Many companies across a variety of industries have been influenced by data science. Along with such progress in data science, another field of study turned out to be a dark horse, data analytics. And with data analytics automation, this field of study will be more embraced in industries that can leverage automation.
Automation in data analytics refers to using computer processes or systems to take the place of the human aspect. Building systems that can automate all or a portion of a data pipeline is necessary for automating analytics. The complexity of the various data automation mechanisms varies. Some are straightforward scripts that work with pre-existing data models. In contrast, others are sophisticated, full-service tools that let users perform tasks like exploratory analysis, statistical analysis, and model selection.
Benefits of Using Automation in Data Analytics
The use of automation in the analytical process has numerous advantages. Here are some justifications for why automation is an effective analytical tool:
- Quality insights: While manual human analysis can offer some business insights, it often needs to catch up to the complex insights provided by automated data analytics.
- Speed: Data Scientists or Data Analysts can finish time-consuming or difficult analytics jobs considerably more quickly than is possible by depending on a human because automation requires little to no human input. Decisions can be made faster, and variables can be changed in real-time without human input.
- Financial advantages: Programming a computer to perform the same duties is far less expensive than paying a person to labor. Automation in data analytics allows businesses to save time and money.
- Finding previously unknown unknowns: Data scientists can use automation to test for scenarios they might not have thought of. They explore many more cases to identify the important ones.
- Ability to manage time-varying data: By grouping data into different periods, data from a particular period can be more conveniently retrieved for decision-making.
- Predictive analytics advantages: Predictive analytics is often time-consuming, expensive, and tedious. Finding issues with prediction is much simpler with automated data analytics languages and tools.
- Increased business value: Automation automates routine, repetitive tasks, freeing data scientists' valuable time for more worthwhile activities like coming up with new queries for the data or identifying new data sources.
Data Analytics Automation Tools
To automate various steps of the analytical process, data analysts and data scientists most frequently employ the following five platforms and tools:
SAS Visual Forecasting
When to use data analytics automation
Data analytics automation doesn't feel the gap of a data analyst; it is applicable in specific scenarios only. For indepth analysis and implementation of automation, companies must hire data analysts for the best execution of data analytics automation.
Before implementing data analytics automation, kindly check if it meets the following conditions.
- The project has real insights. By automation of data analytics, either you can resolve the issue of delayed insights causing the loss of opportunities and save the relevant cost saving or offer the potential for business growth by discovering new sources.
- When specific project tasks are repetitive and require more time, certain charges are done only once, which does not need automation unless you wish to increase its accuracy. Small jobs done repetitively throughout the project should be automated, giving the analyst more time for other tasks that require the human to execute.
- Automation for accuracy. As mentioned in the first point of this section. If any lengthy process requires constant human attention to avoid errors, such tasks could be automated. Because there is a chance that the human would make an error after a while, but the automation can go on without any break.
The Future of Automation in Data Analytics
Although it is still in its infancy, data analytics automation is already essential to the speed and effectiveness with which enterprises can derive insights from data. Nearly one-third of organizations have at least one fully automated operation, according to a 2020 survey. According to projections, this number will continue to rise as new machine learning, and artificial intelligence (AI) methods are widely used in the data sector and as more data is generated. This futuristic technologies rely a lot on using Python as a programming language. You may read Python for automation to understand more on the applications and importance of Python for these futuristic technologies.
According to Gartner, Inc. "More than 40% of data science tasks will be automated by 2023. Leading in increased productivity and enhanced utilization of data analytics." And this will give rise to automation in data analytics. The automation in analytics would not affect the job of data analysts; it can only assist data analysts and decrease the effort in smaller tasks and will increase their focus on tasks that require human concentration. In short, the job security of data analysts will be aided by leveraging data analytics automation.
There are other fields as well which are thriving unprecedently with the help of automation, such as robotic process automation. Robotic process automation has given rise to a new role called RPA engineers. By utilizing Robotic process automation, RPA engineers quickly build, deploy and manage software robots eliminating human interaction with the system and human errors.