Did you know that common data quality difficulties affect 91% of businesses? Incorrect data, out-of-date contacts, incomplete records, and duplicates are the most prevalent. It’s impossible to identify new clients, better understand existing client needs, or increase the lifetime value of each customer today and in the future if there isn’t clean and accurate data.
As data has become a critical component of every company’s activity, the quality of the data collected, stored, and consumed during business operations will significantly impact the company’s current and future success.
What is data quality?
Data quality is an essential component of data governance, ensuring that your organization’s data is suitable for its intended purpose. It refers to the entire usefulness of a dataset and ease of processing and analysis for other purposes. Its dimensions, such as completeness, conformity, consistency, accuracy, and integrity, ensure that your data governance, analytics, and AI/ML projects deliver consistently reliable results.
To evaluate it, one must consider data as the cornerstone of a hierarchy built on it. Information is placed in context over the foundation of data, and information comes next. Inferior quality data will produce inferior information quality, which will raise the hierarchy, leading to poor business judgments.
According to a study, the most common reason for incorrect quality is human error. Working on improving low-quality data is time-consuming and requires a lot of effort. Other factors contributing to bad quality include a lack of communication between departments and faulty data management techniques. Proactive leadership is required to address these issues.
Poor quality has a significant impact on your company at all levels:
- Higher processing cost: It takes ten times as long to complete a unit of work when the data is wrong than accurate.
- Unreliable analysis: Lower confidence levels in reporting and analysis make bottom-line management a difficult task.
- Poor governance and compliance risk: Compliance is no longer optional, and business survival becomes more difficult without them.
- Loss of brand value: When businesses make frequent mistakes and judgments, their brand value rapidly decreases.
How is data quality measured?
It’s easy to spot data quality, and it may be evident. It’s difficult to assess precisely because the data quality is ambiguous. To obtain the appropriate context and measurement technique for data quality, you may utilize numerous variables.
Customer information must be complete, precise, and accessible during a marketing campaign. Customer data must be unique, accurate, and consistent across all engagement channels for a marketing campaign. Data quality dimensions are concerned with characteristics that are particular to your situation.
Data quality dimensions
Dimensions of quality are elements of measurement that you may each evaluate, interpret, and improve. The aggregate scores of many dimensions represent data quality in your specific situation and indicate whether the data is fit for use.
There are six fundamental dimensions of data quality. These are the standards that analysts use to assess a data’s viability and usefulness to those who will use it.
Accuracy
Businesses should reflect real-world situations and occurrences in the data. Analysts should rely on verifiable sources to validate the measure of accuracy, which is influenced by how close the values match with verified accurate information sources.
Completeness
The data’s completeness assesses whether it can successfully deliver all required values.
Consistency
The uniformity of data as it travels across applications and networks and comes from many sources is data consistency. Consistency implies that identical datasets should be present in distinct locations and not clash. Keep in mind that consistent data may be incorrect.
Timeliness
Data that is timely is information that is readily available when needed. This aspect also entails keeping data up to date, which entails real-time updates to ensure that it is always accessible and current.
Uniqueness
Each entity, event, or piece of information in a dataset must be unique from all others. No duplicate records exist in the data set. Businesses may use data cleansing and deduplication to assist with a low uniqueness rating.
Validity
Businesses should gather data following the organization’s established business rules and parameters. All data values should also be within the correct range, and all dataset values should correspond to acceptable formats.
Data quality issues
Poor quality has a wide range of obligations and potential consequences, both minor and severe. Data quality problems waste time, lower productivity, and raises expenses. They may also harm consumer satisfaction, damage corporate reputation, necessitate costly fines for regulatory non-compliance, or even put customers or the public in danger.
How to improve the data quality?
Improving data quality is about finding the right balance of qualified people, analytical processes, and accurate technology for your company. Along with proactive top-level management, all of this can significantly enhance data quality.
Let’s start basic and follow the four-step program:
Discover
To be able to plan your data quality journey, you must first determine where you are today. To do so, you’ll need to look at the status of your data right now: what you have, where it’s kept, its sensitivity level, data connections, and any quality concerns it has.
Define rules
The data quality measures you choose and the rules you’ll establish to get there are determined by what you learn throughout the discovery phase. For example, you may need to cleanse and deduplicate data, standardize its form, or delete data before a specific date. This is a collaborative effort between IT and business.
Apply rules
After you’ve established regulations, you’ll connect them to your data pipelines. Don’t get trapped in a silo; Businesses must integrate their data quality tools across all data sources and targets to remediate data quality throughout the company.
Monitor and manage
Data quality is a long-term commitment. To keep it, you must be able to track and report on all data quality processes both in-house and in the cloud using dashboards, scorecards, and visualizations.
Following are the disciplines that can help you prevent data quality concerns and eventual data cleansing:
- Data governance
- Data profiling
- Data matching
- Data quality reporting
- Master Data Management (MDM)
- Customer Data Integration (CDI)
- Product Information Management (PIM)
- Digital Asset Management (DAM)
You should use some tools for the best results.
What are data quality tools?
Data quality tools clean data by correcting formatting mistakes, typos, and redundancies while also following processes. These data quality solutions may eliminate anomalies that increase company costs and irritate consumers and business partners when used effectively. They also contribute to revenue growth and employee productivity.
Business intelligence software addresses four crucial aspects of data management: data cleaning, data integration, master data management, and metadata management. These tools go beyond basic human analysis by identifying faults and anomalies using algorithms and lookup tables.
How to choose a data quality tool?
Consider these three aspects while selecting a data quality management software to fulfill your company’s requirements:
- You should be able to identify the information issues that exist.
- Recognize what data quality solutions can and cannot accomplish.
- Understand the advantages and drawbacks of different data cleaning solutions.
3 best data quality tool you might need
Data quality management software is essential for data managers who want to assess and improve the overall usability of their databases. Finding a suitable data quality solution necessitates consideration of various criteria, including how and where an organization saves and utilizes information, how data moves across networks, and what sort of data a team wants to tackle.
Basic data quality tools are freely available through open source technologies, but many of today’s solutions include sophisticated features across multiple platforms and database formats. It’s crucial to figure out precisely what a specific data quality solution can accomplish for your company – and whether you’ll need several tools to handle more complex situations.
IBM InfoSphere QualityStage
The Data Quality Appliance from IBM, available on-premises or in the cloud, is a versatile and comprehensive data cleaning and management tool. The objective is to achieve a uniform and correct view of clients, suppliers, regions, and goods. InfoSphere QualityStage was created with big data, business intelligence, data warehousing, application migration, and master data management.
Key values/differentiators:
- IBM provides a variety of key features that help to ensure high-quality data. Deep data profiling software delivers analysis to aid in the comprehension of content, quality, and structure of tables, files, and other formats. Machine learning may auto-tag information and spot possible problems.
- The platform’s data quality rules (approximately 200 of them) manage the intake of bad data. The tool can route difficulties to the correct person to resolve the underlying data issue.
- Personal data that includes taxpayer IDs, credit cards, phone numbers, and other information is identified as personally identifiable information (PII). This feature aids in the removal of duplicate records or orphan data that might otherwise wind up in the wrong hands.
- The platform offers excellent governance and rule-based data handling. It provides strong security measures.
SAS Data Management
The Data Integration and Cleaning Management workstation is a role-based graphical environment for managing data integration and cleaning. It includes sophisticated tools for data governance and metadata management, ETL/ELT, migration and synchronization capabilities, a big data loader, and a metadata bridge to handle big data. SAS was ranked as a “Leader” in Gartner’s 2020 Magic Quadrant for Data Integration Tools.
Key values/differentiators:
- The Data Quality Management (DQM) wizards provided by SAS Data Management are handy in data quality management. These include tools for data integration, process design, metadata management, data quality controls, ETL and ELT, data governance, migration and synchronization, and more.
- Metadata is more challenging to manage in a large organization with numerous users, and it has the potential to lose impact over time as information is exchanged. Metadata management capabilities provided by this tool include accurate data preservation. Mapping, data lineage tools that validate facts, wizard-driven metadata import and export, and column standardization features help maintain data integrity.
- Thirty-eight countries worldwide use native languages for data cleansing, with language and location awareness. The program includes reusable data quality business rules implemented into batch, near-time, and real-time procedures.
Informatica Quality Data And Master Data Management
Informatica has developed a framework to handle various operations connected with data quality and Master Data Management (MDM) to manage and track data quality. This includes role-based abilities, exception management, artificial intelligence insights into issues, pre-built rules and accelerators, and a comprehensive range of data quality transformation solutions.
Key values/differentiators:
- The vendor’s Data Quality solution is excellent at standardizing, validating, enriching, deduplicating, and compressing data. Versions are available for cloud data stored in Microsoft Azure and Amazon Web Services.
- The firm’s Master Data Management (MDM) solution guarantees data integrity via matching and modeling, metadata and governance, and cleaning and enriching. Informatica MDM automates data profiling, discovery, cleansing, standardizing, enriching, matching, and merging within a single central repository.
- Applications, legacy systems, product data, third-party data, online data, interaction data, and IoT data are examples of structured and unstructured information that the MDM platform can capture.
