Massive data fragmentation presents businesses with a number of challenges, including lost revenue possibilities and elevated security breach threats. You must have a better understanding of the nature of data fragmentation, its causes, and how to address its issues in order to better grasp the hazards.
In this article, SmartOSC will demonstrate the underlying reasons for the system fragmented issue in businesses in this article.
Why do we confront fragmented systems fragmented?
Data fragmentation is not the result of a single operation. A fragmented data ecosystem will frequently result from the unpredictability of data operations:
Fragmented data stack
Each tool you use (databases, ETL tools, and BI tools) will tend to monopolize its own portion of the pie as you deal with numerous tools. If you don’t synchronize them, they will rapidly veer off course. For instance, you may create certain metrics in Power BI using DAX that are different from those you computed in your Snowflake data warehouse using transformations.
To please the many technical teams and their data use cases, it is simpler to build up distinct development, testing, production, and analytical servers than to make sure they are all in sync.
When dealing with fragmented systems fragmented, each department and team will have its own interests at heart. For instance, marketing will examine user data from the user’s first purchase on the website when counting new consumers. Sales will use the first interaction they had with a customer when calculating the number of new clients.
Without unifying the two definitions, you might rapidly have two metrics that disagree with one another (and double count some customers who talked to sales AND purchased online).
Data fragmentation should not be confused with the distributed systems process
Another technical term used in system fragmentation is “data fragmentation.”
Data sets are divided into smaller groups by distributed database management systems (DBMS) to enhance system performance. The fragmentation of data is another name for this phenomenon.
How does it help? Data fragmentation is used by DBMS optimization algorithms to speed up SQL/NoSQL query processing. The query workloads use less memory to achieve the same results by merely performing queries on portions of all the (large) data sets.
Additionally, system fragmentation may be advantageous for privacy concerns. For instance, a data architect for a healthcare app can create a hybrid fragmentation policy that uses algorithms to split data based on various locations, with one vertical fragmentation for GDPR compliance and EU customers (different backups and data sharing policies) and one for the rest of the world.
The field of distributed system design is fascinating (read more about it here).
However, rather than using that technical word, this essay will concentrate on the problems caused by having fragmented data throughout your processes.
As you can see, the root of all the aforementioned choices is the absence of a comprehensive data management strategy. Additionally, they occur spontaneously when you utilize data. Get in touch with us so we can use KinCloud technology to help you resolve the system fragmented issue.
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