While Python and R are commonly used for data analysis by IT specialists, it may be quite a heavy burden for those who just started their business path. Young entrepreneurs, as well as big corporations, are not always eager to run local data analysis departments due to the high costs of human resources. They use business analytics tools instead.
There are multiple examples of such tools. Some of them operate as PaaS (Platform as a Service), and some as regular desktop apps. The most popular examples are Power BI, tableau and Google Data Studio. The main advantage is the relative easiness of use. A 10-hour course would be enough to learn how to create interactive dashboards with interactive business data. As a rule, such services have premade templates where a user may insert data. However, it is possible to create your own. Data importation is also not a big deal. Once you have a .csv type file, you import it as you would import a regular .csv in excel through data -> import .csv.
And, of course, it is crucial to conduct the market positioning of business intelligence tools. If drawing a graph where the X axis is the extent to which the tool is difficult to use and the Y axis is the level to which you can get insightful outcomes, then BI tools would be somewhere in the middle. Excel would take the middle-down position, while Python and R would be in the right upper corner.
Besides the easiness, BI apps are capable of visualising the data. All types of graphs that are commonly used by data analysts are available for BI specialists. Usually, it takes from 2 to 4 arguments to make a beautiful graph. The Y axis will take the column with numerical data – income, for example. And the X-axis will take age categories. It is also possible to insert a column with people’s gender to get two lines coloured differently to see the difference between males’ and females’ income within the specific age category.
These all make business intelligence a perfect tool for agile and efficient data visualisation with the following decision-making process.
Links for mentioned services:
- Power BI: https://powerbi.microsoft.com/en-au/
- Tableau: https://www.tableau.com/
- Google Data Studio: https://datastudio.google.com/
In this world of flat-earthers and “alternative facts”, transforming data into understandable and honest information is a matter of societal survival. Thank you for your time!