Excel is a ubiquitous tool anyone with access to a laptop can start using. Even if one is a novice there is no shortage of resources available online to scale up your Excel skills. If you are serious about getting into more complex stuff, you can learn how to write macros. Macros can help you achieve a basic level of automation for routine tasks like KPI reports.
However, Excel has some serious limitations that makes it less than ideal for business intelligence. We have handpicked a few for you –
1. Size limitation is inherent in Excel
Since Excel is not a multi-user software, it is meant to be installed on a single machine and used in an individual capacity. That effectively means that Excel depends on the computing power of the machine it’s installed on for performance.
If you try synthesizing a few hundred thousand rows and as little as 15 columns, Excel starts sputtering and renders your machine useless for what may seem like an eternity. This calls for a super scalable BI tool like DataScout to assimilate millions of rows of data.
2. Joining data in Excel is an agonizingly tedious process
VLOOKUP and HLOOKUP are Excel functions that help you look for data values to eventually combine and categorize data. Since every business uses a variety of tools to manage and measure business activities, they create data silos.
Combining and homogenizing data from different sources is instrumental to extracting meaningful insights. The problem with VLOOKUP and HLOOKUP is that combining data from 2 sources itself is painful. Combining anywhere between 5 to 32 sources is an even more undesirable task.
To address this problem, we’ve built automation features in DataScout to clean and combine data to start picking a bunch of insights from dynamic dashboards.
3. Contextualizing data is impossible in Excel
If you want to discover patterns hidden in your data, Excel is no friend of yours and it’s not going to make it easy for you. After you have painfully combined data from 20 of your critical business systems, you’ll probably end up with 20 different columns and thousands of rows.
Your next task would be to manually scan everything to see if anything is out of the ordinary. We don’t want to be judgemental in saying that you don’t have super human capabilities, but most average users can’t get much out of this exercise. The average business user can always turn to DataScout for discovering hidden correlations without taking the effort to investigate.
If DataScout’s machine learning engine finds correlations, it will present them.
4. Excel cannot detect primary or foreign keys
Primary keys and foreign keys are essential for joining data between two different data sets. Since there is no automatic way that Excel can establish relationships, combining data becomes that much more painful.
This is another reason why a data discovery tool like DataScout is needed to automatically detect relationships.
5. Visualizing complex data in Excel is not possible
Although Excel is known for its ability to help you visualize data, it cannot keep up with the complex layers in your data. As complex as your data might be, it’s likely to be rich with insights that Excel cannot plot on a graph.
6. Excel makes it difficult to collaborate
What’s the point of planning, forecasting, budgeting and reporting if you can’t collaborate with your team? The problem with Excel insights is that they need to be passed around in email threads. Any last-minute changes by business leaders will not automatically reflect on everyone’s machine. Which means there is no guarantee that every single person has the same version of the file.
Fortunately, self-service BI dashboards can be accessed every team member at the same time and they will always reflect real-time data. This means your team can access the same data no matter the location.
7. Excel does not have predictive capabilities
Excel was originally developed for ad-hoc calculations and record keeping, so although it fails to provide any prediction models, it would be unfair to expect it to deliver everything.
We have therefore made predictive modelling a standard feature, so business users can get accurate forecasts. DataScout synthesizes your historic data and ties it with up to date performance to plot future projections.
8. Automation in Excel is confined to creating macros
The ability to write macros for producing predefined results is the extent to which one can automate routine reporting. This of course can be done when the data sources are known. But what if you don’t have the time to sift through all your data sources?
That’s when you need a data wrangling cum data discovery tool like DataScout to identify statistical patterns. DataScout is a machine learning enabled tool that helps you discover correlations you had never considered before.
9. Excel struggles with complex data transformations
Say you have 5 columns of data spanning thousands of rows that need to be combined through complex formulas, you’ll discover the vulnerability of Excel to keep up with your need for insights. DataScout addresses such data transformation issues effortlessly through an easy to use interface.
10. There’s no such thing as automatic category detection in Excel
Excel is a desktop tool that cannot be expected to have any smart capabilities that are confined to big data analytics. Category detection however is an important requirement for enterprises to uncover the stories hidden in their data.
Thanks to a variety of natural language processing algorithms, DataScout sifts through structured as well as unstructured data to categorize it appropriately. Automatic category detection enriches and simplifies your decision-making needs.