Artificial Intelligence (AI) and Machine Learning (ML) technologies have the potential to drastically revolutionize many industries. But AI and ML have an Achilles heel that few people talk about. A study conducted by Refiniv in 2019 Smarter Humans, Smarter Machines: Artificial Intelligence / Machine Learning Global Study, revealed that the biggest barrier to the deployment and adoption of artificial intelligence and machine learning is bad data quality. Data from alternative resources and unstructured data is becoming increasingly important but must be “refined” before their insights become truly valuable for use.
The saying “garbage in, garbage out” applies to AI/ML deployment–if you give the models bad data, the analysis and results will be sub-par, too. According to the Refiniv Survey, 66 percent of respondents said that poor data quality affects their ability to deploy machine learning and artificial intelligence technologies. The report also suggests that three of the four challenges of working with new data in ML models are related to data quality. These challenges include accurate information about the history, coverage, and population of data, identifying incomplete or corrupt records, and cleaning and managing data. One of the biggest challenges data scientists face is finding good quality data, as they have to spend 80-90 percent of their time cleaning and normalizing bad data.
'Data quality is extremely important when performing data analysis, regardless of whether it is to be used for artificial intelligence or not.' -Amy Groden-MorrisonClick To TweetWhy Is Data Quality Important?
Data quality is extremely important when performing data analysis, regardless of whether it is to be used for artificial intelligence or not. Data quality has two components:
- Missing data
- Incorrect data
Both issues are highly problematic, and the impact of each issue can only be determined on a case-to-case basis. If data quality is not sold in ML models, it leads to misunderstanding and wrong inferences. Research has demonstrated that companies analyze market data and unstructured data along with their own company data. This means that they are combining the three different data sources to gain insights. Traditionally, structured data has been the key to strong quantitative analysis. However, unstructured data is the main challenge for companies. Data from alternative sources is mostly unstructured and needs to be refined and validated for accuracy.
Machine learning approaches like natural language processing (NLP) are used to structure and refine text-based data. Facebook and Google have been focusing a great deal on unstructured data. Their success is making unstructured data easier, more accurate, and more effective. And even though ML has made extracting information from unstructured data sources easier, it is still a time-taking process, and it requires a lot of skill and patience to train ML models.
Mobile Apps: Missing Link to AI Interpretation
The best way to ensure that data is of good quality is to get it from a reliable source that’s easy to access. When it comes to trusted sources, using mobile apps can be one way. Mobile apps give you more control over data quality than traditional paper forms that many organizations still use, and you can easily access digital data whenever you need it.
Mobile apps are key to artificial intelligence implementation as they can improve data quality. Traditional data comes from paper-based processes, which are often prone to manual errors. If the data quality is bad, your artificial intelligence will suffer too, not to mention lost information or time delays you will face with paper forms. Replacing these processes with mobile app-based digital forms will eliminate errors and improve data quality. Mobile apps can automatically capture information like time, location, and data and even validate calculations, digital signatures, barcodes, and readings. In particular, mobile apps that collect field data are critical to successful AI implementation when field data is used as a key data source for the model.
The Real Costs of Bad Data
We may not realize it, but bad data can cost a lot of money (as much as $10 per record). A report from the data quality company, “The Real Costs of Bad Data,” notes that up to 20 percent of the information gathered by staff is incorrect. The report suggests that verifying information can cost up to one dollar per record. This money goes towards paying employees, the cost of running computers, and using a validation solution.
However, the one dollar per record may seem misleading as the costs go significantly up if batch processing is used for validation. Then the costs will rise to $10 per record, and even that figure is underestimated if the company doesn’t have mechanisms in place to check records. It may amount to $100 per record due to returned mail, misplaced shipments, and lost marketing opportunities. This means you will lose revenue and have to spend enormous amounts of money on the shipping process. Simply put, bad data not only costs money to refine and repair but also causes a loss in revenue because of the company’s inability to deliver to customers and reach potential ones.
The best way to minimize bad data is going paperless and digitizing all processes. You can save a lot of money by going paperless, improving productivity, and reducing the hidden costs of dealing with bad data. Building powerful apps will help your company save time and reduce costs. Paper-based processes take a lot of time and labor to manage when everything can be digitized with minimal human intervention.
Mobile App Builder
To make mobile apps that can facilitate your business processes, you will need the right app builder to build mobile forms for any mobile device and go paperless. For this, low-code development platforms can be ideal as they allow citizen developers to build enterprise apps. Many low-code development platforms can develop mobile-based forms in minutes with the latest mobile app features (like GPS, camera, etc.) to capture data accurately and quickly.
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- Artificial Intelligence
- Data Analytics
- Machine Learning
- Artificial Intelligence
- Data Analytics
- Machine Learning