When dealing with artificial intelligence (AI), sometimes we only recognize the efficiency and accuracy of the decision-making system. We fail to identify the untold struggles of AI implementations at the other end of the spectrum. As a result, companies invest too much in their ambitions and end up with an underwhelming ROI. Sadly, this is a scenario that many companies experience when going through the process of AI implementation.
After reviewing the causes of a poor ROI, including inefficient AI systems, delayed product launches, or any other shortcomings regarding AI implementation, the common factor that is exposed is usually bad data.
Data scientists can only do so much. If they are presented with inadequate datasets, they won’t recover any helpful information. Often, they have to work with data that is unusable, inaccurate, irrelevant or all of the above. The cost of bad data quickly becomes evident financially and technically once the information has to be implemented in a project.
According to a survey by TechRepublic that focused on managing AI and ML, bad data caused 59% of the participating enterprises to miscalculate demand. Additionally, 26% of the respondents ended up targeting the wrong prospects.
This post will explore the consequences of bad data and how you can avoid wasting resources and generate a significate ROI from your AI training phase.
Let’s get started.
What is Bad Data?
Garbage in Garbage Out is the protocol followed by machine learning systems. If you feed bad data into your ML module for training purposes, it will deliver bad results. Inputting low-quality data into your system puts your product or service at risk of being flawed. To further understand the concept of bad data, below are three common examples:
- Any data that is incorrect – for instance, phone numbers in the place of email addresses
- Incomplete or missing data – if crucial values are absent, the data isn’t useful
- Biased Data – the integrity of the data and its results are compromised because of voluntary or involuntary prejudice
Most of the time, the data that analysts are presented with to train AI modules is useless. Usually, at least one of the examples from above exists. Working with inaccurate information forces the data scientists to spend their valuable time cleaning data instead of analyzing it or training their systems.
A State of Data Science and Analytics report reveals that nearly 24% of data scientists spend up to 20 hours of their time searching and preparing data. The study also found that an additional 22% spent 10-19 hours dealing with bad data instead of utilizing their expertise to build more efficient systems.
Now that we can recognize bad data let’s discuss how it can get in the way of reaching your ambitions with AI.
The Consequences of Bad Data on Your Business
Let that sink in.
Not only are you wasting money by paying a highly qualified professional to do data entry, but the duration required to train your AI systems also gets postponed because of the lack of quality data (your projects take 40% more time to complete). Delivering a quick product launch is entirely off the table, giving your competition a competitive advantage if they efficiently utilize their data scientists.
Bad data isn’t just time-consuming to deal with. It can drain resources from a technical perspective as well. Below are some significant consequences:
- Maintaining and storing bad data is expensive regarding time and cost.
- Bad data can drain financial resources. Studies reveal that close to 9.7mn is wasted by businesses dealing with bad data.
- If your end product is inaccurate, slow, or irrelevant, you will quickly lose credibility in the market.
- Bad data can inhibit your AI projects because most companies fail to recognize the delays associated with cleaning inadequate datasets.
How Can Business Owners Avoid Bad Data?
The most logical solution is to be prepared. Having a good vision and set of goals for your AI implementation ambitions can help business owners avoid many issues related to bad data. Next is to have a sensible strategy to break down all probably use cases with AI systems.
Once the business is prepared correctly for AI implementation, the next step is to work with an experienced data collection vendor like experts at Shaip, to source, annotate, and supply quality, relevant data tailored for your project. At Shaip, we have an incredible modus operandi regarding data collection and annotation. Having worked with hundreds of clients in the past, we ensure your data quality standards are met at every step of the AI implementation process.
We follow stringent quality assessment metrics to qualify the data we collect and implement an airtight bad-data management procedure using best practices. Our methods will allow you to train your AI systems with the most precise and accurate data available in your niche.
Book a one-on-one consultation with us today for accelerating your AI training data strategy.