Data analytics, as a professional skill has come about in so much demand in the last 8-9 years as a result of the capabilities of companies all over the world being able to capture the customers data relating to how to purchase or how the company manages its own financials. Evolution in software development has enabled this capacity. However, there is this constant debate around whether or not the same the software tools used to create the data capturing capabilities can be used for data analytics or do we need more dedicated software tools to achieve the same.
One such tool is the SQL based database software, which essentially forms the backend for any operation. It is used to perform queries to get information from data sources. Audit Command Language is another tool, which is dedicated for only data analytics projects. Let us see the major differences between these tools, to settle which approach is better.
1. Easier Interface – The first advantage of ACL over SQL based tools is the user interface. For any end-user trying to work some on analytics project, they can very well complete basic routine tasks to get to some very insightful reports by simply using the GUI. Please see demo of ACL GUI below:
2. Workspaces – One defining feature of the ACL tool is the “workspaces”. Workspaces allow the end-user to write simple lines of code for data manipulations and test the results without having to actually run any scripts. The workspaces allow the flexibility and another layer to the design process of the ACL project to execute the desired tasks.
3. Scripting – Scripting in ACL Audit Command Language is a combination of scripts and workspaces. most data manipulation tasks like creation of new formatted fields from the raw data are mostly performed in the workspaces, where the syntax for scripting is fairly simple. Other than workspaces, most tasks (such as summarizing, filtering, sorting etc.) data can be performed simply by using the GUI provided by the tool. If any user is inclined to learn how to script (with respect to analytics tasks like summarizing, sorting, appending, joins etc.), the logs give the scripts relating to all such tasks performed using the GUI.
ACL Audit Command Language can be considered an entry-level skill that can be used for almost any kind of analytics projects especially, in the audit/ risk analytics domains. It acts a refined layer of software on top of the data captured in ERP systems. It is easier to catch on for professionals who do not have a technical background and almost takes like than a day maybe, if you have a technical educational background. For these reasons, it is easier to train a workforce on this tool, because of the easy learning curve and the relatively affordable investment as compared to SQL, SAS, HADOOP/HIVE infrastructure.