Most data analytics teams are bogged down with routine works which takes away precious time required for analysis and in-depth insights generation. Automation of data analytics implies using processes and systems to perform the analytical tasks with little manual effort. For example; there can be an interactive dashboard which would get automatically updated rather than manual reports generation. Data Analytics automation is helpful with various tasks like data sourcing, data discovery, data processing, data replication and others. <\/p>\n\n\n\n
Automation can be employed for data analytics in various ways<\/strong> <\/p>\n\n\n\n There can be scripts which can be fed into a pre-established data model. <\/p>\n\n\n\n Script automation steps<\/strong> <\/p>\n\n\n\n Step 1<\/strong> <\/p>\n\n\n\n Create the script file containing all the codes required to perform the data analysis. In our case we are considering a python script for understanding. <\/p>\n\n\n\n With its help, automate the calculation of <\/p>\n\n\n\n And others <\/p>\n\n\n\n Step 2<\/strong> <\/p>\n\n\n\n Create a setup.file necessary to install the package of codes and information like package name, package type, package functions etc. This file should exist out of the folder which contains the script file <\/p>\n\n\n\n Step 3<\/strong> <\/p>\n\n\n\n Create an an __init__. File. <\/p>\n\n\n\n This file tells the language of the script that the folder contains a package. It should be present in the same folder as the script file <\/p>\n\n\n\n Step 4 Pip install<\/strong> <\/p>\n\n\n\n Open the command prompt and navigate to your script package folder. Then enter \u2018Pip install\u2019 for installing your package. Use an IDE such as Jupyter Notebook and type- From file name insights. Create a class object and then call the \u2018automate_analysis() function. <\/p>\n\n\n\n There can be full service, no coding required tools which would automate the tasks. Some of these tools are R or Python\u2019s NumPy, SciPy and Panda packages. These tools help in exploratory data analysis, model selection, conducting statistical tests and more. The analytics systems can be inducted with effective feedback mechanisms, auto run of statistical tests based on parameters and inputs, employ algorithms to process data and auto generate results. The insights from the data are obtained in real time and can be used to transform business decision making <\/p>\n\n\n\n Automating analytics generally implies creation of the model that correctly describes the cause-and-effect relationship of various attributes of data.\u00a0The model then uses predictive algorithms to detect the relationship according to the fed parameters. For example; My neural predictive algorithm finds that your airlines miles point usage is much higher that your normal pattern of usage\u00a0(detected\u00a0by cluster algorithm). So, the company may predict chances of fraud or forgery. These models machine learn and update the behavioural patterns of customers over time.\u00a0The concept of model automation is transforming how insights are used to drive real-time decisions.\u00a0It is important to set the metrics for assessing the performance of the automated processes\u00a0<\/p>\n\n\n\t\t