What is Data Quality Management?
Data quality, according to Wikipedia, refers to the qualitative and quantitative bits of information that will correctly offer data for the intended use within operations, decision making and planning.
This data then becomes of high quality if it correctly represents the real-world situations it operates in.
As the number of data sources increases, so the important question of data consistency has become questioned, regardless of how well the data in question fits any external use.
Due to this, a system of data governance has been created to form a standard and definition of data quality. This is known as data quality Management.
What is data quality?
As data is used in many diverse contexts, it can be hard to define just what data quality is in one single sentence.
Instead, let’s look at data quality from three examples: – from the consumer, the business and from that of a standards-based perspective.
The consumers perspective :
- Data that is fit to be used for consumers to gain the information they need about a business or product
- Data that meets or exceeds the consumer’s needs to educate them for purchase from the business
The business perspective
- Data that assists in the running, organising and decision-making process within a business
- The ability for data to meet the need of it’s intended usage in the daily running of a business
The standard-based perspective
- The usefulness, accuracy and correctness of data to perform its application.
In summary, whilst each sector has different needs, all of the perspectives look for the very same characteristics within data to classify it as quality data.
What is Data Quality Management used for?
Now that we understand what quality data is and why it’s important let’s look at the 5 pillars behind data quality Management.
Technology will only ever be as efficient as the people who implement it.
This is why companies like Gartner MDM employ several people in different roles to oversee data quality.
- Data program manager – the data program manager will oversee and articulate the program’s objectives, outlining the strategy and assess how implementing the strategy will impact a business.
- Organisation change manager – the organization change manager implements the necessary change management shift that occurs when data is correctly used. This includes making decisions about the process and infrastructure of data.
- Data analyst – the data analyst examines the data and concepts it into the information and insight to assist in making informed business decisions.
2. Data profiling
Data profiling is essential and these include :
- Reviewing data
- Comparing data to its own metadata
- The running of statistical models
- Reporting on data quality
Data profiling helps the business to find a starting point and set a standard of data quality.
3. Defining the data quality
A set of quality rules defined and set according to the business requirements and goals.
4. Data reporting
Data Quality Management is the reporting and recording of any compromising data.
5. Data repair
Data repair involves both the best way to remediate data and the most efficient manner to implement changes.
The benefits of good data management
Good quality data, in today’s digital world, sets the huge difference between dynamic leaders in their industry and other businesses.
Bad quality data will directly and negatively impact every aspect of your daily business including :
- The cost of your marketing campaigns
- The effectiveness of any of those marketing campaigns
- The efficiency to turn leads into sales
- The accuracy to make business decisions
In short, Data Quality Management is crucial to keeping your business competitive in today’s highly competitive digital workplace.