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Why are so many businesses investing in AI, but neglecting the foundation of quality data?
AI-driven projects are the new rage these days – but businesses are quickly realizing that the success of any AI project hinges on one fundamental factor: data quality. Despite the excitement around AI, many organizations are hitting roadblocks because of poor data quality management practices.
With over 2.5 quintillion bytes of data generated each day, the need for reliable data quality management has never been more urgent. Businesses can no longer afford to ignore their data’s accuracy, consistency, and integrity.
Let’s explore why getting your data right isn’t just an option but a necessity for survival in the age of AI.
What Is Data Quality Management At Its Core?
Data quality management (DQM) is about ensuring that the data being used across the organization is accurate, consistent & reliable. At its core, DQM involves a structured approach to handling data throughout its lifecycle from acquisition to distribution.
Here’s the definition of data quality management from Techopedia:
“Data quality management is an administration type that incorporates the role establishment, role deployment, policies, responsibilities and processes about the acquisition, maintenance, disposition, and distribution of data.”
In practice, data quality management involves people, processes, and governance. The goal is to create a system where data flows smoothly between departments, is accessible to those who need it, and remains trustworthy no matter how often it’s processed or transferred.
Consider this example: A large insurance company initiates an AI project to improve fraud detection and personalize customer services. The project’s success depends on analyzing comprehensive customer data. However, they face a significant obstacle. Customers have multiple profiles across different systems due to legacy data and past mergers. One customer might exist under several IDs, with fragmented information spread across underwriting, claims, and customer service databases.
This duplication hinders the AI algorithms from accessing a complete and accurate view of each customer. In the same way, multiple records for the same individual increase the risk of data breaches. Disparate systems may have inconsistent security measures, making it difficult to enforce data protection policies uniformly. This fragmentation exposes the company to compliance violations and potential legal penalties.
By implementing effective data quality management, the company consolidates duplicate records into single, accurate customer profiles. They standardize data formats, validate information, and establish governance protocols to maintain consistency across all departments. This enables the AI system to function correctly and strengthens data security and regulatory compliance.
The data must meet the needs of the business context in which it’s used. What’s fit for a marketing campaign might not be fit for financial reporting, and DQM ensures that data is treated with the right level of care based on how it’s being utilized.
What Causes Low Quality Data?
Companies gather data through various means such as online forms, website entries, surveys, social media interactions, and customer feedback. This data is usually loaded directly into Customer Relationship Management (CRM) systems or databases without rigorous validation checks. Consequently, flawed entries like typos, incomplete fields, and duplicate IDs often get recorded into the database.
→ During Collection: The first stage where things often go wrong is data collection. This can include everything from human error during manual entry to technical glitches in automated systems.
Let’s talk about A global retail company developing an AI model to optimize its supply chain through demand forecasting. They collect sales and inventory data from various sources, including online platforms, physical stores across different regions, and third-party distributors. However, during data collection, they encounter significant inconsistencies as product identifiers vary between systems, some sales data is recorded in different currencies without proper conversion, and timestamps are in assorted formats and time zones.
Without proper data quality management, these inconsistencies feed into the AI training model, leading to unreliable forecasts. The AI might predict high demand for a product in a region where it’s actually underperforming, causing overstocking and increased holding costs. Conversely, it might underestimate demand elsewhere, leading to stockouts and lost sales. By implementing data quality management during the collection phase, the company standardizes data formats, ensures accurate currency conversions, synchronizes time zones, and validates entries. This results in clean, consistent data that enhances the AI model’s accuracy.
→ During Transfer or Recording: This stage is a minefield of potential errors. Manual entry mistakes such as typos or incomplete records can cause massive headaches down the line.
For example, in the financial sector, inconsistently recording customer information such as variations in names, multiple email addresses, or different phone numbers can create significant security and compliance challenges. A single customer might be entered into the system multiple times as “Robert Johnson,” “Bob Johnson,” or “R. Johnson,” each with different contact details. This fragmentation makes it difficult to verify identities accurately, increasing the risk of fraud or unauthorized access to accounts. It also complicates compliance with regulations like Know Your Customer (KYC) and anti-money laundering laws, which require precise and consolidated customer records. Without thorough data validation and standardized recording practices, these issues can slip through unnoticed, exposing the institution to legal risks.
→ During Use: Errors in this stage often arise when data is manipulated for analysis or reporting. Outliers, duplicate entries, or missing values can lead to skewed insights.
Take, for example, an e-commerce company analyzing purchase patterns. If outliers like an unreasonably high order value aren’t caught, the company might falsely conclude that their high-end product line is performing better than it actually is. Even worse, decisions based on inaccurate data can lead to misguided business strategies, such as allocating resources to the wrong product lines or target markets.
The Pillars of Data Quality Management
Data quality management includes several techniques. Let’s have a look at the five pillars that support it:
#1 The People
Technology itself will not be of much use if there are no people to implement it. Despite what everyone says, human oversight is far from being obsolete. Therefore, data quality management has several roles and positions for humans including data analysts and data managers.
They offer different services and perform unique duties to ensure the proper management of data. Some even need special training and education to fulfill their role.
For example, in a large financial institution, data analysts work closely with compliance teams to ensure that transaction data is accurate and adheres to regulatory standards. Even with advanced systems in place, it’s these people who spot anomalies or patterns that automated tools might miss. Their role is strategic that bridges the gap between raw data & business outcomes.
#2 The Profiling & Cleaning Process
Data profiling is one of the most important parts of the process. It involves:
- Having a complete look at the data and reviewing all the details.
- Contrasting and comparing the data to ensure correctness.
- Running different statistical models on the data.
- Measuring and reporting the quality of the data.
The main purpose of this process is to develop insights into the data. It helps develop a starting point in the process. Without data profiling, it would be hard to create standards since we wouldn’t know where we want to go with the data that we have.
Consider an e-commerce company trying to launch a personalized marketing campaign. Without proper data profiling, they might not realize that 20% of their customer data is incomplete, leading to poor targeting. By thoroughly profiling their data, they can identify missing customer addresses, outdated emails, or duplicate records that, if unaddressed, would result in a failed campaign.
#3 Defining the Quality of Data
This process can be very difficult to manage as it involves defining data quality rules that must be mapped to an organization’s structure and requirements. This can involve basic factors like setting data collection rules, to more complicated nuances like managing data integration and mergers from third-party sources.
Example: A retail business might define data quality based on its customer segmentation for marketing purposes, but its finance department may need stricter validation criteria for transaction data. Without clear and specific data quality rules in place, each department might have conflicting definitions.
#4 Reporting Data
You can’t improve what you don’t measure. The fourth pillar involves recording and removing issues with the data so that you only have clean data to work on. Ideally, this should be used to identify quality patterns.
Reporting and monitoring make up the crux of this process.
Imagine a logistics company consistently missing delivery deadlines. Upon analyzing their reporting data, they discover that their shipment tracking data has inconsistencies due to poor data entry at the warehouse. By consistently monitoring and reporting on data quality, they can prevent these issues from snowballing into larger operational failures.
#5 Repairing Data
Merely identifying the problem is not enough, one needs to take steps to correct the issue. The business needs to know the right and most efficient way to repair the data.
It is best to go deep into the cause and understand the reason. This will not only help correct the data but can also prevent similar problems in the future.
Take a manufacturing firm that frequently deals with errors in their supply chain data. Simply correcting the data isn’t enough. They need to understand whether the issue is due to human error, outdated systems, or a lack of data governance. By focusing on the root cause, the company can repair the issue permanently, ensuring smoother operations and better decision-making down the line.
Why Is Data Quality Management So Important for AI-Driven Projects?
An AI system is only as good as the data it learns from. Poor data quality leads to inaccurate models, flawed insights & ultimately, bad business decisions. One seldom-discussed aspect is how small errors in data can be amplified in AI models. For instance, inconsistencies or gaps in training data can cause an AI model to misinterpret patterns, leading to significant errors in predictions or classifications.
Another critical issue is bias in data. If the data feeding into an AI model is biased or unrepresentative, the AI will inherit these biases. This can result in unfair outcomes, such as a loan approval system that discriminates against certain groups because the training data didn’t include diverse demographics. Managing data quality helps identify and correct these biases before they become embedded in AI systems.
Data quality management also addresses the problem of data silos. In many organizations, data is scattered across different departments and systems. When building AI models, integrating these disparate data sources without proper quality checks can introduce errors and inconsistencies. By ensuring that data is accurate, consistent, and consolidated, data quality management enables AI models to provide reliable and meaningful insights.
How Can We Measure the Quality of Data?
So how do you know if your data needs to be fixed? The key lies in measuring data against certain parameters. These are:
These are:
- Accuracy: This refers to all changes being implemented in real-time. This way the data will be accurate and up-to-date. The best way to measure accuracy is the ‘source document’. However, one can also count on other confirmation techniques.
Ensuring data accuracy involves verifying that customer records such as phone numbers, emails, and addresses are correct and up-to-date.
Practical methods include using Excel functions like ISBLANK to identify missing values and LEN to check the length of phone numbers. Implementing CRM validation rules can automatically enforce correct data formats, such as regex patterns for email validation and standardized address formats.
Beside that, data quality tools like WinPure automates the cleansing and deduplication process, ensuring each customer has a single, accurate profile. Automating data validation through services like email verification APIs and address validation APIs further enhances accuracy by continuously checking and standardizing data against reliable databases. Regular data audits using these tools help identify common errors and address root causes, maintaining high data integrity essential for reliable AI-driven projects.
- Consistency: When it comes to data, consistency refers to a lack of conflict between two or more values. However, it should be mentioned that consistency does not always mean correctness as these two elements are different.
Consistency ensures that data follows uniform standards and formats across different datasets or within the same dataset. For example, a customer’s state is always abbreviated as “CA” instead of sometimes being spelled out as “California.” This uniformity helps maintain data integrity.
However, correctness pertains to the accuracy of the data itself whether the information truly reflects reality. Even if a customer’s state is consistently recorded as “CA,” it may be incorrect if the customer actually resides in “NY.” Here, the data is consistent in format but fails to be correct in its content.
- Completeness: Incomplete data is of no value. You will not be able to reach a conclusion if you do not have complete data.
Completeness refers to the extent to which all required data is present. Incomplete data can severely impact analysis and decision-making. For example, customer records missing essential details like city names, street addresses, or zip codes can lead to ineffective marketing campaigns or failed deliveries.
Instead of manually checking each entry, data professionals can profile their data to assess completeness efficiently. Excel offers functions such as COUNTBLANK to quickly identify the number of missing values in a dataset. For example, using =COUNTBLANK(A:A) can reveal how many entries in column A are incomplete. In the same way, data profiling tool like WinPure can automate the process, providing a percentage of incomplete records and highlighting specific fields that frequently have missing data.
Other techniques include setting up automated alerts within your CRM or database systems to notify when critical fields are left empty during data entry. Implementing data validation rules ensures that essential information is captured before records are saved. For example, configuring your system to require a zip code when an address is entered can prevent incomplete entries from being recorded.
- Integrity: This refers to data validation. It is important that your data fully complies with all the procedures so that you do not have to face issues in using the data that you have secured.
- Timeliness: It is important for data to be available when you need it. For example, you will need an updated email list to inform users about Christmas discounts before the 25th of December. The list will not be of much use if it reaches you on the 26th.
Challenges in Data Quality Management (DQM)
Some of the toughest obstacles in data quality management come from within the organization itself.
WinPure tackles the core challenges of data quality management with a simple, user-friendly interface. It allows teams to clean, standardize, and deduplicate data quickly, without needing complex software or advanced technical skills.
Cultural Resistance to Data Governance
One big challenge in managing data quality is when people resist new data rules. In some companies, employees have their own way of handling data. They’ve been doing it for years and feel comfortable with it.
For example, a sales team might prefer to store customer information on their personal devices or spreadsheets instead of the company’s centralized system. They might think, “It’s faster this way,” or “I know where everything is.” But this makes it hard to keep data accurate and consistent across the organization. When employees don’t see the importance of following data policies, the quality of data suffers.
Data Ownership Conflicts
Another problem happens when it’s unclear who is responsible for certain data. Imagine a company where both the marketing and customer service departments collect customer emails. Marketing wants them for newsletters, while customer service needs them to solve issues. If there’s no agreement on who “owns” the email list, updates made by one team might not be reflected in the other’s records. This leads to inconsistencies, like sending promotions to outdated email addresses or missing important customer complaints. Without clear data ownership, teams can step on each other’s toes, causing confusion and errors.
Unstructured Data Management
Unstructured data includes things like emails, social media comments, videos, and images. Unlike data in neat tables, this information doesn’t have a set format. For instance, think about the feedback a company gets on its social media pages. Customers might post comments, reviews, or complaints in different ways. One might write a long paragraph; another might leave a short note with emojis.
Extracting meaningful insights from this mixed-up data is tough. Important information can get overlooked because it’s hidden in an unexpected place. Without proper tools to analyze unstructured data, companies miss out on valuable feedback that could improve their products or services.
Managing Data Quality for AI-Driven Success
Data quality management is essential for the success of AI-driven projects. High-quality data ensures that AI models generate accurate and reliable insights. Without it, organizations risk deploying AI systems based on flawed data, leading to biased outcomes, erroneous forecasts & compromised compliance. For instance, inaccurate customer records can distort AI-based risk assessments, resulting in financial losses and damaged reputations.
Implementing data quality practices allows organizations to build AI models on a solid foundation. Tools like WinPure simplify these processes by offering intuitive interfaces and advanced capabilities for data cleansing and deduplication. These solutions enable data teams to efficiently manage and maintain precise datasets, ensuring that AI systems operate with the best possible information.
