What is Data Quality?
Wikipedia defines data quality as “the state of qualitative or quantitative pieces of information. There are many definitions of data quality, but data is generally considered high quality if it is “fit for [its] intended uses in operations, decision making and planning.”
Here are some other definitions of data quality…
- “Fit for a purpose. Meets the requirements of its authors, users and administrators.” ( Peter Aiken, adapted from Martin Eppler)
- “Reliance on accuracy, consistency and completeness of data to be useful across the enterprise.” (Michelle Knight)
- Tools and processes used for parsing and standardization, generalized “cleansing,” matching, profiling, monitoring, and enrichment (Gartner)
Simply put, data quality is an assessment whether the given data is fit for purpose. High-quality data is likely to satisfy the requirements. It is all about ensuring that information is accurate and appropriate for consumption and meet the needs of data customers. The quality of data is determined by accuracy, timelessness, uniqueness, completeness, consistency, and validity.
Why is data quality important?
Well, data is becoming the backbone of every business operation. The quality of the information being gathered, stored and consumed during business processes can determine the success of a business. For example, data helps you know your customers, their lifestyle, their past purchases, their concerns and their budgets—based on which you can plan a solid marketing strategy. This way, data quality helps in decision making, productivity, marketing and compliance.
Data is just like your years of experience you can tap on whenever you need to know if a particular strategy works. But the quality of the data can be good or bad. If you have poor data quality, you won’t have actionable knowledge and not be able to imply that knowledge.
The Role of AI in Data Quality
So you must have understood the importance of data quality. High-quality data is essential to ensure accuracy and timely information to manage services. But getting high-quality data is quite challenging. Rigorous data profiling, efficient controlling system, robust data pipeline and accurate data gathering are highly required in this context. While all these systems are essential, it can be a time-consuming exercise and might affect the accuracy of data. That’s why the data experts are looking for new solutions for data quality. And machine learning or AI is one of them. AI (Artificial Intelligence) has an important role to play in data quality.
Let’s say a company called ABC Singapore wants to rectify its domains in official records as it is mentioned as AB or ABCS. It would be tedious and labor intensive tasks, plus there is a risk of human errors with manual data reconciliation process. That’s where machine learning comes in. The technology will scan all of the data in a matter of hours, and then autocorrect the things. This is just a small example of the role of AI in data rectification. From capturing data records, identifying duplicate records, detecting anomalies to granting access, AI can make a big difference when it comes to improving the quality of data. Many companies are leveraging AI for maintaining the quality of their data. GE (General Electric) uses AI to predict and machinery upkeep. GE’s Prefix software processes the historical performance data of the equipment. This performance data can be used to address various concerns when a breakdown occurs. Aviation sector uses GE’s Prognostics tool to determine the life of the landing gear in the aircraft. It also helps them predict maintenance schedule and minimize unexpected problems and flight delays.
Avanade, a joint project between Accenture and Microsoft, helps insurance companies like Pacific Specialty to get more insight and perspective. “Data has a direct impact on your bottom line, so having clean and accurate data is more critical than ever. AI is already playing a huge role in data quality with automation and will continue to do so.
How AI Can Benefit Data Quality
Automating Data Capture:
A study by Gartner has found that $14.2 million are lost annually due to poor data capture. AI helps in improving data quality by capturing the data automatically. This way, it assures that all the required information is captured without leaving any gaps in the system. Good thing is that the process requires no manual intervention. If the crucial information is automatically captured, workers can focus on the core competencies of the companies.
Tracking Duplicate Records:
While duplicate records aren’t necessarily a poor piece of data, it can cause outdated entries and improper records that in turns affect data quality. The manual identification of duplicities is cumbersome and stressful. The system should automatically identify duplicate data. That’s where AI comes in. AI can identify and eliminate duplicate records in the database. For example, Sales force CRM is equipped with smart functionality that makes sure that your business accounts and database is free from duplicate entries.
A simple human error can drastically undermine data quality and the usage of the data in your CRM. What if someone forgets to add zero? Or the year or month in data format is incorrect? Such anomalies can be identified and rectified by AI in your database.
Making Way for Third-Party Data:
Apart from rectifying and maintaining the quality of data, AI can enhance data quality by adding to it. Third-party units can add value to the variety of the management system and MDM platforms by offering better and more complete data, which leads to precise decision making. AI can suggest what to get from a specific set of data and the established connections in the data. Having comprehensive and clean data in one place helps you make informed decisions (that are based on data). For more details regarding the AI Benefits you can Read this
How to Maintain Data Quality for AI
(In other words, AI NEEDS RIGHT DATA TO IMPROVE DATA QUALITY) While AI can automate and streamline the data quality process, the technology itself needs accurate data to operate on. Your AI system needs data to perform.
- You must have used FIND And REPLACE feature in MS Word.
- To use it, you need to fill it up with the sentences you want to search across the document. Right?
- If there is an error in your word or sentence, it won’t be able to find the desired one.
The biometric system, which recognizes the certain traits of an individual using the algorithm and biometric data like fingerprint scan, also needs the right data. If you enter employee B data for employee A in this system, it would be problematic for both employees at the points being equipped with AI.
The point is that you should use valid and accurate data in your AI system so that it can work over data quality efficiently. Simply put, you should train the machine with the correct data. Feeding incomplete or inaccurate data can lead to disastrous results in your AI-enabled models. To produce high-quality data for your AI programs, efficient annotators are required to label the information to be used with your algorithm carefully. When we talk about quality training data, we’re talking about both the accuracy and consistency of those labels. Accuracy is how close a label is to the truth. Consistency is the degree to which multiple annotations on various training items agree with one another.
Here are some quality controls that an organization can incorporate to ensure high-quality data for their AI system.
Using Standard Quality-Assurance Methods:
Generally, organizations that are developing high-quality training data sets utilize three standard methods for ensuring accuracy and consistency: gold sets, consensus, and auditing. Gold sets, or benchmarks, determine accuracy by comparing annotations to a gold set or screened example. This step helps to determine how well a set of annotations from a group or individual meets the benchmark.
Consensus determines consistency and agreement amongst a group. It works by dividing the sum of agreeing on data annotations by the number of annotations. It is one of the most common methods of quality control for AI. As the name suggests, this method aims for arriving at a consensus decision for each element. And disagreement is generally sorted out by an auditor. In auditing method, the quality of training data determines the accuracy by reviewing the labels either by checking on the spot or by assessing all. This method is essential for projects where auditors review the content unless it gets the highest level of accuracy.
Advanced Quality Assessment Measures for Various AI Projects
Sometimes, AI projects are too complicated and advanced to be handled with the abovementioned basic quality assurance methods. In this scenario, organizations have to look for unique quality assessments to customize the way to a particular initiative. Here are some of them…
Multi-layered Quality Evaluation Metrics
This method is used to capitalize on the methods of baseline quality measurement mentioned above. It ensures the highest accuracy level in quick time possible.
Weekly Data Deep Monitoring Process:
In this method, a project management team is deployed to assess the data every week as well as boost productivity and quality score. For example, if you need 80% accurate data, the goal can set at 90%.
Management Testing and Auditing:
Project managers can be deployed to carrying out annotation work and quality audits. This step will let your management team view the project from 360-degree and sit them on the top.
So you must have understood the relation between Artificial Intelligence and data quality. You must have also understood the role of AI in data quality and how it can be used as well. Data is the lifeline of your business. From getting market insight, identifying customer’s needs to locating issues, relying on the right data helps you make informed decisions. The role of AI in data quality is critical as it manages the process and improves the quality of data as well. However, it is important to train your AI system with the right data sets. It will improve your bottom line. With AI in place, you can enjoy all the advantages such as predicting trends, finding new opportunities, and resolving business queries.
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