Data Analysis – Will it be Automated?

Simple repetitive tasks like conducting a basic linear regression by hand, which is quite an uphill task, even with the aid of a calculator, were once commonplace. While nothing is wrong with this, it is restricted in terms of the amount of data that can be processed this way, and it uses a lot of laborers.

But as technology developed, more and more processes became automatic. And by the early 1980s, even a mediocre programmer was competent enough to use a programme to complete such menial chores. Do you see what I mean? Many aspiring data scientists are concerned that AI will soon replace them after a large portion of data analysis is automated due to the current age's high processing capabilities and AI's extraordinary strides.

Therefore, in today's article, we'll discuss this in detail, looking at things from various views before deciding whether or not you're correct in thinking that AI will inevitably play a role in data analysis. So let's get going!

What is Data analysis?

Data analysis is one of the most important aspects of a data science endeavor. Data must be handled in every conceivable manner before being fed to the model for training. Data analysis ensures that you are giving your algorithms the right data which you can learn via the professional data analytics courses online. 

The essence of data analysis is the process of cleaning, analyzing, transforming, and modeling data to discover pertinent insight for improved organizational decision-making.

Can Data Analysis Be Automated?

Yes, for the most part. Data cleaning, processing, and ETL processes all involve a lot of repetitious and illogical steps. As a result, automating them is relatively easy. However, automating activities like identifying minute adjustments or tracking patterns may be difficult.

To respond to this query more precisely, you must know which data analysis area you're referring to. In the following sections, we'll examine some areas that have already undergone some automation and others that aren't likely to do so anytime soon.

What is automated analytics?

The practice of using software to analyze data with little to no human involvement is known as automated analytics. This could be as easy as executing a script that has already been written to change some data before it is added to a specific table.

Once these scripts are created and used, they could revolutionize productivity because they would save significant human resources and make the process quicker and error-free.

Automated statistics are being used by many businesses today to streamline their operations. However, this has only been demonstrated to be effective for a small subset of processes, and since these processes are not subject to substantial change, no human thought is required.

A few real-world illustrations of data analysis automation

• Data Gathering

You must first gather sufficient data before you can begin the data analysis. Before beginning data analysis, the first stage that must be completed is always data collection. However, gathering data from third-party apps necessitates sifting through countless excel files or creating time-consuming programmes because data is dispersed across numerous locations.

You can save a lot of time by automating the data-gathering process to move right into data processing or cleaning. In some circumstances, you can handle the data cleaning/processing step, though it's challenging.

• Extract, Transfer, Load, or ETL

Extract, Transfer, Load is referred to as ETL. When you have raw data in your database, you must modify it to meet your requirements or make it consistent with the other data. This is the process that takes place after that.

ETL can occasionally include data gathering as well. However, it typically begins once you have collected the data and must perform certain transformations. Automated ETL solutions help businesses save time for their resources and put that time to use better. For detailed explanation of ETL process, refer to the data analytics courses, and learn directly from the industry tech leaders. 

• Dashboards

Dashboards contain graphics that assist in calculating KPIs and other metrics. The process is very repetitive once you've created your dashboards and determined the images you need. Building end-to-end pipelines that are linked to data sources and give you pertinent information without human intervention allows businesses to use automation in such situations easily.

• Automating Data analysis Challenges

You presumably have a good idea of how far we have come in automating data analysis by looking at the example discussed above. To completely automate data analysis, however, there is still a long way to go, and, to be honest; it doesn't feel like we're getting there any time soon.

Let's examine some of the most significant roadblocks to automated data analysis.

• Human Capabilities for Issue Context Understanding

The ML industry has experienced a rollercoaster of a decade, and we have seen some ground-breaking developments that have raised the bar for AI powers. The increase in processing power has made the majority of this feasible. However, innovations like GPT-3 are true marvels.

That said, AI is still very far from comprehending problem contexts as people do. Regarding situations that call for common sense, even programmes like GPT-3 stumble; even 175 billion parameters cannot replicate common sense as it exists in humans.

Do you see what I mean? Even though the technology is advancing at an unprecedented rate, it still appears that machines still need to reach the level of thinking humans do, which is required for completely automating tasks like data analysis.

• Innovation

The second primary reason why computers alone are insufficient for data analysis is a lack of innovation. If you follow data analysis closely enough, you know that many different methods are used for various situations and that sometimes particular techniques are developed to deal with particular datasets.

As a result, when presented with a marginally different situation, machines cannot extensively think creatively as humans do. AI will only do what it has been taught to do; it cannot successfully use all of the knowledge at its disposal to solve a problem in a novel way. And if we want to see people and machines interacting, this is one of the biggest gaps to close.

Conclusion

In the modern world, data analytics is a booming industry, and it is expected to expand as data output increases. With the tremendous growth that AI has seen over the past few years, there have been discussions about the possibility of completely automating specific industries, including data analysis.

However, as discussed throughout the article, it's difficult to imagine how a profession that depends on logical reasoning and creative solutions could be entirely automated, except for a few parts of data analysis involving repetitive tasks. Even with the enormous advancements AI is making these days, it is not yet prepared for it.

Data analysts won't have to fear automation taking over their jobs for quite some time. Even if it does, I doubt we'll be around to see it, but even if we were, it would be a fantastic development. That said, if you are a beginner wanting to upgrade your skills, Learnbay has the best data analytics course for you! Learn the skills and become certified by IBM.