This data explosion is pushing enterprises in a more data- driven direction. Organizations are now performing complex analysis on their data. It helps them analyse the market trends, develop streamlined operations and enhance the customer experience
IBM states that “The majority of data in the world has been generated over the last few years". As we have adopted the Internet of Things, we're now on track to beating any and all records of data generation year-on-year.
One of the key concerns during this analysis is that of the data's quality. High-quality data is important because it gives us accurate and timely information to manage services and accountability. Also, it helps us to prioritize and ensure the best use of resources. This is a no brainer that good quality data will lead to valuable information and appropriate insights for your organization. But, obtaining high-quality data is not an easy task.
Improving your data quality and sustaining good quality data output is one of the major challenges faced by the enterprises today.
According to IBM, data-quality related problems can result in a loss of millions of dollars in revenue. Wrong decisions are made due to poor quality of data and poor data management processes. Due to this, many companies lose their customers and clients.
Thus, if data quality is not ensured, your data can become a risky liability instead of a significant asset.
Before learning how to maintain high-quality data, we must first learn what factors are creating data quality issues:
Factors that cause data quality issues
Thus, maintaining data quality has become essential. Following are the 4 ways in which you can improve your data quality:
1. Data Profiling
The first step in improving data quality is to examine your data defects through data profiling. Data profiling analyzes the correctness and uniqueness of data. It also checks whether the data is reusable by collecting appropriate statistics. Similarly, data mining tools are also used to assess data quality.
2. Data Normalization
The second step is to normalize your data. Data normalization is crucial because data is collected from various sources and may include a variety of spelling options. It confuses CRMs as they see them as different data points. Thus, data standardization is essential to establishing a singular approach and to remove redundancy.
3. Semantic Metadata Management
The third step is having a semantic metadata management process. As the number and variety of data sources grow, situations can occur when end users in different parts of an organization will misinterpret some of the data concepts and terms. Thus, centralizing the management of business-relevant metadata is required. This will help in establishing corporate standards and reduce inconsistent interpretations.
4. Data Quality Firewall
The fourth step is building a data quality firewall. Gartner predicts that poor data quality leads to 50% failure in CRM systems. Data is a strategic information asset to an organization and has a huge financial value. Hence it must be protected. A data quality firewall uses software to keep data error free and non redundant. Many organizations now realize that the consequences of poor quality of data can result in adverse effects. To avoid customer attrition or severe loss in market share, companies are now paying attention to maintaining and improving data quality.