Importance of Data Quality Management Process For Businesses
Digital transformation has promoted several innovative things to be included in business operations. Among several initiatives, improving data quality of product information has played a key role for business success especially those involved in eCommerce activities.
Quality data has become so crucial for businesses in the past few years, they are the most powerful weapon to tackle the modern data challenges that businesses are facing in this digital age. This article briefs about the importance of having a data quality management process within a business operation.
What is Data Quality Management?
Data quality management (DQM) is nothing but a set of processes done by professionals to maintain the high standard & quality of the product information. The data quality management process involves data acquisition along with the implementation of advanced data processing techniques.
Apart from that, DQM does play a significant role one data distribution to multiple sales channels effectively along with boosting the managerial oversight of the product information by the business leaders. When it comes to data management, you can find numerous strategies and methods suggested by experts to improve product data quality.
However, the DQM process helps businesses to handle all those modern challenges faced by eCommerce businesses in this digital age.
Pillars of Data Quality Management Process
Before diving into the core topic importance of data quality management of a commercial business, let us discuss some basics of the DQM process along with its pillars and techniques being followed.
DQM Role
Data quality management performs multiple roles in an organization or eCommerce business. Roles of program manager, organization change manager, and business & data analyst, are performed silk & smooth by the DQM.
Data Profiling
It is one of the essential processes which every business needs to do. This process involves developing essential insight into the already existing product data/information. In general, data profiling involves data reviewing, data comparison, running & analyzing statistical models, and generating reports over the data quality.
Defining Rules of Data Quality Standards
Defining data quality is the most significant pillar of DQM, it determines the data quality rules based on the business goals & requirements. This set of data quality rules ensures product data comply with the business values & requirements. The data quality rules set predicts the success of the DQM process.
Data Reporting
Reporting & monitoring the standards & quality of the product data is the most vital part of the data quality management process. It enables business leaders to view the state of the data in real-time. Moreover, it promotes business leaders to identify the data quality patterns to be followed for business success.
Fixing Defective Data
Fixing defective data also known as data remediation is the process of finding the root cause for the defective data origin and fixing it. The data repair process under the DQM is carried out in two steps, finding data remediation in the best way and finding the most efficient manner to implement the data changing plan. In addition, this process also enables a business leader to review the data quality rules set by the DQM process.
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How Does Data Quality Management Work?
The data quality management process involves various approaches that will guide you to understand the data standards and identify the root cause of the poor data quality. By implementing data quality management, you can save a lot of money and time on spotting poor data and carrying out data remediation.
This section will clear you how data quality management works for every commercial business including eCommerce.
Data Governance
It is an operation that allows eCommerce business leaders to take control over the process of data collection, data storage, data distribution, and data usage. In general, data governance is always considered as the core for every business and even termed as the business intelligence that predicts the root of data quality.
Often companies fail in creating awareness over the data quality standards among their employees/staff and suffer a lot.
Data Cleansing
Data cleansing is a set of operations carried out under the data quality management process to identify inaccurate and duplicate/corrupted data. A typical data cleansing operation will eliminate a significant amount of low-quality product data.
So, if you want to run a robust eCommerce business with better product data quality, then conducting a data cleansing operation is an essential process to do.
Data Matching
As the name suggests, it is the process of comparing two identical data present in two different data sets. By doing so, business leaders can easily eliminate the risk of having data duplications and also ensure their product data are more precise & relevant. Apart from that, most businesses including eCommerce carry this data matching operation to compare their product data with their business competitors to improve it to attract more consumers.
Master Data Management
For every business master data is the core entity. A master data is a consistent and uniform data set that predicts the current marketing trends. Master Data Management is a kind of data management approach carried out under the DQM process to attract more target audiences.
Product Information Management
It is the process that collects and stores all the details/information of the products in a single repository. Product Information Management enables various departments and business entities to access product data easily and quickly. Every product catalog preparation can be done at ease with accurate and updated product information.
Digital Asset Management
It is a set of processes that stores, manipulates, and shares the digital assets of the product from a centralized location. Digital Asset Management enables eCommerce businesses to drive the attention of their viewers towards their products and convert them into customers.
Customer Data Integration
It is the process of consolidating all the details or information of the customers in a single access system. By doing so, business leaders can easily connect customer data with other data sets for effective business operations like product shipping, new products marketing, product sales, and so on.
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How to Measure Data Quality?
Measuring the quality of data requires certain metrics. Before taking steps to measure the quality of your product data, make sure data quality metrics are at top-notch and you must be very clear on defining them.
Let’s take a look over those data quality metrics and their categories in detail.
Data Accuracy
As the name suggests, it points out the degree to which the data accurately reflects the nature of the products listed on your eCommerce store.
Data Completeness
Data completeness refers to the data which fulfills all the expectations of the buyers of your eCommerce store. In simple terms, data completeness indicates how meaningful it delivers about the product and offers a clear thought over the products to consumers.
Data Consistency
In general, eCommerce store utilizes multiple channels to drive the attention of potential buyers. Data consistency simply makes sure the data provided in those multiple sales channels do not conflict with each other.
Data Integrity
Data integrity can also be termed as the data validation carried by a structural testing operation; this ensures the product data posted on your eCommerce store is error-free and appropriate data types.
Data Timeliness
Data timeliness is a set of operations, which determines whether your product data are ready and visible to the consumers when they are in need. This makes sure your product data posted on the eCommerce store fulfills all the data quality dimensions sought by the consumers.
Apart from those metrics, there are a few more data quality aspects that an organization or eCommerce store should give importance to measure their data quality. Take a look over those listed aspects below.
- The ratio of Data Errors
- Data Transformation Error Rate
- Data Storage Costs
To ensure high data quality, Credencys apply all the above-listed data quality management practices and ensure the quality of your data to ensure delivering accurate and consistent data.
Why eCommerce Business Requires Data Quality Management?
Well, the above sections will clear you about how data quality management works and it may give you a glimpse of the importance of having a proper data quality management process for your business. Moreover, having bad data will cost you a lot in terms of business success like revenue loss, reputation loss, profit cut, time delay, and so on.
Here this section will elaborate on why your business requires a dedicated data quality management strategy. Having good quality data will always boost confidence among the people who manage the business operation and also provide better customer satisfaction for consumers. Let’s take a deep look in depth.
1. Increases Business Revenue
As businesses are initiating digital transformation all over the world, having accurate and high-quality product data becomes inevitable to attract more potential consumers and also to beat the competitors in the crowded market.
Having a proper data quality management process in an eCommerce business will boost its revenue by providing quality product data right from the launch of a product, this will decrease product return and trigger consumers to shop again once they experience a good shopping experience.
2. Reduces Business Cost
Businesses around the world are ready to spend more on providing quality product data to their consumers, aware of its importance to sustaining in the eCommerce business. However, with a strong and quality data management strategy & process eCommerce businesses can cut a significant amount of money.
Effective data management process will allow you to stop spending huge amounts of money on marketing campaigns across multiple sales channels, by providing quality data by itself. This enables marketers to concentrate on generating more sales and concentrate on improving ineffective channels, rather than spending money on generating quality product data.
3. Eliminates Data Reconciliation Problem
Data Reconciliation (DR) is the term commonly used in the business world for referring to the data verification process that takes place during the data migration operation. It is a process where employees/staff spend a significant amount of time to compare the target data with the original data source and ensure the data migration process happened correctly.
The data quality tools inherited with the data quality management strategy will do this data reconciling process automatically and streamlines the data migration process smoothly. This provides huge relief to the employees and saves a significant amount of time and allows them to concentrate on other business operations.
4. Boost Confidence Among Analytical Team
The analytical team for an eCommerce business plays a key role in predicting business growth and taking prompt initiatives to drive the business operation towards its success. Often eCommerce business leaders depend upon the reports provided by their analytical team.
Having a proper data management technique will ease the tedious job of your analytical team on collecting the business revenue details of your eCommerce business. Also, this enables analytical team members to take correct steps for your business growth.
5. Provides Better Customer Satisfaction
Having a good data management tool will increase the usability of your website by the users. In addition, good quality product data will allow customer handling staff to provide better services to consumers. This set of operations eventually increases customer satisfaction and leads towards boosting the sales ratio.
Good quality product details or information will enable consumers to have a better view of the products and plays a huge role in the target audience to make a purchase decision. Also, it will trigger them to visit again and shop for more products again and again.
Well, a recent study report conducted by the experts in 2021 suggests more business leaders especially eCommerce business owners demand quality data insights and the best data-driven decision to meet the expectation of the modern consumers on purchasing products.
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Bottom Line
The data management process has become the most crucial aspect for every business that includes eCommerce too. Also, the data management process assists business leaders in taking important business decisions based on their business operations.
The importance of having your product data in high-quality using proper data management processes and tools is mentioned-above. If you are experiencing real pain in maintaining high-quality product data, then it’s the right time to implement better data quality management strategies and techniques.
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