The world today is swimming with data, and organizations need to handle a large amount of it to derive valuable insights for decision-making. But data is only helpful if it is of good quality. According to SAP, bad data costs the US $3 trillion annually. These costs include employee’s time and effort to correct bad data and errors.

Why is data quality important?

Data quality is a base for data analytics and data science. It measures how well-suited your data is to accomplish a particular task accurately and consistently. Good data helps an organization make key spending decisions, improve operations, and develop growth tactics. Even though technologies like AI and machine learning have enormous potential to handle large volumes of data, they need good quality data to produce reliable results quickly.

As data is an integral part of an organization, data quality impacts many aspects, from marketing to sales to content creation.

Good quality data helps you make informed decisions, target the right audience, drive effective marketing campaigns, strengthen customer relationships, gain competitive advantage, and so on.

Benefits of improving data quality

In this competitive era, organizations try to understand their customers better and make better financial, marketing, and development decisions based on accurate data for a better ROI. Bad data is unstructured data that may show quality issues like inconsistency, inaccuracy, insufficiency, or even duplicate information. It could be misleading and even more harmful for a business than a lack of data.

Improving the data quality of your company can result in the following advantages:

• Data-driven decision-making: Decision-making is based on solid reasoning, and the correct data can only help make wise business decisions and provide the best outcomes.
• Customer intimacy: Drive marketing and customer experience by analyzing entire consumer views of transactions, sentiments, and interactions by using data from the system of record.
• Innovation leadership: Learn more about your products, services, usage trends, industry trends, and competition outcomes to help you make better decisions about new products, services, and pricing. ​
• Operational excellence: Make sure the correct solution is provided quickly and dependably to the right people at a fair price.

Challenges faced while maintaining data quality

Poor data quality can lead to financial losses, increased complexity, and limited usefulness of real-world evidence. Understanding data quality challenges is crucial for informed decision-making, reducing business risks, and achieving data-driven goals. Explaining a few challenges below.

• Data debt reduces ROI: Data debt refers to the negative consequences of accumulating poorly managed data. This includes inaccurate, inconsistent, incomplete, or inaccessible data. If your organization has a lot of unresolved data-related problems, such as issues with data quality, categorization, and security, it can hinder you from achieving the desired Return on Investment (ROI).
• Lack of trust leads to lack of usage: A lack of data confidence in your organization leads to a lack of data consumption, which has a detrimental impact on strategic planning, KPIs, and business outcomes.
• Strategic assets become liabilities: Poor data puts your company in danger of failing to meet compliance standards, resulting in millions of dollars in fines.
• Increased expenses and inefficiency: Time spent correcting inaccurate data equals less workload capacity for essential efforts and an inability to make data-driven decisions.
• Adoption of data-driven technologies: Predictive analytics and artificial intelligence, for example, rely on high-quality data. Delays or a lack of ROI will come from inaccurate, incomplete, or irrelevant data.
• Customer experience: Using bad data to run your business can hinder your ability to deliver to your customers, increasing their frustration and reducing your capacity to retain them.

Improving data quality with Fractal’s methodology 

Maintaining high levels of data quality allows organizations to lower the expense of finding and resolving incorrect data in their systems. Companies can also avoid operational errors and business process failures, raising operating costs and diminishing revenues. 

Good data quality enhances the accuracy of analytics applications, leading to improved business decisions that increase sales, improve internal processes, and provide firms with a competitive advantage over competitors. High-quality data can also help increase the use of BI dashboards and analytics tools. If analytics data is perceived as trustworthy, business users are more inclined to depend on it instead of making judgments based on gut feelings or their spreadsheets. 

Fractal has developed a process that significantly improves data quality across enterprises. Here’s how we do it. 

Data Preparation and Rule Calculations 

Fractal helps handle large volumes of data, preparing it for further processing. It also performs calculations based on data rules, identifying defective records in the data. 

Data extraction and preparation 

Fractal leverages a back-end engine for data extraction, data preparation, and data rules configuration on various data sets. 

Optimized process 

The focus is an optimized process with minimal processing time, parallel processing, and data aggregations to reduce the storage space and provide the user’s best dashboard performance. 

Data quality improvement 

Fractal helps transform the data cleansing operation with faster reduction of data defects, improved data quality, and tracking key KPIs like asset score, coverage, and conformance. 

How can Fractal help maintain data quality with Google Cloud?

Fractal leverages all the services provided by Google Cloud and supports integrations with the Google Cloud Platform. It also determines which Google Cloud services best meet Fractal’s data quality needs for each project.

Here are some ways Google Cloud can help maintain data quality.

• Data Governance: It helps automatically detect and protect sensitive information in data, ensuring data privacy and compliance. It also helps enable granular control over data access, preventing unauthorized modifications or deletions.
• Data Profiling & Cleansing: It offers a user-friendly interface for data cleaning and transformation, including outlier detection, data standardization, and data validation. It also provides AI-powered tools for data profiling and anomaly detection, helping identify data quality issues proactively.
• Data Monitoring & Improvement: It offers comprehensive dashboards and alerts for monitoring data quality metrics, allowing for proactive identification and resolution of issues. It also helps run large-scale data quality checks and analysis, providing deeper insights into data quality trends.
• Machine Learning & AI Integration: It provides tools for developing and deploying custom AI models for data quality tasks, such as entity extraction, classification, and matching. It also helps build serverless data quality functions that can be triggered on specific data events, ensuring real-time data quality checks.

Conclusion

In today’s data-driven world, maintaining high-quality data is no longer an option but a necessity. Poor data quality can lead to financial losses, operational inefficiencies, and inaccurate decision-making. By leveraging Fractal’s data quality methodology and Google Cloud’s powerful tools and services, organizations can effectively address data quality challenges and unlock their full potential.

Fractal empowers organizations to achieve data quality excellence. When combined with Google Cloud’s data quality capabilities, Fractal delivers a comprehensive solution for managing and improving data quality throughout the entire data lifecycle.

Are you seeking help to improve the data quality of your organization? Contact us to get started!