Seven Reasons You Need to Move to Professional Data Modeling Services from Excel

The development of Excel by Microsoft is one of the most significant inventions of the last 30-35 years. The Excel spreadsheets have played an integral role in the rapid growth of the financial sector in this century. Every enterprise, small or large, relies on Excel to manage huge datasets and finances. Excel is easy to use and flexible. These are great attributes for data mining and management when you are starting out.

However, the same characteristics become weaknesses (more on that later) as your business grows. Over the last couple of years, the rate of data generation has increased manifold. It has been reported that 90% of all world’s data has been created in the last two years. 

On the one hand, this unprecedented data generation has pushed organizations to have their dedicated data centers. On the other hand, it has also rendered Excel ineffective for maintaining and sharing data and using it in collaborations. Working on spreadsheets is gradually turning into a nightmare. This is especially true for data miners. If you are also struggling with your data mining and management on Excel, you need professional data modeling service.

This post will walk you through all the reasons why you need to move from Excel to data modeling solutions for your data mining and management needs.

1. Stiff Siloization Is Not Good for Data Mining and Management

Excel inherently works with stiff siloization due to its technical architecture, which is not designed for collaborative working. In technical terms, siloization is the act or process of splitting data into separate electronic places with poor communication. Siloization is not good for efficient data mining and management practices. Also, professionals struggle in maintaining data consistency with siloization.

2. No Formal Architecture and Design Guideline for Collaborative Work

Data mining is mostly collaborative work. All team members need to work with one standardized approach while data mining. However, no such standardized Excel approach is there. Data miners start feeling this insufficiency when the number of Excel datasheets continues to pile up. 

The number of Excel sheets with lots of data is growing. Mining them is not easy when there is no formal architecture and design guidelines in place. Also, many people don’t have formal training in using Excel. This lack of training also becomes an impediment for collaborative work. 

3. Easy Duplications with No Master File

There was a time when “copy/paste” and “save as” commands were considered a blessing by Excel users. However, these same useful commands have given birth to productivity killer duplications. When dealing with large sets of data and scavenging them for useful information, duplication in the entries or the entire spreadsheets only makes the job more cumbersome. 

Duplication can also result in the loss of the data’s original copy. Also, there is no way to track the master copy of the file with Excel. Moreover, data mining and management done on duplicate information can also result in erroneous inferences and bad decision-making. 

4. Multiple-Author Accessibility

As mentioned earlier, Excel doesn’t promote good collaborative work. But still, it offers multiple-author accessibility. This feature leads many people into believing that they can use it for collaborative data management and work. However, there is no standard roadmap for a team of data miners to use spreadsheets. 

There are hosts of approaches, tricks, and formulas to manage, maintain, and mine spreadsheets. As the data grows on the sheet, everyone starts using their own Excel skills set. The inconsistencies caused by the users make the data unmanageable after a while.  

The other problem with this multiple-author accessibility is you can’t classify the work. Excel doesn’t feature granular permission assigned to different authors for viewing and modifying different cells. This widespread accessibility of spreadsheets makes them more susceptible to human errors. More error means poorer data mining and management.

5. Work without Data Logic

Excel offers you to perform a range of arithmetic actions on the data. However, it’s just that. The MS program doesn’t run code and scripts in the background that execute logic in line with the nature of the work you perform on the data. This is only possible when you go for specialized data modeling applications. The logic-based operations help you make your data mining efforts more time-efficient.

6. Simultaneous Working Is Unfeasible

If a team of remote data miners works on a spreadsheet through Excel Workbooks, they are in for plain frustration. On a good day, you will be able to complete the task with a lot of back and forth. On a bad day, it gets virtually impossible to run multi-user editing and scrawling on Excel Workbooks. The reason why spreadsheets remain ineffective for multi-user is their architecture, which is fundamentally single-user.

7. No Audit and Accountability

Finally, there is no way to tell and track who is updating which cell or sheet and how many times. If you are working on a large set of data with a team, this shortcoming aggravates an accountability crisis. There is no way to develop audit trails for spreadsheet management. In any data discrepancy stemming from Excel sheets, there is no way to pin down the culprit. 

Data Modeling Services: The Solution to Your Data Mining and Management Woes

If you want to improve efficiency and leverage your information repository, you need to move to professional data modeling services. Whether it is logical modeling, object-oriented modeling, network modeling, or hierarchical modeling, you can get your data modeled according to your organizational requirements. When data is managed through state-of-the-art data modeling applications, it remains easy and efficient to mine it.

If you want to provide relief to your data professional and improve your bottom line by good data utilization, consider getting data modeling services from Microsys. Its experts can create six types of customized data models that lead to better data mining and management practices. Microsys’ mission is to deliver affordable and high-quality digital and technology solutions that enable small, medium, and enterprises to scale their businesses more efficiently.