Excel models won’t get you a corner office (but these new tools might)
New AI-powered techniques are pushing business intelligence to the next level, and now you don’t need to know Python to use them.
Data management has always been at the core of supply chain management and demand planning. Unfortunately, disruptions caused by the pandemic have made it even more challenging. Yet, the stakes are higher than ever as businesses look to recover.
According to Forbes, 90% of the world’s data was generated in the last two years, with 2.5 quintillion data being created daily. With this growth of data, data analytics is becoming more complex than ever.
In light of these challenges, pre-pandemic tools will no longer cut it as tackling the pace of change, combined with the complexity of this new post-pandemic landscape, requires robust tools capable of meeting these new demands.
There’s no need to depend on Excel for your data operations:
If you inadvertently change a cell, there would be no way of knowing what happened. In addition, the lack of debugging or testing tools makes it difficult to keep track of errors.
Large chunks of data housed in a single Excel file can quickly slow down the application. If you attempt to split the data into smaller files, your macros may not work, resulting in incomplete or lost data.
Compiling and consolidating data in Excel takes too much time and effort to complete. Data from various sources must be opened, copied, and shared over email, portable storage, or a shared network folder, which must be repeated until all necessary files are sent.
Agile is all about business flexibility. It focuses on developing working solutions - not on comprehensive documentation. Any process or application that begins with a complex spreadsheet may require those who take over to start from scratch.
Collaborative work through an Excel spreadsheet is risky, especially since email is the only way to exchange data. Users often end up with duplicate and inaccurate data due to issues with keeping track of shared files.
Copying and pasting data are common operations within Excel spreadsheets and require manual control. These cannot be automated, so every time you have to calculate new values, you will need to reopen the spreadsheet, reorganize the data, and recalculate.
The lack of automation tools and the need for constant manual control places too many limitations on usage, making it too time-consuming.
Instead, read this report to find out how new tools help overcome data preparation issues and produce deeper and more accurate analyses with no coding or data science knowledge. Specifically, the report dives into the following:
Are you looking to test out these tools? Book a demo with us to see them in action.