5 Pillars of Effective Data Quality Management
Data is one of the most important resources that enterprises have today. An article published in The Economist in 2017 proclaimed that “The world’s most valuable resource is no longer oil, but data.” Today, the most valuable companies in the world (Apple, Google, Microsoft,
Amazon, Facebook) are all data companies which collect vast amounts of data about their users, analyze this data to generate insights, and use these insights as a resource from which they make money. Other companies are deriving tremendous value from data by using it to
speed up business processes, to avoid errors, to detect and mitigate risks before they occur,and more.
A study by Gartner has revealed that poor quality data is costing organizations on average $14.2 Million annually. Addressing the state of data quality and its business impact is a key priority for chief data officers and data leaders.
Enterprises are rightly placing emphasis on using data intelligently to drive important business decisions. However, having access to datasets is only part of the puzzle. Businesses often struggle with managing the quality of their data, leading to other issues that can considerably harm the company. This is where Data Quality Management (DQM) enters the scene.
What is Data Quality Management?
Data Quality Management is a set of standards to ensure all collected information is reliable, accurate, and meets quality standards. As organizations accumulate huge amounts of data, it is important to ensure good quality of the data – if the input is bad or erroneous, the output will not be accurate either. Therefore, Effective DQM is essential to any consistent data project, as the quality of data is crucial to derive actionable and – more importantly – accurate insights.
Why is Data Quality Important?
Just collecting data is not enough – maintaining the quality of data over time has a lot of benefits for any organization. These benefits include:
- Enables accurate decision-making
- Eliminates duplicate effort and improves operational efficiency
- Ensures Compliance
- Improves customer experience
- Saves cost and effort
The 5 Pillars of Data Quality Management
- Data Cleansing: Data Cleansing is used as an umbrella term for the entire process of fixing or removing incorrect, corrupted, incorrectly formatted, duplicate, or incomplete data within a dataset. This is a crucial step for maintaining high data quality. There is no one right way of cleaning a dataset. However, many data cleaning techniques can be automated using dedicated software.
- Data Validation: Once the data is cleaned, data validation is an important step in ensuring the accuracy and quality of data. While it is critical to validate data inputs and values, it is also necessary to validate the data model itself. If the data model is not properly structured or built, you will encounter problems when attempting to use data files in various applications and software.
- Data Linking: Data linking is the process of collating information from different sources in order to create a more valuable and richer data set. This allows employees to work seamlessly.
- Data Enrichment: Enriched data is a valuable asset for any organization because it becomes more useful and insightful. It involves combining first party data from internal sources with disparate data from other internal systems or third party data from external sources. Companies conduct data enrichment on their raw data so that they can use it to make informed decisions. In 2018, data enrichment grew by 80% and is growing year on year.
- Data Deduplication: Removing all redundant information from your data pool ensures that each entity, such as a specific customer or product, is uniquely represented, thus eliminating inconsistencies between different instances of the same data.
As data continues growing in complexity and scale, it is important for business decision makers to prioritize effective DQM strategies for today and for future organizational growth. If businesses can’t process their high-quality data in a way that is both adaptable and scalable, they will not be prepared when another disruptor or volatile market event comes along.
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