In multiple domains, AI has made tremendous progress, surpassing human expertise. A classic example is how accurate it is than oncologists at predicting breast cancer. As per a prediction, by 2030, companies that adopt AI will experience doubled profit margins, and businesses that don’t embrace AI might encounter a 20% drop in cash flow over the same period.
But the real question is why isn’t everyone rushing to incorporate machine learning and artificial intelligence into their business initiatives, considering the benefits? While some people and organizations have already embraced the transformation enabled the best recommended AI company, it is essential to investigate why other individuals must also take a bold move. In this article, we will examine the importance and benefits of collaborating with an AI company, focusing on the compelling reasons for making this game-changing decision.
Vital Connection Between Data Quality & AI Performance
Let’s discuss the current state of AI and the successful versus struggling industries before diving into the particulars of how data quality contributes to the success of artificial intelligence.
Why should you care about data quality? Whether or not the results of the analysis will be used in AI, data quality remains an essential factor in any business journey. There are two aspects to data quality:
- Incorrect data
- Missing data
Miscommunication and incorrect conclusions can result from ML models with reduced data quality. Recent studies have shown that businesses examine:
- Internal company data
- External market data
- Other forms of unstructured data
This indicates that they are integrating information from all three sources. Strong quantitative analysis has historically relied on well-organized data sets. However, the biggest obstacle for businesses is dealing with unstructured data. The majority of alternative data is unstructured and therefore requires additional processing and verification.
Unstructured data has received much attention from tech giants like Facebook and Google. They are simplifying, improving, and optimizing the handling of unstructured data. Natural language processing (NLP) is one example of a machine learning technique used to organize and improve textual data. Even though ML has accelerated the process of extracting details from unstructured data sources, training ML models still takes considerable time and expertise.
Learn Why to Prioritize Data Quality for Success
The top three reasons why an AI company prioritizes data quality are:
Role of mobile apps in AI
Obtaining information from a trustworthy and convenient source is the surest way to guarantee accuracy. Mobile applications can greatly enhance the quality of data used by AI systems as these apps may serve as a reliable resource when looking for information. They have greater oversight of data quality than paper forms, which are still used by many businesses, and this information can be accessed on the move.
Paper-based procedures hold a lot of past information, but they frequently cause human error. If the quality of data is poor, not only will your AI perform poorly, but you will also lose information and experience delays if you rely on paper forms. Errors will be reduced, and data quality will increase when paper forms are replaced with digital ones that are accessed via mobile apps. Some of the examples of tasks that mobile apps can automate include validating:
- Computations
- Digital signatures
- Readings
- Barcodes
Bad information consequences
A firm might lose money due to inaccurate information. According to a report, up to twenty percent of data collected internally may be faulty. The cost of verifying each record may be between $0.01 and $0.10. Payroll, computer maintenance, and the cost of a validation solution all take a portion of this budget.
You should understand that the price will increase to $10 per record if the company has no record-checking systems. $1 per record might seem low, but using batch processing for validation can significantly increase those costs. The cost of lost mail, misplaced packages, and missed promotional opportunities could add up to $100 per record. As a result, you will see a significant drop in revenue and incur huge costs associated with shipping.
In other words, bad data not only results in a loss of revenue due to the inability to provide products to clients and reach potential customers but also consumes funds to refine and repair. Going paperless and digitizing all processes is the best way to reduce inaccurate information. You can save a substantial amount of money by eliminating paper, increasing efficiency, and decreasing the hidden costs associated with incorrect data. Thus, your business can save money and time by investing in the development of powerful apps.
Mobile app creator
With the right app creator, you can create mobile forms that can be accessed through any mobile device, eliminating paper from your business processes. Platforms for low-code development can be useful in this regard because they permit any programmer to create enterprise apps. With the latest features of mobile apps (GPS, camera, etc.), many platforms for low-code development create mobile-based forms in minutes.
An Ultimate Guide to Data Quality Mangagement
Many sectors gather vast quantities of data suitable for AI. A few examples are the retail, healthcare, pharmaceutical, and transportation industries. Tried and proven data quality techniques that contribute to AI success are:
Data profiling
Experts in artificial intelligence say data profiling, a relatively simple data analysis method, is essential for getting a handle on data before acting on it. The following can be learned by profiling a dataset:
- Statistical data
- Distribution values
- Formatting inconsistencies
These are crucial in determining whether or not the data set is usable and, if not, how to fix it.
Data preparation
Artificial intelligence (AI) and data science researchers are constantly adjusting data to better train AI. These users require simple instruments for performing tasks like attribute parsing, column transposition, and value calculation.
Data quality evaluation
With a centralized library of already developed data quality rules, you can quickly verify any dataset via the data domains. Rules for verifying email addresses, customer names, and internal product codes can all be reused with little effort. Some data, like address information, may require completing and standardizing it.
Data quality monitoring
Pre-calculating data quality for datasets, data scientists consider an excellent option. They can then examine each attribute in greater detail to learn its unique drawbacks before deciding whether or not to employ it.
The takeaway
The field of artificial intelligence is a dynamic one performing wonders in various sectors. Some sectors have the upper hand, while others are just starting to overcome the hurdles. Despite the fact that different businesses occupy different points on AI readiness, we can all agree that high-quality data is essential for any AI initiative. Keeping data in good shape improves your success rate and eliminates the need for extremely large datasets. Checking the quality of your data is an obvious first step if your company is making the transition to AI and machine learning. Get in touch with a professional AI company like Opporture in North America to achieve success in business by improving your data quality.
