Mitosheet: Enabling Collaboration
Introduction
Analytics is increasingly infused throughout organizations to help leaders make data-driven decisions. All teams consume and create data that has the potential to add value back to the company. The challenge is that these decentralized teams likely have different analytic tools and capabilities. But, to be most effective, the teams must work together to produce actionable insights.
This article will explore how Mitosheet, a free Python package, facilitates collaboration between disparate analytic teams to help organizations make data-driven decisions.
What is Mito?
Microsoft Excel may be the oldest and most prolific analytic software created. Most analysts are familiar with at least Excel’s primary functions of filtering, sorting, and pivoting data.
On the other hand, Python’s capabilities are superior to Excel, but Python is far from the same adoption rate, even though it is currently the most popular coding language. What if we could have the best of both worlds?
This is where Mitosheet comes in — it is a free Excel-style Python widget that empowers users to analyze big data effortlessly without coding. Mimicking Excel’s user experience, users can similarly interact with data at scale. Where Excel maxes out at ~1M rows, Mito has no limit. Plus, advanced analytics, such as machine learning, can easily be applied since the data remains in Python.
Below is a glimpse of interacting with a Python dataframe using Mitosheet like one would an Excel spreadsheet. You can find more examples at the end of this article.
How does Mito empower collaboration?
Collaboration is the key to actionable analytics as different teams can contribute different strengths. One group may have more sophisticated analytic skills, another may be subject matter experts, and another may be best suited to operationalize the analysis. These teams need to collaborate even if they have different analytic tools or capabilities.
Mito overcomes those hurdles to bridge the gap between teams.
First, Python and Mito are free! Not every team can afford the hefty price tag of Alteryx, Talend, or similar tools. Even cheaper tools like Tableau will likely require involvement from procurement, accounts payable, and IT departments. These hurdles can result in different teams using different tools. Since it’s a free Python package, Mito removes all barriers by not requiring budget dollars or likely involvement from other departments. Python and Mito are easily accessible to all!
Second, Mito offers accelerated time to value with a natural and commonplace user experience. Nearly no instruction is needed if the user has Excel or Google Sheets experience. Analysts will immediately feel right at home when using Mito!
Third, Mito produces insightfully marked-down Python code with each click. This code is easily accessible right below the interface. It can be copied and pasted, edited, and shared between collaborators.
Collaboration in practice with Mito
Let’s see how Mito could help a pricing department needing to generate $20M in gross profit by year-end. Unfortunately, their analytics are limited by having only Excel 2013. To achieve this goal, they will need far more data than they’re used to analyzing. The CFO asks an advanced analytics team to help out, but the teams don’t have the same tools or acumen.
Mito is perfect for bridging the gap between the two teams.
The advanced analytics team engineers the required data without concern for data size. Armed with Mito, the pricing team fearlessly dives into the data. They use Mito with little instruction since the Excel-style widget is a natural way for them to interact with data.
Without needing to know Python, the pricing team can effortlessly import and query the data despite it being millions of rows. Every detail needed to make the best pricing decisions possible is now at their fingertips. As pricing requests come in, the team can effortlessly pivot, filter, sort, and chart the data in near real-time.
Over time, the pricing team develops consistently helpful analyses. With each analysis, Mito produces shareable marked-down Python code. The pricing team can share the code with IT to operationalize the data refresh, analyses, and distribution. By sharing the code, IT saves development time and effort.
With this descriptive analysis in hand, the pricing team can now re-engage with the advanced analytics team. A machine learning Python model is needed to scale beyond manual ad hoc analyses.
The advanced analytics team can collaboratively model since the original analysis is in Python already. Although the pricing team never needed to code, they became familiar with Python since Mito produced the code below the interface. Now they have the foundation to learn more advanced machine learning coding techniques.
This is just one example of how Mito can empower collaboration across analytic teams with different analytical tools and acumen.
Mitosheet: Empowering Collaboration
Mito offers many capabilities to make analysts more productive. However, its ability to empower collaboration between teams may be its most valuable feature. Mito clears the hurdles of analytic teams working collaboratively. Mito allows all analytical groups to effectively collaborate so that the business can rapidly action comprehensive insights.
Mitosheet Functionality
Below are just a few examples of Mitosheet’s powerful, intuitive functionality.
