In this blog, we will discuss, how you can Improve your Data Quality using PIM.
The more your product catalog grows and the more sales platforms you use, the more likely it is that your firm is dealing with a wide range of product information.
There are several possibilities for data mistakes, duplication, and irregularities.
If you’ve ever struggled with reporting or developing a data strategy from scratch, you’ve probably struggled with data quality.
This is where PIM software comes in.
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Let’s look at the best practices for product information management to discover what the most important PIM elements are and how eCommerce leaders have benefited from using a PIM system.
What is Data Quality?
Data quality is a measure that assesses the state of data based on characteristics including correctness, completeness, consistency, dependability, and timeliness.
Evaluating data quality levels may help businesses discover data issues and determine if the data in their IT systems are fit for their intended purpose.
As data processing has grown more firmly connected with business operations and companies increasingly employ data analytics to assist and drive business decisions.
Typical Data Quality Issues
Implementing a PIM system without first doing a data quality study might cause problems throughout the project.
There are a few ad-hoc data profiling activities that may and should be done during the requirements process.
Implementing a new solution is an excellent opportunity to start again, and some appropriate housekeeping requires.
The following are some of the most prevalent data quality concerns we’ve encountered:
- Primary keys aren’t entirely unique.
- ID or Product record duplication.
- Fields that are or have been “free form,” resulting in non-standardized fields.
- Date fields that are not valid.
- Items that have abandon.
It’s conceivable that without a Data Quality workstream during implementation, the solution will produce an erroneous Product identification.
If this architectural flaw isn’t discovered until late in the game, the team may be forced to scramble to recover.
Multiple Factors for Data Quality:
We examine the many components of data quality that have an influence on the dimensions of our dataset, as well as the significance of data quality in the organization that generates it.
84 percent of marketers that buy demographic data believe accuracy is very essential in their purchasing decisions.
The accuracy of the data relates to how effectively it represents the real-world situations it seeks to depict.
There are no gaps in data if it is comprehensive. Everything that was meant to collects successfully.
If a client, for example, missed numerous questions on a survey, the information they provided would be incomplete.
The information you gather should also be valuable for the campaigns and projects for which you want to use it.
Even if the information you collect meets all of the criteria for quality data, if it is not relevant to your goals, it is useless to you.
The term “validity” relates to how the data is gathered rather than the data itself.
Data considers legitimate if it is in the acceptable format, of the correct kind, and falls within the appropriate range.
Timeliness relates to how recently the event represented by the data happened. In general, data should captures as quickly as feasible following the real-world occurrence.
It should be the same whether comparing a data item or its counterpart across various data sets or databases.
Consistency refers to the absence of differences across various versions of the same data item.
What Are the Advantages of High Data Quality?
Good data management is essential for staying ahead of the competition and capitalizing on opportunities. High-quality data may also bring a number of tangible benefits to organizations.
More Informed Decision Making
Better Decisions Improved data quality leads to better decisions throughout a company. The greater the amount of high-quality data you have, the more confident you may be in your judgments.
Good data reduces risk and can lead to continuous improvements in outcomes.
Improved Audience Targeting
Improved data quality also leads to better audience targeting. Without high-quality data, marketers are obliged to apply it to a large number of people, which is inefficient.
Worse, they may have to make educated guesses about who their target audience should be.
Data Implementation Made Easier
Poor-quality data is also far more difficult to use than high-quality data. Having high-quality data at your fingertips boosts your company’s productivity.
If your data isn’t comprehensive or consistent, you’ll have to spend a lot of time correcting it before it’s usable.
What is PIM?
PIM stands for Product Information Management. It is the process of managing all of the data, information, and other materials required to promote and sell items.
PIM also facilitates the creation of high-quality data for internal usage and multichannel distribution.
Akeneo is an open-source PIM system that assists organizations in increasing productivity by providing their teams with the most effective, high quality, and correct product information across many channels.
It creates a central store for all of your essential product data and Akeneo provides multi-channel data importing and exporting capabilities.
Akeneo SEO Manager is a powerful tool for improving Akeneo’s SEO. The SEO analyzer is available to the admin for assessing the SEO of the items and product models. The rating scale is shown as a percentage.
If your data is corrupted for any reason, you may save it by creating a backup using Akeneo Backup Management, which can help you restore it if something goes wrong.
You can connect, consolidate, and manage any type and amount of information throughout your organization using Pimcore’s PIM!
The most significant benefit of utilizing PIM is the ability to work from a single location with several domains, regardless of size or industry!
In addition, PIM enables you to automate the development of unique product information from master data.
PIM can help you to Improve Data Quality in following Ways
Whether you just print catalogs for your clients or sell online, information is viewed by humans or under Google Shopping, one thing is certain: you will need to showcase your items properly in order to sell well.
PIM becomes a must-have for a variety of reasons, but notably for data quality.
Data is Missing
Typically, a product’s information must have a basic set of details in order to fulfill standards for publishing in customer-facing media such as a printed catalog or an online shop.
PIM standard features such as data inheritance also play an essential role in ensuring that your product catalog is not lacking any data by automatically filling in common data for product categories.
Data Precision and Consistency
A PIM system allows for data standardization on several fronts. PIM allows you to have uniform descriptions for your items, whether it’s units of measurement or feature values.
Reducing human effort on product data also reduces unnecessary data duplication. This may be avoided by having a strict structure in your PIM and a well-thought-out product tree.
PIM’s easy bulk data imports from suppliers ensure that you always have up-to-date information about your goods.
However, Pim’s supports full automated updating for all sales channels, your sales channels will update in a timely way if product data changes.
Multiple Hands in the Pie
When your product data disperse among Excel files, it is difficult to govern who has the authority to do what.
It is fairly unusual in such settings for data discrepancies to generate by people who accidentally make the incorrect changes, or make the appropriate changes in the wrong locations.
The time has come to recognize that data cannot treat as a residue of an organization’s processes. The intelligent enterprise’s product is information, and its raw material is data.
Organizations must bite the bullet and engage in data quality improvement techniques since the quality of the product can only be as good as the quality of its raw materials.