Every organization that deals with databases may face data fragmentation as of customer data or product data, which may lead to data inconsistency and redundancy. As we can assume, the users may share their data through various input modes through web forms, sales departments, customer surveys, and more. Like sales, accounts, marketing, etc., the internal departments may enter the transactional information also about various customers. So, as the channels and data points for input are increasing, the chances for data redundancy will be. With this, it becomes important to know these major issues related to the reliability of data and adequate measures to tackle the same.
When information about the same customer is entered through different channels using different proprietary data formats, it is easy to ensure that the database’s duplications are avoided. There are many different ways to structure the database properly and ensure standardization of data, but not all are equally effective. Data verification and validation will help to smooth out some of the major challenges of redundancy and inconsistency, but not fully. It is vital to streamline the data so that you have easy access to it. It is also vital as you may need to access the information anytime based on your business requirement.
Understanding data redundancy as a potential challenge
As we see, data redundancy comes up as a challenge in enterprise database management when a data point is multiplied across different databases or database fields in the same database and repeatedly exists in any unnecessary forms. This usually stems from poor database design in which the information is structured inefficiently and gets replicated needlessly within the same fields of a table. Adding to it, redundancy may also arise over time as the database is expanding with inefficient planning on how to implement them properly.
A very common example of redundancy is when the name, address, or phone number present in a database column gets duplicated again within the table. If the link between these data points is defined in each single database entry, then it may ultimately lead to duplication across the table.
Various types of data anomalies
Based on the database type, storage and processing needs may increase. This may further lead to a complicated table with many inter-connected relations, which may confuse some confusion while executing queries. The data updates may not take the same effect across various points, which will lead to data inconsistency. Another aspect of redundancy is having identical information stored at different tables that are unrelated. This is called an update anomaly, which is highly likely to occur as the database develops at various stages, with disjointed handovers and poor architectural plan.
Two other distinct cases may stem from poor DBMS design, which will lead to some severe issues in ongoing database operations. However, these cannot be classified purely as redundancy related issues. One such thing is insertion anomaly, where a relational database model remains rigid and locked down. In contrast, a new addition that falls beyond the scope cannot be included without substantial re-engineering.
Another case is called a deletion anomaly. A database is sub-optimally built, and deleting the final database point in the given table will lead to an unwanted data loss. This may be particular to an element that is not stored anywhere else. For example, if some address details are found in the user table, which is getting deleted when the user profile is deleted and cannot be retrieved further. For data recovery, you can take the assistance of external expert consultants like RemoteDBA.com.
Ways to tackle data redundancy
There are many smart ways to tackle database redundancy at its root. One should put in more time and effort to develop a more efficient structure before implementing the database. If it is not possible, then it is important to have a process for database normalization. The objective of database normalization is to effectively reengineer the tables so that the purpose of each is defined well, and the relation between them are logical and purposeful. This process will also aim to set up the database to make it more scalable and can be retracted in the future without causing any insertion or deletion anomalies. The database built in the OLTP structure tends to be more normalized and can also be highly resistant to any data duplication.
Causes of data inconsistency
In data inconsistency, many tables in a database deal with the same set of data butreceiving it from various inputs. Redundancy related issues usually compound inconsistency. Data inconsistency and redundancy are not the same, even though inter-related. Inconsistency and related anomalies referto the problems with the database content rather than its structure and design. This is more related to the existence of various challenges and data touchpoints and a human propensity to put some creative spin on setting up the inputs, which may further end up in compound DBMS problems.
Minimizing data redundancy and inconsistency
There are two ways organizations use to tackle data inconsistency:
- Central semantic store approach. This involves meticulously focusing on logging all rules for database integration at a sole centralized repository. So, it will be ensured that the data sources are always updated,or new ones get added to not fall outside the rules of data integration.
- Master reference store approach. In this, we try to solve any data consistency issues through centralization. This approach aims to create a unique source-of-truth for reference data and implements strict rules to sync all the secondary tables while the change is triggered in the primary one.
Whatever suits your organization the best, one should implement it without fail. There are many tools available for this purpose as the Microsoft Power Platform, which lets you implement a unique data model across various apps, better data visualization, and workflow automation. With data standardization at its foundation, Power Platform will let you build many business tools by avoiding the scope of data inconsistency or redundancy.