A Brief Introduction to Data Quality Management Systems
In the past few years, there has been an agreement in business and government circles about the efficacy of data. How useful can the right data be, has been answered by the growth in the data governance industry?
However, there is still some form of disagreement when it comes to branding data. In other words, what is the quality of data that is being accrued is more important than anything else is. Bad data results in bad planning, strategy, and execution. It ultimately results in huge amounts of wastage- time, money, and human resource and lowers motivations.
This is why data quality management has become as important as data collection. In fact, data quality management is the single most important process when we talk of data. In this article, we will look at the meaning, definition, importance, and advantages of quality data management.
Data Quality Management: Meaning and Definition
Data might be a broad terminology, however, its optimization, is something very niche and specific. No organization can collect all forms of data. There needs to be an intent to the collection, storage, and processing process.
The intent is always fixed at the level of top management. Accordingly, data is collected, stored and processed. This helps in increasing efficiency, reduces wastage and contributes to better revenues and processes.
If you are thinking as to who maintains an organization’s data quality management processes, I might have an answer for you. The biggest organizations previously accorded this function to the Marketing department.
They, in turn, used this to inform Operations and Sales. However, since they were too embedded in the working of the organization, things were not done in an optimum fashion.
In the last few years, separate Data Management Departments have come up, which are led to a Chief Technical Officer or a Chief Data Manager.
They work in small and niche teams and work through terabytes of data on a daily basis. The different departments submit their queries and receive filtered data from the Data Management Departments.
Advantages of Data Quality Management:
1. Predict Customer Purchase Behaviour:
One of the primary purposes of data quality management is knowing what the consumer wants before he knows it. This involves aligning a consumer’s interest patterns, his buying trends, and his requirements and so on.
A sound and strategic Data Quality Management system filters through layers of ambiguity and arrives at the right strategic point. This is the tipping point, which is needed by the marketing and sales teams to close the deal.
2. Prevents Bad Decision Making:
Data quality management involves the processing of end-to-end data for an organization. This means that the quality of data is always directly proportional and informs sound decision-making. Bad decision-making can cost organizations millions of dollars in losses.
Bad decision-making can set the goals of an organization on the backtrack. They can also divest it of resources, personnel, and monetary investments. This is why data quality management can help businesses when it comes to decision making.
3. Improves Customer Experience, Branding, and Reputation:
What you do with the data of your consumers says a lot about your ethics in business. Some of the biggest companies in the world are plagued by data scandals over privacy and breach. Businesses take great pride in preventing data misuse. They use it as a form of branding.
If you have the best data quality management systems in place, security will never be an issue. You can also help customers in a big way when it comes to understanding their likes and dislikes.
While many businesses think of data management as an adversary, few look at it as a friend. There is no doubt that this is not an easy thing to do. It requires the best plans, strategies, and personnel in charge. However, if you are able to create a long-term strategic goal and work towards its execution, data quality management can influence your business positively in a big way.
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