Microsoft Excel has served as the key player in data management. Small and large organizations opted for Excel to effectively handle all sorts of data. Excel spreadsheets can store simple to complex datasets by allowing users to employ different formulations. However, with the growing data handling challenges, Excel may not be the best fit anymore.
Over recent years, data generation has massively increased worldwide. Professional organizations are switching to data modeling services to carry out data management tasks effectively.
The need for collaborative data handling has increased over the years. With dedicated data generation units, companies rely on more than one resource for data entry, formulation, and retention. Moreover, the data mining industry has witnessed considerable growth recently. Although Excel can handle large amounts of data, it lacks the collaborative functionality needed to meet the demands of data miners.
If you are a part of an organization that handles large databases of sensitive information, you may have many concerns. Today we will walk you through some of the essential factors you need to consider to switch to data modeling from Excel.
Reasons to Choose Data Modeling Services Over Excel
Poor Collaborative Architecture for Data Miners
Excel was introduced back in the days when there was no need for collaborative data management. Most organizations (small and large) used to maintain a single database of valuable information. The technical architecture of Excel lacks the ability to encourage collaborative data management. This increases the chances of inconsistency and poor communication between data handlers.
Data mining is a complex process. It requires multiple resources to create, optimize, and manage large sets of information. With most data moving to digital platforms, relying on collaborative data modeling has become inevitable. Repetition and inconsistency of valuable information can lead to massive trouble. Data modeling services are designed with a highly collaborative architecture to ensure flawless data management.
Lack of Standardized Rules for Collective Data Management
Collective work requires a standard set of rules to carry out task management. The same rule applies to data mining. With the large amounts of information placed on digital platforms, multiple resources access databases. Therefore, it is vital to have a standard set of guidelines to manage data when accessed by multiple resources.
Excel lacks a formal architecture to offer such guidelines to data handlers. With the ever-increasing number of spreadsheets, it becomes difficult for data miners to manage information without collective data management rules. This, again, leads to inconsistent practices followed by different information managers accessing the databases.
No Option for Granular Permissions
Modern data handling software has a technical edge over Excel. Data owners can restrict authors from making changes to certain sets of databases based on their roles. Data mining is a highly sensitive task. Many authors may have access to the same information. However, not all of them can make changes to the data any more than they are allowed to. The option for granular permissions gives data modeling services a definite edge over Excel spreadsheets.
Despite Excel allowing users to apply complex formulas to manage data, it lacks the granular permissions feature. Multiple-author access can lead to increased human errors. Data handlers can easily access and edit all the information stored in small and large databases with no custom restrictions. Data miners cannot afford such risks making Excel unfit for them.
Inability to Run Coding Scripts
Excel has been impressive with a large number of mathematical functions. Data managers could apply multiple formulas to handle information efficiently in spreadsheets. While this may be valid, Excel lacks the ability to run background scripts. Excel may disappoint you if you want to apply more than arithmetic functions to your data.
Data handling software is capable of handling complex data with logic operations. You can apply data management scripts to formulate data using data modeling services. Such functionality is highly fruitful for data miners. They can carry out complex operations without losing consistency.
Multiple User Engagement Issues
As mentioned earlier, Excel’s architecture does not make it favorable for multiple data managers. When authors access the same database for managing information simultaneously, Excel may cause problems. Data miners typically handle large amounts of valuable information at the same time. Any problems in the process can mess up important data within seconds.
Data modeling applications make it super easy for multiple authors to work on the same data. The architecture of such applications focuses on multiple user engagements without compromising efficiency. More importantly, data managers can perform complex operations simultaneously without systematic malfunctions.
Absence of Tracking Abilities
Databases with multiple author access can fail to deliver without tracking options. You may never know who made the changes in the spreadsheets. The absence of tracking abilities can cause irreparable trouble in data mining. While Excel spreadsheets can store large amounts of information, it is fairly impossible to keep track of accountability.
Data modeling software has no such problems. Data miners can easily track all the changes made to the databases. From author information to data changes, everything is auditable. This feature also proves to be highly fruitful when it comes to data discrepancy problems.
How Data Modeling Can Solve Your Information Management Issues
Data modeling services are taking over the traditional practices of managing valuable information. Whether you need to manage large amounts of user data or complex system information, data modeling applications can be your all-in-one choice. From data storage to utilizing information when needed, the user-oriented architecture of modern data handling programs can serve all your needs.
Data management is a complicated process. You may not want to keep switching applications. To get maximum efficiency out of your data management applications, you need to opt for professional data modeling services.
Microsys offers high-tech data management services to small and large organizations. You can find customized models to store, organize, and reuse valuable information with smart functionality. Moreover, the quality of remodeling applications may take you by surprise. The company offers state-of-the-art data management applications built with cutting-edge digital technology at affordable costs.