Incessant development is happening in the field of artificial intelligence and machine learning right now. And so, whether automating various data science processes will be able to take over the role of data scientists completely or partially in the near future is now a hotly debated topic.
Data Engineers and Data Scientists
Before delving into the data science automation debate, let’s first understand the difference between a data engineer and a data scientist, as well as the various data science tasks that are getting automated right now.
Data Engineers – Responsible for extracting and assembling data from numerous sources as well as cleaning and transforming. Thereafter, loading the data in a repository in some standardized format. They prepare the data to be further processed by data scientists.
Data Scientists – Takes out data from the data repository in the aim of building, designing, and testing advanced machine learning models, based on complex algorithms.
Artificial Intelligence and machine learning are helping in automating various tasks, traditionally performed by data engineers and data scientists, such as:
- Cleansing data and identifying empty records and outliers
- Detecting significant prediction features
- Developing basic data models
- Preparing data and checking it for correctness
- Producing hundreds to thousands of model variations intended for different markets and segments
- Pushing data models into production
Artificial intelligence, machine learning, and data science automation
Contrary to popular belief, AI combined with the human problem-solving, critical thinking, and decision-making skills empowers data scientists. It enhances the way several data science processes are accomplished.
Lower-level tasks are the first in line to be taken over by AI. Consider this example: during the 1980s, when programming languages started becoming more complex and advanced, the demand for low-level programmers declined.
Nonetheless, a major repercussion of this was an increase in the demand for developers, programmers adept in several programming languages and associated technologies.
The same is happening with data analytics right now. AI and ML are automating lower-level tasks leading to the creation of more complex tasks that can only be handled by humans. It reassures the notion that AI and innate human skills have empowered data scientists.
Artificial Intelligence is helping data scientists to quickly reproduce hundreds to thousands of variations of a machine learning model with a distinct set of prediction features. Thereafter, iterative simulations are created to find the best model variation to opt for.
Any dynamic decision process gained via automation outperforms any single algorithm by automatically testing, iterating, and monitoring data quality. Moreover, it incorporates new data points, allowing for a quick and wise response to events in real time.
Although it has now become possible to use AI for preparing raw data and then cleaning it, not everything can be taken care of by AI single-handedly, not anytime soon. Drawing out important insights from collected data still requires human intervention.
Yes, AI can make it easier to do data science but not solely take charge of it. Robots and machines able to comprehend the specific needs of their employers or organizations in different industrial contexts still have a very long way to go.
AI allows for automating lower-level steps in data preparation and visualization so that data scientists can help decision-makers understand the true meanings of gained insights.
The future of data science automation
Data science is already huge and will only get bigger and better with each passing day. And data science automation is already revolutionizing the way data preparation is done.
So, the relevant questions being asked right now are:
- Will data science get completely automated?
- Will data science automation be able to completely replace data scientists?
The time when artificially intelligent machines will pass the typical level of human intelligence, technically known as the technological singularity, is still predicted, hypothetically, to be around sometime during the 2040s.
However, being a hypothetical concept, we aren’t sure whether technological singularity is a thing of certainty or not. In short, we just don’t know whether there will be a time or not when data science jobs will be completely taken over by AI-powered bots.
Nonetheless, the surge in AI and ML is imminent and can’t be ignored. So, data scientists need to keep stepping up their game to stay relevant and minimize their chances of being replaced by AI-powered bots or tools.
On the brighter side of the things, AI can prove to be an incomparable aid to data scientists. AI-powered bots and tools can serve as smart assistants allowing data scientists to run even more complex data simulations and achieve what seems to be unachievable right now.
Data science tasks that are most likely to be automated better in the near future
Thankfully, the job of a data scientist is not on the list of jobs to be soon consumed by automation. The field of data science has a very diverse range of tasks. Not all of them are going to be replaced by automation anytime soon. However, here are some of the most important ones that are likely to lead the change:
- Data cleansing
- Data delivery
- Data ingesting
- Data integration
- Data visualization
- Model building
- Model fitting
But this is just a projection. Moreover, if these tasks involve creativity, critical thinking, and curiosity then the chances of them being replaced by automation are significantly reduced due to a bot’s inability to (critically) think and work like a human.
Still, lower-level data science tasks are beginning to get automated and AI will become capable of carrying out basic data interpretation and visualization in the years to come.
A future data scientist’s role will likely be to add meaning to the data that is to be used and develop scripts that will automate these tasks in an effective way.
4 reasons why AI automation isn’t there yet
So now we can say that data science automation is nowhere near consuming data scientist jobs, and here are 4 good reasons that bolster this conclusion:
1. Data preparation is a context-sensitive task
Most data science starts with data collection followed by data preparation. Although data preparation might seem like a simple process, it certainly isn’t. On the contrary, it involves several complexities.
Any automated process starts with feeding smart data to the machine. This simply means that data needs to be structured in some sense and collected with a preplanned intent.
Before dealing with data preparation, a smart AI must need to be aware of the context the data is collected for. Moreover, the industry belonging to the nature of the task for which the data is to be prepared must also be studied inside out.
Unfortunately, such a smart AI doesn’t exist. As such, human intervention is still required during the data preparation step and will likely remain that way for several years to come.
Even the smartest machine learning systems of today can only work after they’re told what and how they need to work. Such systems will only optimize data using representative training dataset already prepared.
2. AI is incapable of gaining crucial business insights from raw data
Data collection has become very common. Every organization, may it be a soap manufacturer or a robotics firm, is keen on collecting more data to make the business more profitable, cut-down operating costs, know their customers, market better, etc.
Collecting data is simple unless it involves only collecting data from specific sources, which can be of digital or non-digital nature. Even though companies have succeeded in automating big data collection, cleansing, structuring, and even analyzing to some extent, still require human supervision.
Even automated machine learning systems need a human supervisor to draw out key business insights from raw data. Machines are yet not able to judge what organizations need and what they don’t.
Although AI might be becoming capable of picking up trends and patterns from raw data, it doesn’t know how to use it in the context of an organization. How can these trends and patterns impact business performance in the long run? They just don’t know!
AI-powered machines can detect the dependencies amongst different data science operations, but falls short when comprehending how they’re beneficial to an organization. Machines simply can’t interpret data in a meaningful way. For that, we need data scientists.
3. Only lower-level tasks are automatable
According to Gartner, over 40% of data science tasks will get automated by 2020. While this percentage might seem like a premonition of the looming risk to data scientists, the reality is quite different.
AI can only replace data scientists in terms of operating lower-level tasks, including data cleansing, delivery, ingesting, model fitting, and visualization. Many tasks that are today carried out by newbie data scientists will soon be handled by AI-powered machines and tools.
Nonetheless, automated machine learning systems cable of handling complex, problem-solving, and theoretical tasks involving critical thinking and interpretation of results are still a concept of fiction and will likely stay that way for several years to come.
4. New high-level, data scientist jobs are being created
The advancement in data science automation has only resulted in an increase in the demand for skilled data scientists.
More organizations are becoming data-driven and tech-oriented. As a result, the need for professionals who are able to understand and work with AI, big data, and machine learning is also on the rise.
Simple activities like data ingesting and visualization are either being completely automated or carried out with the aid of smart bots. Data scientists are required to oversee this as well as bringing the creativity and innovation to the table that machines simply can’t deliver.
Like programming, the more complex the data science process becomes, the more high-level jobs are created. In the upcoming years, data science will no longer be the next big thing it is today. Instead, it will be an essential commodity for any organization.
Although automation will deplete several jobs around the world, it will also pave the way for creating more, specialized jobs, free from redundant tasks. This applies to data science, too.
Why data science automation will never be able to completely takeover data scientists
Many experts are skeptical about data science automation growing to such a level that it’s able to completely wipe out data scientist jobs. The belief is backed by the following assertions:
- Any AI-powered tool requires human guidance for concluding meaningful insights from raw data
- Further innovation in data science automation requires data scientists capable of handling advanced tasks
- Higher-level data science jobs are created faster than the automated workforce is trained
- Machines aren’t capable enough to handle data preparation processes on their own, human monitoring is required
- Robots can only handle simple, reiterating tasks
So, what should the data scientists of today do?
Being a data scientist is one of the hottest career paths available as of today. Yes, data science automation will keep getting better, even if it is only for mundane, low-level and repetitive tasks. Intricate tasks requiring problem-solving and critical thinking will still be carried out by humans.
Data scientists today need to evolve along with the progress in automation and prioritize higher value tasks. No matter how ‘smart’ or ‘intelligent’ AI-powered machines and tools will become, they’ll only be able to complement highly-skilled, human data scientists.
In the future, data scientists will have more time to invest in advancing their skills and making various processes efficient rather than mulling over low-level tasks.
To cut to the chase, data science automation is real and getting bigger and better. However, the future of data scientists also seems brighter. Data scientists will surely survive and thrive in the upcoming future.