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Guidelines to Monitor Data Quality Management with the Right Metrics
No matter in which industry you are operating and what products you are selling, once in a business lifecycle, you will come across the anecdote “the importance of data.” It is indeed playing a crucial role in empowering the decisions of entrepreneurs based on trends, facts, and statistical numbers. But managing immense data as well as data quality is where most of the businesses fall apart, and the reasons are uncountable- be it lack of resources, lack of time, etc.
That’s the reason, in this article, we are sharing some guidelines that will help you monitor data quality with appropriate metrics for your business, along with effective data management solutions.
What is data quality management (DQM)?
As the name suggests, it is a set of practices that help businesses to maintain data quality and achieve better results. DQM is all about getting hold of data and implementing sophisticated data processes for an efficient allocation of data. Effectual DQM is acknowledged as an indispensable part of any reliable data breakdown, as the quality of data is vital to obtaining actionable and accurate information from your database.
There are innumerable strategies you can adopt to maintain DQM in your organization and prepare yourself for every upcoming challenge in this digital era.
Why should you opt for Data Quality Management?
Presently, every company’s strategic and operational decisions greatly rely on data, so the significance of quality has become higher than before. And certainly, poor data quality is becoming the major cause of failure. According to experts, $9.7 million businesses in the US have been the victim of such failure, hampering the overall productivity, ROI, and business brand value.
From the supply chain to customer relationship management, an efficient DQM can positively impact your business organization dramatically. Besides quality data, DQM can also help you set up a data storehouse with respect to the probing trends and devising great strategies that can guide you through future challenges.
What are the techniques used behind DQM?
Now that you’ve understood the significance of high-quality DQM in your organization, let’s take a look at the top techniques used behind DQM and its five supporting pillars:-
Right Staff in Place
Though there has been a great evolution in technology, human oversight is still not out of the picture. You’ll require experts to monitor data flow in your business, as automating everything can expose you to big trouble. So, the following are the people whom you should appoint in your business:-
- DQM Program Manager- A DQM program manager is responsible for monitoring daily data activities but on a higher level, which involves project budget, data scope, and program implementation.
- Organization Change Manager- As the name suggests, the change manager’s core responsibility is to organize data. An Organization Change Manager provides clear insights into advanced technology data solutions.
- Data Analyst- This person ensures effective communication of thoughts regarding data quality across departments by defining the organizational need for quality.
It is an inevitable process in DQM, which involves:-
- Reviewing data to the core
- Implementing statistical model
- Contrasting and comparing data to its own metadata
- Generating report related to data quality
This procedure takes place to develop insights into the existing data of the organization in order to compare it with the set business goals. It is also the initial step for a data quality management process and sets standards for businesses on how they can improve the available information.
Defining Data Quality
This is the third support pillar in DQM, which refers to “quality” itself, enabling businesses to set “Quality Rules” based on organizational goals. Your data should comply well with these rules in order to make it viable. However, your business needs to play a front role in this pillar as significant data elements depend upon the industry.
In DQM, the reporting process involves recording and removing all the bad data. It is advisable for businesses to treat this as a usual process in data rule enforcement. After capturing and identifying irregularities, combine them effectively to recognize the quality models.
This is a two-step process that involves:-
- The best tactic to rectify data
- The most efficient manner to formulate the change
The rectification of data is all about finding the root cause of where, why, and how the data has defected. It is also the position where data quality rules should be reviewed, helping you to understand where the rules need any adjustment or upgradation.
How do we measure data quality?
Measuring data quality is of utmost importance, for which you should know the data quality metrics. We have an acronym for metrics, “ACCIT,” which means Accuracy, Consistency, Completeness, Integrity, and Timeliness.
Accuracy: Accuracy of data should be measured via source documents, whether it is about the business transaction or any status change that happened in real time. But if you don’t have source documents, you can opt for an alternative confirmation technique, which will specify whether the data is void of important errors. The general metric for measuring data accuracy is the ratio of data errors that tracks the total number of identified errors according to the data set.
Consistency: To be honest, consistency means that two data values captured from distinct data sets should not clash with each other. So, consistency does not directly imply correctness. For instance, consistency is responsible for verifying the total number of employees in each department of a company without exceeding the number of employees.
Completeness: Completeness is specified to recognize whether the organization has adequate information to depict conclusions. It determines whether the data captured is complete or incomplete, as data entry includes various fields that must be filled in properly.
Integrity: The integrity process is also called data validation, which defines the structural testing of data to make sure that the data meets the terms of different processes. Integrity simply means that there are no unintentional data errors, and it suits its appropriate designation.
Timeliness: It is all about accessibility and availability of information. Simply put, it measures the time between data anticipation and the minute when it is obtainable for use.
We believe that this article might help you to measure the data quality in your organization with the right metrics. If, despite implementing multiple strategies, you remain dissatisfied with the quality of your business data, it may be time to consider outsourcing. Contact Cogneesol, a team of highly skilled and professional data entry operators with the required management skills, offering quality data management services worldwide. To learn more about us, call us at +1 646-688-2821 or email at [email protected].
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