How Machine Learning Automation And Data Storytelling Can Overcome The Shortage Of Data Scientists?

Hrvoje Smolic
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22/02/2022

Let’s take a closer look at why it's challenging to find data scientists in a world that’s increasingly dependent on data and how machine learning automation can help.

The world is facing an acute shortage of data scientists. Statistics show that in 2016, there were over 1700 data scientist job vacancies on Glassdoor, and in 2020, there were over 6500. This glaring gap is only set to grow further. IBM analysts and the US Bureau of Labor Statistics predict a 28% increase over the next five years.

If you’re wondering why there’s such a huge unmet demand, the answer is anything but simple.

The Shortage of Data Scientists in 2022

Research from Quanthub summarizes that there are not enough data scientists in the market—the job postings outnumber the job searches threefold.

shortage of data scientists
shortage of data scientists

According to The Quant Crunch, job openings for data scientists were 100k+ in 2021. Companies offered incentives such as a 14% salary hike to applying data scientists, and yet the quantum of big data projects far outweighed the availability of individuals with the required skill set to handle the data.

There are similar trends and findings for 2022 as well. A large number of data science jobs point to the increasing demand for big data and analytics professionals, further driving the need for distinctive skill sets. Data is being used to create massive change in many industries — healthcare, finance, marketing, business, retail, eCommerce, SaaS.

While this does not mean that there won’t be some improvement in the availability of data scientists, the ratio between the number of data scientists and the big data structure shall always be unfairly distributed.

Why? For starters, the need for skilled data professionals continues to grow as companies work towards improving their efforts concerning big data and attempt to simplify their business models and revenue opportunities in the digital sphere.

Smaller businesses are beginning to employ specialists as they too have come to understand how vital data analysis is in helping their brand grow. There are other factors such as lack of appropriate education and guidance. The situation compounds further as there is rapid technological advancement— there is a demand for new skill sets that include understanding ML, data visualization, and knowledge of advanced statistical tools.

The problem is that, while data analysts are found relatively easily, they lack skills comparable to data scientists.

Data Scientist vs. Data Analyst

The biggest difference between a Data Analyst and a Data Scientist is that the former translates available data into common language and provides clear and concise insights.

On the other hand, a data scientist might be required to analyze actionable insights and create models using machine learning and advanced SQL.

Data Scientists are often required to employ the following technical skills:

  • Python programming / R programming
  • Advanced SQL
  • Machine Learning and AI

These skills are fairly uncommon amongst data analysts. They are already required to do a lot of tedious work which involves:

  • Inspecting and cleaning data to reveal crucial insights
  • Identifying correlations or patterns and statistically analyzing or mining data
  • Visualizing data to interpret and present findings in a comprehensive manner

Data analysts are thus more focused on studying large amounts of data, interpreting it, and answering questions or solving problems.

graphite data analysts vs data scientists
data analysts vs data scientists

While having skills related to coding and programming might make their jobs easier, they are not required and, therefore, are often overlooked.

Though often used interchangeably, both jobs have extremely different roles. The scarcity of data scientists might impact the work of data analysts.

The way to circumvent this is to automate a data scientist’s contribution to handling data for specific jobs.

Let’s look at how automation and no-code ML can tackle the scarcity of Data Scientists.

Data Analysts Turned Data Scientists

As companies begin to pivot towards collecting and mining data, machine learning and artificial intelligence have become basic expectations for those in the data analysis field. However, most data analysts lack the technical skills that data scientists possess. This means that while BI is quickly picking up, data analysts might struggle to keep up, and businesses shall continue to hunt desperately for data scientists.

While this might seem discouraging, it’s an incredible opportunity for data analysts to recover their position without the formal training of a data scientist.

It is a unique situation to be in, as the experience as a data analyst puts you in a novel position to blend BI with ML effectively. You can use your existing knowledge of extracting information from any given data by using the right software. This doesn’t have to be complicated, as automated machine learning will provide you with the tools to compensate for your lack of technical knowledge.

Essentially, you can use ready tools for advanced diagnostic analytics and other predictive and prescriptive calculations without taking on all the responsibilities of a data scientist. This could be a successful attempt at bridging the gap between the market’s need for data scientists and the skills you can offer.

Tackling The Lack of Data Scientists

When discussing how the lack of data scientists can be overcome, automation and no-code Machine Learning (ML) are the most obvious answers.

Here’s a closer look at why.

An article studying how automation might take over jobs that require repetitive actions, lack creativity or a high social IQ, and do not require much training, shows interesting statistics for automated jobs.

Further, The Fourth Age: Smart Robots, Conscious Computers, and the Future of HumanityByron Reese lays out the characteristics of jobs at high risk for automation.

These are:

  • Repetitive: either physically or mentally
  • Low creativity: no need for improvisation and novel ways of thinking
  • Low social IQ: no communication, persuasion, or charisma required
  • Limited training: the ease of automating a job is inversely proportional to the length of a “manual” it would take to fully describe that job

Why Machine Learning Automation?

Mastering how to code well enough to be proficient in the technical aspects required for machine learning and artificial intelligence can take years. Even the most seasoned programmers struggle with the process, so it’s not surprising that data analysts lack the skills to do so.

After all, the time and effort to learn how to program and code effectively is extremely high. Even then, it can take months to make an automation model that is effective and functional.

Therefore, no-code machine learning is the answer to solving all kinds of automation issues. Wondering why? Simple. They allow analysts to make predictions and work with available data at a much quicker pace without requiring a higher budget for resources involved in employing data scientists.

Therefore, data analysts can be creative and proactive and utilize their skills better with no-code machine learning at hand.

To understand this better, we can look at traditional machine learning and how it differs from no-code machine learning.

What is Traditional Machine Learning

This image gives a basic idea of what goes into traditional machine learning.

graphite traditional machine learning
the traditional machine learning process

Essentially, this type of Artificial Intelligence (AI) enables applications to accurately predict outcomes even though they may not be explicitly programmed to do so.

The algorithms employed in machine learning use historical data input to produce the output values.

The four basic approaches to machine learning are:

  • Supervised learning - In which both the input and output variables of the algorithm are specified.
  • Unsupervised Learning - The algorithm in this type of machine is trained on unlabeled data, and the data input and predicted outputs are predetermined.
  • Semi-supervised Learning - This is a mix of supervised and unsupervised models, and the training data may be labeled, but the model can freely explore the data on its own.
  • Reinforcement Learning - This model teaches a machine to complete multi-step processes with clearly defined rules.

Traditional Machine Learning is often used in:

  • Customer relationship management (CRM) software
  • Business intelligence and analytics
  • Human Resource Information Systems
  • Self-driving cars
  • Virtual assistants

What is Machine Learning Automation

machine learning automation
the process of machine learning automation

As seen in the image above, no-code machine learning is evidently a much simpler process.

In automated ML, all repetitive processes are automated:

  1. Import data
  2. Preprocessing
    • Feature engineering
    • Feature selection
    • Feature transformation
    • Missing values
    • Outlier handling
    • Numerous checks
  3. Model selection
    • Choosing the best model based on model scoring for the dataset
    • Model assessment
  4. Tune model hyper-parameters
  5. Model deployment, so it can be used by team members
  6. Model maintenance

No-code machine learning is thus a drastically simpler process for making data predictions and does not require too many technical skills.

No-code machine learning enables data analysts to build applications and systems without requiring any conventional programming knowledge or software. It is accessible through visual interference and guided actions, along with pre-built integrations that allow the user to exchange information however required.

When speaking about the no-code machine learning, Alphabet’s growth fund CapitalG, Alex Nicholas, said, “No code is empowering business users to take over functionality previously owned by technical users by abstracting complexity and centering around a visual workflow. This profound generational shift has the power to touch every software market and every user across the enterprise."

Some of the best no-code machine learning platforms allow easy drag-and-drop predictions that make editing queries extremely simple. Users must thus only replace identifiers and prediction columns and ignore those they do not wish to use.

This also reduces the time taken to create predictions to a few minutes!

Evidently, the doing-away of technical computing programs has certain undeniable benefits:

●           Data-driven ethos without allocating resources for a data science team

●           More feasible to create and scale machine learning-driven products

●           Improve decision-making based on facts and projections

●           Reduction in cost and improving profit margins

Data analysts can receive deeper insights into key business drivers through Graphite’s no-code machine learning-generated insights, which use proven model templates.

The software can also help expand the data analytics teams’ augmented and predictive analytic skills and tap into their full potential to make predictions based on available data.

Moreover, Graphite makes it extremely simple for analysts to use AI and ML tools for business analytics. It also allows users to generate machine learning models without coding or programming knowledge.

Graphite is automated to build models by selecting an ML algorithm that produces and explains results as a data scientist would.

That’s not all. It also has certain crucial data storytelling features. Why is this such an essential offering?

Let’s discuss!

The Importance Of Data Storytelling

Data storytelling converts complex data into easily comprehensible stories that can be read and shared. It essentially presents information in a clear, concise, and readable manner, allowing you to understand crucial details and insights regarding your business.

Notebook 2
Graphite Note data story

A data storytelling program is extremely beneficial to make the jobs of data analysts simpler and more effective.

Here are some of the key aspects in which data storytelling acts as a solution to the complexities caused by big datasets:

●           Converts metrics into insights

●           Assists with enhancing engagement and communication

●           Helps visualize narratives

●           Provides a simple basis for complex explanations

Roger C Schank, a cognitive scientist, states that “Humans are not ideally set up to understand logic; they are set up to understand stories.”

This is essentially what data storytelling is meant for - to make data easier to understand.

Here are some essential features for an effective data storytelling solution that Graphite employs:

●           Human-friendly visuals that are easy to understand and comprehend at a glance

●           Easy integration of text and visuals

●           A narrative flow of information and data

●           Ability to connect and save essential bits of data

●           Tools to share and collaborate on datasets

Data storytelling, thus, transforms data from a database's impartial fields into opinions, arguments, and insights. With the required tools, data analysts can perform more engaging and meaningful data storytelling and, as a result, get a higher return on their investment if data culture is elevated and the education on digital literacy across the board is improved.

This is exactly what Graphite Note aims to do!

Conclusion

Data scientists play an important role in data analytics. However, with the shortage of skilled data scientists, it is only a matter of time before automation technology advances enough to bridge this gap.

Software platform such as Graphite Note is ideal for instances where no-code machine learning can automate data scientists’ jobs.

Why? Because no-code machine learning is the simplest way to empower data analysts in all spheres of business and with all kinds of backgrounds to work with machine learning.

While BI and analytics platforms exist to help organizations understand the reason behind certain things happening, Graphite helps users understand what is expected to happen and how they can prepare and deal with it.

Graphite also helps simplify the use of AI and machine learning in business analytics, thus assisting users in generating machine learning models without coding or programming-related skills. Its explainable AI enables everyday business users to work with their data efficiently.

In addition to its optimized ML algorithm, its data storytelling features are also changing the game. In short – “how to spend less time explaining to your colleagues what's happening, over and over again.”

Now that you are here...

Graphite Note simplifies the use of Machine Learning in analytics by helping business users to generate no-code machine learning models - without writing a single line of code.

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