Mito builds on the best of spreadsheets, Python, and point and click EDA tools.
The mitochondria is the powerhouse of the cell. It's responsible for creating the energy your cells run on - or something. We took Biology was a long time ago.
Mito is the powerhouse of your data analytics workflow. We built Mito to be the first analytics tool that's easy to use, super powerful, and designed to keep your workflow yours forever.
When it comes to data analytics, there's nothin' quite as easy to use as a spreadsheet. For this reason, spreadsheets have been the predominant tool when it comes to basic data analysis for the past 20 years. If you work with data, you've done work in Excel or Google Sheets.
Spreadsheets make it easy to see and understand what data you're actually working with - making it simple to diagnosis how to proceed with your analysis. Once you're ready to start your analysis, spreadsheets make it easy to point and click your way to a completed, informative analysis — no 'coding' required.
But spreadsheets aren't built for large datasets, or complex analytics, or repeatable processes.
If you're looking to analyze large data sets quickly, or to do a complex analysis, or to create a repeatable data analytics process, you're probably looking to use python. Python is the go to language for modern data analytics. It's able to support significantly larger data sets than traditional spreadsheets, allows you to do machine learning and AI analytics, and provides infinite opportunities for customization.
However, Python is much harder to use and less intuitive than spreadsheets. For one, it's a lot harder to gain a visual understanding of your data when it's not all right in front of you. The default visualization of a pandas dataframe, the primary data type for Python data analysis, is not at all interactive and only minimally informative, as it only displays a handful of rows and columns of your dataset. Moreover, actually learning how to manipulate your data in Python in a useful manner can take days or weeks as you learn the necessary syntax and background knowledge to actually complete your analysis. Nobody has ever argued that the pandas syntax is intuitive 😊
Data analytics tools like Alteryx and Power BI were built to address these usability problems, while also giving users similar power to Python.
Alteryx and Power BI are point and click data and analysis tools that have the ability to analyze much larger data sets than traditional spreadsheets, but they limit data workers in two ways. First, unlike programming languages, these tools actually lock you into a specific environment. If you do your analysis in Alteryx, you always have to do your analysis in Alteryx. Not only does this lock you into a specific workflow, but it constraints your analysis unnecessarily. If Alteryx doesn't support the specific analysis tool you require, you're SOL. This lack of flexibility either forces you to fracture your workflow across multiple tools or alter the question/answer pair you're pursuing. (It's also worth noting that tools like Alteryx cost upwards of $5000 a month per user.)