Data analysis is one of the core functions within a business right now. With data analysis, businesses are able to convert the raw data into useful insights, statistics, or inference to make data-driven business decisions. However, the task of choosing the right data analytics tool can be daunting since there are a number of such tools available in the market right now.
The best part about these analysis tools is that each of them are designed for a specific use case. However, if you find data analysis tools with similar functionalities, then the one offering speed or agility will be a better fit. With that in mind, let us take a look at some of the most popular tools available right now for different use cases and how to choose the right analytics tool.
Microsoft Excel is arguably the first data analysis tool that will come to mind whenever the word data is mentioned. It is used widely across industries for things like storing the data, editing the data, and for creating graphs with that data. You can also use Google Sheets as the online alternative for those similar functions. Excel is widely acknowledged as a versatile tool for creating graphs and charts.
It supports a variety of chart types including bar charts, stacked bar charts, clustered bar charts, pie charts, et cetera. There is also support for colour customisations and the resizing feature is extremely useful. Excel can also be used for data manipulation thanks to the support for functions and formulas. When in doubt, Excel can be your first tool for data analysis. The popular use cases include calculations, pivot tables, and data manipulation.
Python is a free, open-source programming language that is popular among data analysts and data scientists. It is ideal for use cases such as machine learning, app deployment, and big data. The biggest advantage of using Python for data analysis is that it is fast, advanced, and contains a lot of libraries to aid your workflow. Python becomes ideal for big data since it is extremely fast at handling big data.
The open-source programming language is also flexible with smaller datasets. By automating processes with Python, data analysts and data scientists can save a lot of time. There are also over 2,00,000 packages with popular ones being Matplotlib, Plotly, and Seaborn. Python is ideal for advanced users and the drawback being that it is not good for mobile apps. Some of the popular use cases of Python include cleaning data, exploratory data analysis, and automation.
R is another advanced level analysis tool that is similar to Python in their capability to handle big data. However, R is a better tool for statistical analysis procedures and thus considered ideal for exploratory data analysis (EDA). If Python is a general tool then R is a specialised tool, meaning it is specifically designed for data analysis.
Data analysts also consider R to be easier for common analysis than Python. Thus, R becomes ideal for use cases such as advanced statistical analysis, big data, and machine learning. However, it cannot be used to create production grade apps like Python. If you want to do advanced maths, R is the tool for you.
Microsoft Power BI is a business intelligence tool that offers support for a number of data sources. The tool makes it easier for business managers to create and share reports. It also supports visualisations and dashboards.
Power BI is used in a business environment to combine a group of dashboards and reports for simpler distribution. Since Power BI is from Microsoft, the tool also allows users to build automated machine learning models and it also integrates with Azure Machine Learning. If your enterprise already uses Microsoft tools then Power BI is a good option for data reporting, data visualisation, and for building dashboards.
Tableau is another popular data analysis tool that allows users to create reports and share them across desktop and mobile platforms. It supports data visualisation and analytics, and the resulting report can be shared within a browser or embedded in an application. The advantage of Tableau is that it can be run on the cloud or on-premise.
The Tableau platform runs on top of its query language called VizQL. It translates drag-and-drop dashboard and visualisation components into back-end queries. Another advantage of Tableau is the minimum need for end-user performance optimisation. However, Tableau lacks support for advanced SQL queries, which may not appeal to advanced users.
Akkio is a beginner tool that is ideal for those wanting to get started with their data. It is an AI tool where you upload your dataset, select the variable that you want to predict and Akkio builds a neural network around that variable. This is ideal for predictive analysis, sales, and marketing, and does not require any prior coding experience.
The platform uses 80 per cent of your uploaded data as training data and 20 per cent is used as validation data. Akkio is also unique because it does not predict results and instead, it offers an accuracy rating for the model and weeds out false positives. Akkio is limited to table data only and cannot be used for object detection or image classification.
SQL is a standard language used for storing, manipulating, and retrieving data in databases. It might sound similar to Excel in terms of functions but it is much more efficient and is capable of handling big data. SQL is also faster than Excel and allows users to store data in plain text files, which take less space.
SQL is also used to join multiple datasets together, but there is a learning curve attached to it when compared to Excel. The best use cases of SQL include big data manipulation and joining multiple datasets together. As for querying and manipulating big data, there is no better tool than SQL if you can learn the language.
Data analysis: how to choose the right tool for different use case
There are a number of factors to consider while choosing the right data analysis tool. These range from understanding about your data, technicality, company budget, data visualisation, and size of the data. Here is a look at some of the factors to keep in mind when choosing the right data analysis tool:
- The type of data that you want to analyse within your organisation
- Data integration or data modelling requirements
- Size of the data that you will be working with including a clear distinction if it is big data
- The level of technicality required for the job. This basically translates to distinction between data scientists, data analysts, and marketing and sales people
- Need for any specific type of data visualisation
- Ability to replicate your data sources in a data warehouse
- Assessment of data security and data governance
- Pricing and licensing model of the available tool
The three factors that you should keep in mind before choosing from the above mentioned tools is the type of data, budget of your organisation, and expertise level. A data scientist will often be seen using a combination of tools to get their job done. This is a great approach since it offers flexibility in terms of tools and could lead to improved productivity. For beginners, the best approach would be to get a free trial of these tools wherever applicable and see if they are versatile for your use case.