Since the data is a strategic capital asset For today's institutions, it is easy to understand that poor quality is a major obstacle. Exactly for that, different solutions are required to remedy them and also to prevent them, including correction of common problems related to product data.
In this article we will explain why improve the quality of product data brings a qualitative change that enables us to take the business to another level. Fortunately, companies that handle a large number of products often have similar problems that can be fixed with proper data management tailored to their needs.
Some of the most common problems.
One of the most common inflection points around product data is related to lack of correspondence, in some fields, between sales information and information stored in catalogs.
By appointing the same supplier, product or feature (as an example, sizes or colors) in different ways, we cause a mismatch between apparently different categories, when they actually refer to a single category. Due, information lacks reliability.
Errors of this type prevent working with it correctly, both from an operational approach and for reporting product sales. Since we do not have realistic information, among others drawbacks Practically, we face a serious problem for activities as essential as ordering products or supplies.
A lack of reliability that, in summary, negatively affects decision-making and causes problems of various kinds, whether they are logistics (excess or lack of stock) O, as an example, not having good information to make decisions in this regard. .
in addition, a common obstacle is an incorrect or non-existent information synchronization new compared to what was already in the different systems. Simply, knowing how to keep information clean is key, as well as whether it should be replicated between systems or stored in a master data repository.
Detect problems to find solutions
The aforementioned issues must be addressed within the different contexts, depending on the type of project that is carried out, company needs, specifically the objective and objectives to meet with this information.
Once these problems are detected, now is the time to apply solutions that improve the quality and management of data to make fast and correct decisions. Opposite case, we will continue to suffer the consequences: unreliable information, waste of time correcting discrepancies and, with that, a decreased efficiency from work and, therefore, of the company in general.
Data quality problems also make companies losing large sums of money, either because they cannot make a sales strategy for their products or due to problems such as those mentioned. Finally, the problem affects other areas, like customers, which can also be an extension of the products, and vice versa.
Target: clean product information
It is key to apply ad hoc data quality improvement solutions through connectors and applications. At the same time, heThe data stuarts have to solve the problems that arise, not forgetting that these procedures must be applied to new data.
So that the product information is clean Should be directed full flow. After data discovery and lexical analysis (column separation), then we will make a correction by standardizing the records.
We can even find matches in other systems, in which case it would be convenient to merge it into a master data repository, and it is also possible to enrich the data of our products with new categories. In the latter case, as an example, infer information about socioeconomic status from sociodemographic data to cross-sell.
They are operations that are carried out in different projects (migrations, data quality and governance projects, quality and enrichment, MDM projects), whose common denominator is to achieve better data quality.
Keys in the data quality strategy
Everything indicated indicates the The crucial relevance of the data is quality. to make decisions correctly. But achieving them needs to have an adequate strategy, where finding quality problems quickly and easily is critical.
Opposite case, we can spend a lot of time figuring out where the critical points are. In reality, in companies that handle a high volume of data is key, once problems are detected, determine strategies that include tools that facilitate cleaning through automated processes.
It's about a cyclical procedure, that is done continuously. At first it will probably require a bulk action for the entire database and then it will run those same processes for the new records.
This is how we make sure that our data is contaminated, even though the strategies tend to be broader and include comprehensive solutions that, beyond the quality of the information, they also include data governance.
In summary, the best strategies are those that take into account that processes cannot always solve problems, especially when they are specific. At this point it is essential to have the necessary technologies and knowledge clean the data using tools that allow to have automated quality processes adapted to the needs of the organization from a global approach.
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