Data Quality is Underrated
In today’s data-driven world, businesses rely on information to make crucial decisions, optimize processes, and gain a competitive edge. However, the value of data lies not just in its quantity but, more importantly, in its quality. It might seem like a big ask to make sure that data quality principles are enforced, but certain tools and platforms can greatly assist in those efforts.
The Data Quality Dilemma
Data quality refers to the accuracy, completeness, consistency, and reliability of the information you collect and store. It is the bedrock upon which every data-driven initiative stands. Without high-quality data, your organization may encounter a range of problems, including:
- Misinformed Decision-Making: Inaccurate or incomplete data can lead to poor decision-making. Decisions based on faulty information can result in financial losses and missed opportunities.
- Inefficient Operations: Low-quality data can lead to inefficiencies in your processes. Duplicate or inconsistent data can hinder productivity and waste valuable resources.
- Damaged Reputation: Data quality issues can harm your organization’s reputation. Customers, partners, and stakeholders may lose trust in your ability to handle their information, which can lead to a loss of business.
- Compliance Risks: In some industries, such as healthcare and finance, data quality is crucial for regulatory compliance. Failing to maintain data quality standards can result in hefty fines and legal consequences.
The Impact on Business Intelligence (BI)
Business Intelligence (BI) relies heavily on the quality of data. BI tools and analytics platforms transform raw data into actionable insights. If the input data is of poor quality, the output insights will be unreliable and potentially misleading. Here’s how data quality affects BI:
1. Reliable Insights
High-quality data ensures that the insights generated by BI tools are accurate and trustworthy. Reliable insights are the foundation of strategic planning and informed decision-making.
2. Enhanced Predictive Analytics
Predictive analytics, which help organizations forecast future trends and outcomes, are only as good as the data they are based on. Improved data quality leads to more accurate predictions.
3. Faster Decision-Making
Quality data accelerates the decision-making process. With confidence in the data, organizations can make faster decisions, gaining a competitive advantage in dynamic markets.
Facilitating Data Quality with Low-Code Platforms
Now that we understand the significance of data quality, let’s explore how low-code platforms can help ensure data integrity:
1. Data Validation
Low-code platforms can incorporate data validation rules, ensuring that only high-quality data enters your systems. This reduces the likelihood of errors and inconsistencies. These validations can be placed on many different places, and in many different ways. For example, in Linx you can add these validations in on your process by using expressions, or you can add checks in via SQL. You can add checks on fields and then flag those records. The flagged records and their issues can then be combined into a report that is mailed to the person or team that is responsible for fixing the validation breaches.
This is a Linx function that will read an Excel file, apply validation rules on the data, insert the data int a database and then, finally send an email with the validation issues to a specified team. This entire process was created in 15 minutes, by using drag and drop components and configuration.
Linx is not the only low-code tool that will allow you to do this, many low-code tools can really make data validation easier by implementing robust and custom data validation processes. It's only up to you to come up with the business process around those processes.
2. Automated Data Cleansing
Low-code platforms can automate data cleansing processes, detecting and rectifying data quality issues in real-time. This saves time and resources while improving data accuracy. Mapping tables help quite a bit if you want to standardize data.
Similar to the above, you can for example apply data transformations to clean the data before it is loaded to storage. In Linx, this transformation can either be done in your SQL script, or it can be done in Linx itself. By using expressions, you can search and transform your data to match a pre-defined format, to remove unwanted characters or to clean the data only to what you are expecting.
The above screenshot illustrates how you can apply transformations in Linx. This specific expression will replace all spaces with underscores, and it will format the value to be uppercase only. You can apply much more complex transformations than this.
3. Data Governance
Low-code platforms often include data governance features, allowing organizations to define and enforce data quality standards throughout the data lifecycle.
You can also use these platforms to develop your own processes and applications to facilitate your data governance processes. For example a monitoring and auditing process that will monitor data for any specific outliers or that (like mentioned above) will apply validations to your data. Then those outliers, errors or warnings can be emailed to a responsible team to take action.
4. Data Integration
Many low-code platforms offer seamless data integration capabilities, enabling you to pull data from multiple sources while maintaining its quality.
In the age of data, the importance of data quality cannot be overstated. It underpins effective decision-making, operational efficiency, and regulatory compliance. Without high-quality data, your organization’s business intelligence efforts are likely to falter. Embracing low-code platforms that prioritize data quality can be a strategic move, ensuring that your data is not just abundant but also reliable. Invest in data quality today to secure a brighter future for your organization.
