Great business vision can help a hospital chain look at a particular aspect of the business and say "can we look at patterns in how patients react post-discharge?"

Or a retail organization to say "can we look at data to optimize our warehouse inventory in Hoboken, NJ?"

Or say an insurance company might look at analyzing patterns in claims in a particular location.

All of these cases are specific, potent business visions, and solutions to these questions can change the way they conduct business.

However, without high quality data, no AI/ML model will be able to lead businesses to the promised land.

An Alegion survey reports that nearly 8 out of 10 enterprises currently engaged in AI and ML projects have stalled.

And that 81% of the respondents admit the process of training AI with data is more difficult than they expected before.

According to a 2019 report by O’Reilly, the issue of data ranks the second-highest obstacle in AI adoption.

Gartner predicted that 85% of AI projects will deliver erroneous outcomes due to bias in labeled data, algorithms, the R&D team’s management, etc.

The data limitations in machine learning include but not limited to:

Data Collection: Issues such as inaccurate data, insufficient representatives, biased views, loopholes, and data ambiguity affect ML’s decision and precision.

Data Quality: Since most machine learning algorithms use supervised approaches, ML engineers need consistent, reliable data in order to create, validate, and maintain production for high-performing models. Low-quality labeled data can actually backfire twice: during the training model building process and future decision-making.

Efficiency: In the process of machine learning project development, 25% of the time is used for data annotation. Only 5% of the time is spent on training algorithms.

So businesses need their data to be labeled, annotated, classified as datasets, structured to run training models quickly.

And only when the training model shows promise, can they invest more time and money on building an actual project.

It is unfair and ineffective to ask data scientists to label, annotate, and clean data.

That's why we built Traindata Inc., to help organizations prepare your data through labeling, annotation, structuring, and cleaning at affordable costs.

Talk to us about your AL/ML data training challenges today, or visit www.traindata.us to learn more.