Are the team able to explain unusual records?
AI data management needs to be integrated with these checks and everyday data operations. Teams can develop validation rules, track data pipelines, and track changes over time.
Teams can sometimes detect poor data quality at the time of sampling, comparing sources and investigating unusual values prior to training.
It’s not about making all records perfect. The aim is to have the data reliable enough for the specific AI task. Each project will have its own set of data standards, and they should be determined by the decisions that the model is used to support.
Poor data quality can kill a project before it even gets to the training stage. Models are more reliable when supported by reliable data; tests are clearer when based on reliable data; results are more reliable when based on reliable data. It’s crucial for teams to verify their sources, eliminate key mistakes, and track data constantly. With proper preparation, rework can be minimized and confidence built in the output of AI systems. Kazma Technology can provide practical advice on serious AI projects for businesses.
