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According to a recent survey in 2022, poor data quality issues had a 25% impact on company revenue. Unsurprisingly, ensuring data accuracy, completeness, and integrity continues to be among the top KPIs for data teams in 2024. There’s a need for businesses to invest in data quality software that meets their needs.
Be it managers, data analysts, or employees, today’s businesses rely on data on an everyday basis. The process of collecting and prepping data can be incredibly exhausting and costly, even with dedicated data scientists, in the absence of enterprise data quality software. Most businesses resort to fixing data themselves, resulting in bad data that gets pushed to other departments and systems, resulting in sub-optimal reports and decision-making.
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Why do you need data quality software?
Most data teams entertain the idea of automating certain tasks with the help of technology but seldom do something about it. You see, introducing new technology for digitizing your business can be disruptive, with new challenges and processes to learn. But in the long run, it can prove to be beneficial for your team’s performance and productivity.
For example, menial data preparation tasks can be handled by data quality software, leaving data scientists to focus on meaningful work on modeling and developing algorithms. In most organizations, data scientists end up spending 80% of their time doing data preparation tasks, a job that 76% of them do not enjoy at all. It’s no wonder that top data talent ends up switching employers in 1.7 years on average.
It doesn’t have to be this way.
Other than retaining top data talent, there are plenty of other good reasons why you need to invest in an enterprise data quality software:
- Accurate Decision-Making: Reliable data ensures that business decisions are based on accurate and up-to-date information, reducing the risk of making choices that could be detrimental to the organization.
- Compliance: Data quality tools help ensure compliance with regulations and standards by maintaining accurate and consistent data, which is crucial in industries where regulatory compliance is a priority.
- Customer Satisfaction: High-quality data contributes to better customer experiences by preventing errors and inaccuracies in customer interactions, leading to increased trust and satisfaction.
- Operational Efficiency: Clean and consistent data streamlines business processes, reducing the time and effort required for manual corrections and preventing operational disruptions caused by data errors.
- Cost Reduction: Improved data quality reduces the costs associated with data errors, such as customer complaints, product returns, and wasted resources on incorrect marketing campaigns.
- Enhanced Analytics and Reporting: Reliable data supports accurate analytics and reporting, providing a solid foundation for extracting meaningful insights and trends that can drive strategic decision-making.
- Effective Marketing Campaigns: Ensuring that customer data is accurate and complete helps in targeted marketing efforts, preventing wasted resources on campaigns that target the wrong audience or use outdated information.
- Risk Mitigation: Quality data reduces the risk of errors in financial reporting, compliance, and other critical areas, minimizing the potential legal and financial consequences associated with inaccurate data.
- Improved Collaboration: Consistent and accurate data facilitates better collaboration among departments, as teams can trust the data they share, leading to more effective cross-functional cooperation.
- Data-driven Innovation: Reliable data is essential for organizations looking to leverage emerging technologies such as artificial intelligence, machine learning, and data analytics for innovation and staying competitive in the market.
Questions to Ask Before Looking for a Data Quality Tool
Data quality can mean different things to different organizations. You need to identify your data KPIs to get a baseline idea of what you need in a data quality tool.
Furthermore, there are so many options in the market, which makes choosing a data management software a hassle. However, if you’re just starting your search, knowing your data KPIs and asking yourself these questions can narrow it down for you:
What are our specific data quality objectives?
Clearly define the goals and outcomes you aim to achieve with the data quality tool. Understand whether you primarily seek improved accuracy, streamlined processes, or compliance with specific standards.
What are the sources and types of data we need to manage?
Identify the variety and volume of data your organization deals with. Does it work well with CRMs, mailing lists, spreadsheets and databases? Consider whether the tool can effectively handle diverse data sources and formats relevant to your business.
How user-friendly is the tool for non-technical users?
Assess the tool’s usability, especially for individuals without extensive technical expertise. A no-code data quality tool allows users to design, implement, and manage data quality processes without the need for coding or programming skills.
Does the tool align with our scalability requirements?
Evaluate the scalability of the data quality tool. Consider whether it can seamlessly grow alongside your organization’s increasing data volumes and evolving user needs.
How well does the tool integrate with existing systems and workflows?
Ensure compatibility by assessing the tool’s integration capabilities with your current systems and workflows. A seamless integration can prevent disruptions and facilitate a smoother transition.
What is the speed and efficiency of the tool’s data processing?
Evaluate the tool’s processing speed, especially for computationally complex tasks. It should efficiently handle data quality processes without causing significant delays, ensuring timely results.
These questions can narrow down your search for a data quality tool immensely.
Key Features to Look For When Choosing Data Software
There are three main things to consider when looking for features in a data software. They are:
- The data quality processes that it can accomplish
- The additional features that can improve data quality processes
We’ve covered them all to help you decide:
Data Quality Processes
The data that you collect can be polluted by various data quality errors. Your data issues must be run through a proper end-to-end data quality management lifecycle to be of value.
This lifecycle consists of a list of systematic processes, the most common of them include:
Data import
Also called data ingestion, this feature is about connecting, ingesting and integrating data from various data sources. This also includes native support for CRMs, third-party file extensions, databases and file formats.
Data assessment
This feature helps you quickly examine and analyze a dataset to gain comprehensive insights into data quality. It accomplishes this by providing a 360-view of your data quality by inspecting field data, recurring patterns, blank values, and other statistics in your data. It also points towards areas where cleansing efforts can be directed.
Data extraction
Also known as data parsing, its primary purpose is to extract meaningful information or elements from the strings for further validation against a predefined library of accurate values. Data extraction can help you identify first or last names, as well as convert abbreviations to proper names among other things.
Data cleansing
This feature helps you achieve a standardized view for all your data by eliminating incorrect values across data sources.
Data matching
Data matching algorithms are designed to identify potential matches between records in datasets based on certain criteria or similarity measures. You can use configuration parameters like threshold values, similarity criteria, or other settings that can be adjusted to fine-tune the matching process.
Data deduplication
The data deduplicate feature involves identifying and handling duplicate records within a dataset to ensure data accuracy and consistency. It flags and eliminates instances where multiple records pertain to the same entity or individual. It reduces the likelihood of errors, improves the efficiency of data analysis, and ensures that decision-making is based on a clean and accurate dataset.
Combine or merge multiple records within a dataset while establishing rules for prioritization and conditional data overwriting. This feature helps you create a consolidated or master record that represents the most accurate and comprehensive information.
Data export
If you want to export your results back to the original source file or any other data source, you can.
Additional features for improved data quality
Apart from specialized data cleaning and management features, it is also imperative for data quality tools to help make your work easier, accurate and faster.
A no-code data quality tool like WinPure helps you out with:
A User-friendly Visual Interface
No-code tools typically provide a visual interface, often using drag-and-drop components, flowcharts, or other graphical elements. This allows users to design workflows, set up rules, and configure data quality processes intuitively. WinPure’s intuitive user interface helps users perform complex data quality tasks comfortably.
Speedy Data Processing
The processes we outlined earlier can be resource-intensive, requiring serious computational power. However, an optimized and extensively tested no-code data matching tool like WinPure delivers fast performance across various system configurations.
Accurate Results
With WinPure’s industry-grade algorithms for matching, standardizing, cleaning, and merging data, you can preview how accurate the results are visually across a variety of data samples.
Configuration Instead of Coding
Users can configure the tool by specifying rules, transformations, and logic through the graphical interface, eliminating the need for manual coding. This makes it accessible to a broader audience within an organization.
User Empowerment
WinPure empowers business users, data analysts, and other non-technical staff to take an active role in managing and improving data quality. This democratization of data quality initiatives promotes collaboration across different departments.
Rapid Deployment
Since users do not need to write code, the setup and deployment of data quality processes are typically faster. This allows organizations to address data quality issues promptly and with greater agility.
Adaptability and Flexibility
No-code tools often offer flexibility in designing and modifying data quality processes. Users can adapt workflows and rules quickly to respond to changing business requirements or evolving data quality needs.
Reduced IT Dependency
No-code tools reduce the dependence on IT departments for implementing and managing data quality processes. Business users can independently design and execute data quality initiatives, leading to increased efficiency.
Cost-Effective Solution
No-code data quality tools like WinPure can be a cost-effective solution as they minimize the need for specialized coding skills. This can result in cost savings in terms of both personnel and time required for implementation.
WinPure is an Enterprise Data Quality Software that checks those marks
Be it speed, reliability, or an easy-to-use user interface, WinPure helps you get started on your data cleaning journey with a low barrier to entry. Whether you’re a small business or a large enterprise, WinPure can scale to meet your data quality needs. With flexible licensing options and support for high-volume data processing, WinPure adapts to your business requirements as you grow.
You can check out our free trial today and decide for yourself.
To Conclude – Choose a No-Code Data Quality Tool for the best results
Investing in any data quality software requires time and commitment. However, a no-code data software can help you considerably reduce the gap between learning and implementation, especially with most specialized data processes requiring a few clicks.
WinPure facilitates teams by helping fix data quality errors accurately and faster. This way your data team can focus on the important tasks while making use of an all-in-one data quality software to take care of data cleaning and matching activities.
