How to make BI tools R Scripting inclusive?
Most BI tools require coding in languages such as R or Python in Business Intelligence dashboard software. The requirement of one of these languages restricts the R scripting in BI tools to be only for advanced users.
This blog will look at some of the limitations, how one can overcome them, and tips to make Business Intelligence tools R Scripting inclusive.
What are BI tools?
Business Intelligence (BI) tools are accessible to everyone and provide a graphical user interface. The advantage of these tools is they allow a user to view the data visually rather than working through code. They help in building dashboards that are more interactive with users.
BI tools use R scripting to connect the data and transform it. Various BI tools like Tableau, Looker and Power BI are built on top of proprietary scripting languages like Pyspark, Python, etc.
What is R scripting?
R is a programming language that has been used widely by data scientists for almost three decades now.
R scripting uses a programming language called R that is built on top of the base R language. This bridges data manipulation, data integration, data analysis, data visualization, model building etc.
Benefits of R scripting
R scripting has various benefits attached to it. Some of these are:
R is built on top of the base version called R, which is open-source. This makes creating a tool based on R much more flexible and easier over other programming languages like Java or Python.
The tool's code can be easily modified and changed as changes in business needs change.
Also, since it serves as a bridge between different Business Intelligence dashboard software programs, it need not be built individually for each program. Instead, it can work with multiple tools that work into one product.
This helps save time and cost by reducing the number of products an organization develops.
What are some tips to make a Business Intelligence tool R Scripting friendly?
Customization of R scripting takes time and resources. You can still reduce these by further customizing a few functions of R, such as:
The function used for visualizing data- Since this is a standalone function, if it can be modified and kept unchanged for every program, it will save time developing new processes.
The function is used for accessing data from other programming languages like Python and Java. Also, if you can integrate this with every tool, it will reduce the time spent on building new functionalities.
Tips to make BI tools R scripting inclusive;
Use a Gateway:
A gateway is a server that acts as an interface between a BI tool and an R script. The gateway also stores the data during and after the R script's execution, so that it can be used to store other types of data in future as well.
Use Open Source Data:
Many open-source databases, like Hive, Hadoop, HDFS, etc., store and process large datasets. Since these data types have been used widely in various business functions, they are suitable for integrating BI tools with similar functionalities.
Use Data Pipelines:
Data pipelines can be used to connect the data stored in different systems together and make sure they are synchronized. The various databases store data differently, and BI tools need data to be stored in a uniform format that can be used to manipulate.
Hence data piping is necessary to get data into the proper format before it is fed into the tool for use. The approach of creating a pipeline solution will help in reducing the number of steps required for the integration of new tools with old ones.
Concluding Words:-
Organizations can build dynamic, adaptive R scripting inclusive Business Intelligence tools with the help of Grow's BI professionals, which enable teams to make data-driven choices.
You can take advantage of the value and insights from your data by using our one-of-a-kind Business Intelligence dashboard software.
To learn more about our BI services, read Grow Reviews from Verified Users on Capterra.
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