Can Data Analysis Be Automated? (We Find Out)

Not so long ago, people were used to doing simple repetitive tasks such as performing a simple linear regression by hand, which is quite a tedious task even with the help of a calculator. While there’s nothing wrong with this, the amount of data that can be processed in this way is quite limited, and you end up exhausting a lot of human resources.

However, as technology advanced, more and more things got automated. And by the early 1980s, even a below-par programmer was skilled enough to carry out such mundane tasks by using a program.

So, you get the idea. Given the high processing capabilities of the current era, and AI making extraordinary strides; a lot of aspiring data scientists out there are worried if they’ll soon be replaced by computers, after a high ratio of data analysis is automated.

So, in today’s article, we’ll talk about this argument in detail, looking at things from different perspectives, and finally concluding if you’re right suspecting the inevitable role of AI in data analysis or not. So, let’s start!

What is Data Analysis?

Data analysis is amongst the most crucial parts of a data science project. It involves dealing with data in every possible way before feeding it to the model for training purposes. Data analysis makes sure you’re supplying your models with the correct information.

Data analysis, in essence, is the process of cleaning, analyzing, transforming, and modeling data to find relevant insight for better organizational decision-making.

Can Data Analysis Be Automated?

For most of the part, yes. Processes like data cleaning, processing, and ETL consist of many things that don’t require logic and are repetitive. Hence, it’s not impossible to automate them. However, automating tasks like noticing slight changes or observing trends manually might be a long shot.

Data analysis is a vast field, and for answering this question more specifically, you must realize what part you’re referring to. Further in the article, we will explore some areas that are already somewhat automated, and some not expected to be automated anytime soon.

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The 4 Types of Analytics Explained

Some companies are already using automated data analysis for quite some time.

Automated Analytics – What is it?

Automated analytics is the phenomenon of using software to analyze data that doesn’t contain a lot of human intervention. This could be as simple as running a pre-prepared script to modify some data before adding it to a given table.

Once these scripts are ready and put to use, they could prove to be a game-changer for efficiency, since a lot of human resources would be saved, and the process will also get a lot quicker and error-free.

Many enterprises nowadays are taking advantage of automated analytics and using it to speed up their processes. But again, this has only proven to work for a small subset of processes so far; these processes are not subject to a significant change, so there is no human thinking involved.

Some Concrete Examples of Data Analysis Automation

  1. Data Collection

You cannot start with the data analysis before you have collected enough data. Data collection is always the foremost step that needs to be taken care of before proceeding to data analysis. However, since data is spread across many places, it requires going through tons of excel files or writing time-consuming scripts to collect data from 3rd party apps.

By automating the process of data collection, you can save a lot of time; and directly jump to data processing or data cleaning instead. Moreover, in some cases, you can automate the data cleaning/processing part as well, but that’s a bit tricky. 

  1. ETL – Extract, Transform, Load

ETL stands for Extract, Transform, Load. It refers to the process that happens once you have the raw data in your database, but you have to modify it according to your needs or make it consistent with the rest of the data.

Sometimes, ETL involves the data collection part as well. However, mostly, it starts once you have the data collection and need to apply certain transformations. Automated ETL solutions save companies plenty of time for their resources and help them spend time where it matters.

  1. Dashboards

Dashboards include visualizations that help to calculate KPIs or things like that. Once you have made your dashboards and figure what kind of visualizations you need, the process is heavily repetitive. Companies can easily leverage automation in such scenarios by building end-to-end pipelines, which are connected to data sources and provide you with the relevant information without needing any human interference.

There are various tools such as Metabase that help with tasks such as data visualization in dashboards.

Hurdles in Automating Data Analysis

By looking at the example discussed above, you probably have got an idea of how far we have come in the way of achieving automated data analysis. However, there is still a long way to go to fully automate data analysis, and frankly, it doesn’t feel like we’re getting there anytime soon.

Let’s look at some of the biggest hurdles that stand in our way of achieving automated data analysis.

  • Human Abilities to Understand Problem Context 

The ongoing decade has been a rollercoaster ride for the ML world, and we have witnessed some groundbreaking innovations that have upped the bar of AI capabilities. Most of this has been possible due to the increase in the processing powers. But technologies such as GPT-3 are absolute wonders.

With that said, AI is still far cry from achieving a fraction when it comes to understanding problem contexts as humans do. Even the likes of GPT-3 struggle when it comes to scenarios that involve common sense. Even the 175 billion parameters aren’t enough to mimic humanly common sense.

So, you get the idea. Even though the machines are making progress at an unprecedented speed, they still don’t seem to approach the thinking capabilities of humans, which is a must-have ingredient to fully automate something such as data analysis.

  • Innovation

Lack of innovation is the second big reason why machines are not enough for data analysis. If you follow data analysis enough, you know there are a lot of different techniques employed for different scenarios, and sometimes, people come up with specific techniques to tackle a certain dataset.

Luckily, this is something humans are great at; Innovation. Techniques such as Wavelets and Fourier Transforms are being used in data analysis nowadays. Do you think AI could have thought to employ these techniques all by itself? Yeah, not really.

Hence, machines highly lack the ability to think outside the box like humans do when faced with a slightly different scenario. AI cannot effectively use all the knowledge it has to come up with an innovative solution for something; instead, it’ll only do what it’s trained to. And this is one of the largest gaps to fulfill if we want to see humans and machines crossing paths.

Key Takeaway – Should You Worry?

Data analytics is a booming field in today’s world, and as the volume of data rises, data analysis is only predicted to grow. With the immense growth that AI has experienced over the past few years, there have been talks that there are chances that fields such as data analysis might be fully automated.

However, as we have discussed throughout the article, except for some parts of data analysis that consist of repetitive tasks, it’s hard to think how such a field that requires logical thinking and innovative solutions could be fully automated. AI is not ready for it right now, even with the massive strides it’s taking nowadays.

So, it’s still a while before data analysts have to worry about their profession being overtaken by machines. Even if it happens, I reckon we won’t be here to witness that, but surely that’ll be a great evolution to witness!

Emidio Amadebai

As an IT Engineer, who is passionate about learning and sharing. I have worked and learned quite a bit from Data Engineers, Data Analysts, Business Analysts, and Key Decision Makers almost for the past 5 years. Interested in learning more about Data Science and How to leverage it for better decision-making in my business and hopefully help you do the same in yours.

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