Data Quality Systems

Data quality is of the utmost importance to Canadian companies. The ability to collect timely, accurate and high quality data is often the difference between successful and failed decision making and strategic plans. It is not uncommon for industry leaders to be the companies that have an effective data management and data quality system in place. Being able to collect large volumes of data is one thing, but a company’s ability to turn this into high quality data and knowledge is what sets many companies apart from the crowded marketplace.

What are data quality systems?

Data quality systems, which are sometimes referred to as data quality management systems, are used by companies to assist with the development and collection of high quality data. The system, depending on the strategic initiatives from a company, works to separate poor quality or unusable information from high quality data that is accurate, complete, relevant, and reliable.

Data quality systems operate based on a set of rules, guidelines and best practices that help to develop data into knowledge that can be used internally. It can also help companies uncover new opportunities in terms of operations, processing, and decision making.

When implementing a data quality system, companies need to be clear on their definition of data quality. Defining data quality correctly is a vital component in ensuring that you are collecting the right type of information that can be converted into usable knowledge.

Therefore, it is best to take time to properly define data quality and outline its specific characteristics. Companies should also expect that how their data quality system is used and how data quality is defined will change over time as the company shifts its focus.

Types of systems for different data management functions

There are many different types of data quality systems. They can be used in virtually every industry and for a number of data related tasks and initiative. Data quality systems can be used in:

• Data governance
  
• Data quality management
  
• Data warehousing
  
• Business intelligence management
  
• Data architecture and design
  
• Database management
  
• Data security management
  
• Content management
  
• Meta data management and SEO
  
• Contact data management and customer relationship management

Common features of data quality systems

Here are some of the common features that come as part of these systems:

• Rule customization: This allows companies to customize the data collection and analysis tools based on their specific data requirements.
  
• Data cleansing: Tools to cleanse data of any issues, and inconsistencies as defined by the company
  
• Data profiling: The ability to find inaccuracies and anomalies in the data collected.
  
• De-duplication: The process of removing duplicate or redundant data that could skew results
  
• Identification analysis: The ability to use target marketing techniques as part of data collection.
  
• Standardization: The ability to standardize data formats to conform to the rules set by the business.
  
• Windows compatibility: A graphic user interface that is easily integrated within a Windows operating system environment.

Benefits of data quality systems

Data quality systems are an effective tool for companies that collect and use large volumes of data as part of running their business. Using this type of system provides the following benefits:

• Providing key decision makers with information they can trust to make important decisions
  
• Define data within the context of how companies intend to use it
  
• The standardization of data throughout the organization on multiple systems, servers, and devices
  
• The ability to manage the quality of data that is used internally
  
• The ability to properly profile data and week out information that could skew results and decision making