Data Quality Management: All You Need to Know
As organizations accumulate more data, managing the quality of that data becomes more consequential every day. After all, data is the lifeblood of any organization. Data quality management avails by amalgamating organizational culture, technology, and data to distribute results that are precise and utilizable.
Data quality is not good or bad, high or low. It’s a range, or measure, of the health of the data pumping through your organization. For some processes, a marketing list with 5 percent duplicate names and 3 percent bad addresses might be acceptable. But if you’re meeting regulatory requisites, the risks of fines demands higher calibers of data quality.
Data quality management provides a context-concrete process for amending the fitness of data that are utilized for analysis and decision making. The goal is to engender insights into the health of that data utilizing sundry processes and technologies on increasingly more astronomically immense and more intricate data sets.
Why do we need data quality management?
Data quality management is an essential process in making sense of your data, which can ultimately avail your bottom line.
First, good data quality management builds a substratum for all business initiatives. Outdated or unreliable data can lead to mistakes and missteps. A data quality management program establishes a framework for all departments in the organization that provides for – and enforces – rules for data quality.
Second, precise and au courant data provides a clear picture of any company’s day-to-day operations so you can be confident in upstream and downstream applications that utilize all that data. Data quality management withal cuts down on nonessential costs. Poor quality can lead to costly mistakes and oversights, like losing track of orders or spending. Data quality management builds an information substratum that sanctions you to understand your organization and expenses by having a firm grasp on your data.
Determinately, organizations require data quality management to meet compliance and risk objectives. Good data governance requires clear procedures and communication, as well as good underlying data. For example, a data governance committee may define what should be considered “acceptable” for the health of the data. But how do you define it in the database? How do you monitor and enforce the policies? Data quality is an implementation of the policy at the database level.
Data quality is an important part of implementing a data governance framework. And good data quality management fortifies data stewards in doing their jobs.
The dimensions of data quality management
There are several data quality dimensions in utilization. This list continues to grow as data grows and diversity; however, a few of the core dimensions remain constant across data sources.
- Precision measures the degree to which data values are veridical – and is paramount to the faculty to draw precise conclusions from your data.
- Completeness denotes all data elements have tangible values.
- Consistency fixates on uniform data elements across different data instances, with values taken from a kenned reference data domain.
- Age addresses the fact that data should be fresh and current, with values that are au courant across the board.
- Uniqueness demonstrates that each record or element is represented once within a data set, availing evade duplicates.
Key features of data quality management
A good data quality program utilizes a system with a variety of features that avail amends the trustworthiness of your data.
First, data cleansing avails correct duplicate records, nonstandard data representations, and unknown data types. Cleansing enforces the data standardization rules that are needed to distribute insights from your data sets. This additionally establishes data hierarchies and reference data definitions to customize data to fit your unique needs.
Data profiling, the act of monitoring and cleansing data, is utilized to validate data against standard statistical measures, denude relationships, and verify data against matching descriptions. Data profiling steps will establish trends to avail you discover, understand, and potentially expose inconsistencies in your data.
Validating business rules, and engendering a business glossary and lineage, avail you act on poor-quality data afore it harms your organization. This entails engendering descriptions and requisites for system-to-system business term translations. Data can additionally be validated against standard statistical measures or customized rules.
In additament to those key features, having a centralized view of enterprise activity through a data management console is a key way to make the process simpler.
How important is data quality management for big data?
Big data has and will perpetuate to be a disrupting influence on businesses. Consider the massive volumes of streaming data from connected contrivances in the Internet of Things. Or numerous shipment tracking points that flood business servers and must be combed through for analysis. With all that immensely colossal data comes more immensely colossal data quality management quandaries. These can be summed up in three main points.
These days there is a rampant repurposing of the same data sets in different contexts. This has the negative effect of giving the same data different designations in different settings – and raising questions about data validity and consistency. You require good data quality to grasp these structured and unstructured sizably voluminous data sets.
When utilizing the externally engendered data sets that are commonplace in astronomically immense data, it can be hard to embed controls for validation. Rectifying the errors will make the data inconsistently erratic with its pristine source but maintaining consistency can mean making some concessions on quality. This issue of balancing oversight with astronomically immense data sets beseeches for data quality management features that can provide a solution.
Data rejuvenation elongates the lifetime of historical information that anteriorly may have been left in storage, but it additionally increases the desideratum for validation and governance. Incipient insights can be extracted from old data – but first, that data must be correctly integrated into more incipient data sets.
Where and when should data quality happen?
You can best visually examine data quality management in action through the lens of a modern-day data quandary. In genuine-life applications, different data quandaries require different latencies.
For example, there is an authentic-time desideratum for data quality when you’re processing a credit card transaction. This could flag fraudulent purchases, availing both customers and businesses. But if you’re updating staunchness cards and reward points for that same customer, you can do overnight processing for this less-pressing task. In both cases, you’re applying the principles of data quality management in the genuine world. Concurrently, you are apperceiving the desiderata of your customers and approaching the task in the most efficient and subsidiary way possible.
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