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Will Data Analytics be Replaced With AI?

With the introduction of ChatGPT and other mass-market generative AI models in recent months, discussion about artificial intelligence (AI) has intensified. Many employment opportunities, including data analytics, might be revolutionized by these formidable technologies. But does this mean that data analysts are no more relevant? Let’s answer the question to one of the most sought-after topics today – Will Data Analytics Will be Replaced with AI?

Will Data Analytics be Replaced with AI

Short answer: absolutely not. Even before ChatGPT’s rise, AI and machine learning have long been instruments in the data analysis arsenal. The field of data analytics has grown steadily over time. Through 2031, it is expected that demand for data professionals will increase by an astonishing 36 percent, significantly above the norm.

Counterpoint? AI is important. Data analysts won’t become obsolete due to technology, but it will change (and is already changing) the type of analytical job and the value data experts contribute to your company. Let’s first understand what Data Analytics is. 

Here is a guide to Data Analytics and Data Science

What is Data Analytics?

The multidisciplinary subject of data analytics uses a variety of analysis methods, such as arithmetic, statistics, and computer science, to glean information from data sets. Data analytics is a vast concept that covers anything from straightforward data analysis to conceptualizing methods of data collection and developing the frameworks required to keep it.

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Data Analytics’ Uses

Whether we are aware of it or not, we utilize data every day and it is there everywhere.

Since many company executives utilize data to make educated decisions, data analytics is significant across a wide range of sectors. A shoemaker may use sales information to choose which styles to keep and which to retire, while a healthcare administrator may use inventory information to choose which medical supplies to purchase. 

Businesses that utilize data to inform their company strategy frequently discover that they are more self-assured, proactive, and financially astute.

You should know the most important Data Analyst Interview Questions

What is Artificial Intelligence?

Artificial intelligence is just that—artificial—and recently it appears to be the topic of Internet discussion. And with good reason: Artificial intelligence is changing how organizations operate and who works for them.

AI is defined as “the science and engineering of creating intelligent machines, especially intelligent computer programs” in this Stanford study. Although it is connected to the related job of utilizing computers to comprehend human intellect, AI should not be limited to techniques that can be seen by biological means.

Nearly 20 years later, the globe is still struggling with AI issues. Executives, researchers, and engineers working actively in the field of AI are among the business leaders from a variety of sectors who have signed open letters outlining the immediate risk to not just the future of work but also to the future of mankind.

In a declaration issued by the Center for AI Safety and signed, among others, by the founders of OpenAI, Google DeepMind, and Microsoft, it is said that “mitigating the risk of extinction from AI should be a global priority alongside other societal-scale risks, such as pandemics and nuclear war.”

Does this imply that AI will no longer exist? that the workplace will return to “normal”? that positions such as data analyst won’t be impacted? – No.

 

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Who is a Data Analyst and What is His Role?

A data analyst is someone who gathers, arranges, interprets, and resolves issues. Large data sets are searched for patterns and trends, and insights that might affect corporate plans and choices are found.

Data analysts start with data collecting from a number of sources to get there. After making sure the data is accurate, they analyze it using statistical techniques, data mining methodologies, and visualization tools to help make sense of the data.

What are the patterns, trends, and insights? is a crucial step in the data interpretation process. A data analyst then responds to the following query: What can we do with this?

A data analyst will collaborate with many departments, teams, and leaders to present solutions to this question and assist the decision-making process through the use of reports, charts, graphs, and other visual and summarizing tools. This necessitates effective communication, the capacity to comprehend the particular demands of both a firm and its customers, as well as the obvious likelihood that both may alter over time. 

Data Analysts may help with the outcomes monitoring to determine when and if to pivot, or how agile a strategy has to be. Setting up dashboards to monitor key performance indicators (KPIs) is the first stage in this process. The next step is to design any subsequent actions based on data in order to maintain the plan current and meet the demands of the business.


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When Two Roles Converge: The Integration of AI and Data Analytics

Reading the aforementioned can help one understand how AI can be included in a data analyst’s job description. But does that genuinely imply that AI is a danger to this expanding industry?

We must begin at the beginning and ask how we got here in order to respond to it.

Machine learning techniques started to progress in the early 2000s. These made it possible to evaluate and derive inferences from vast volumes of web data.

Ten years later, the era of deep learning began. We saw object identification capabilities in computer vision (consider self-driving vehicles and the systems that warn them of possible risks) as well as voice detection capabilities that would enable speech assistants like Siri and Alexa to reply in more believable ways.

 

In under 20 years, machine learning has given way to deep learning and then to generative AI.

With the formal introduction of ChatGPT, we now have access to a fresh perspective. Executives and employees alike began to ponder “What does this mean for me?” 

This question may cause anxiety for the data analyst, prompting existing employees and those considering the industry to wonder how the position will develop over time and whether they will still be required.

There is no simple solution, and that is a good thing.

You might begin to comprehend why this position is so complicated if you consider a data analyst as the engine of a corporation. As a driver, they direct teams ahead with information, their findings, and their suggestions on how to proceed.

AI doesn’t pose a threat in this circumstance; in fact it creates the opportunity.

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AI as a Productivity Tool  

AI need not be a negative development. You may utilize it as a tool to increase your productivity and your outcomes when you accept it in your capacity as a Data Analyst. 

One of AI’s greatest advantages, for instance, is its ability to analyze vast amounts of data. Finding patterns and trends, discovering critical insights, and then figuring out how to convey that knowledge to the people who require it in order to make the best decisions might all fall under this category.

It seems to make sense that the concern “What about me” is foremost in people’s minds given that this strength is also a key component of a data analyst’s work.

Despite recent substantial advances in AI, it is important to remember that these systems still have a long way to go before they can match human levels of general intelligence and comprehension.

Furthermore, it stated, “While ChatGPT can help with repetitive work, technology should be utilized as a tool in conjunction with human skill and judgment. To ensure the quality and dependability of the study, data analysts should verify and critically assess ChatGPT’s recommendations.

A Data Analyst can still be the driver—this time with a co-pilot—when this technology is utilized as a tool to optimize labor, not to replace it. In this situation, the Data Analyst is in charge of not only leading the way and laying out a clear course of action but also ensuring that any instructions and input provided by their co-pilot are accurate and appropriate for the road ahead—which, as we are discovering, won’t always be the case.

 

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Here’s How Using AI Can Benefit Data Analysts

It is quite improbable that AI will disappear. It will also continue to evolve throughout time, even if it is slowed down or stopped temporarily so that humans can keep up. And you can as well. You actually have to.

In order to deliver the value required in a generative AI economy, businesses and the people they employ must learn to reinvent themselves and their positions as technology evolves more quickly than ever before.

Knowing when and how to employ generative AI, like ChatGPT, helps us better appreciate both its strengths and its weaknesses. This becomes more crucial when technology advances without oversight or recognition of the potential legal and ethical issues that might arise from improper tool development.

It would be an understatement to suggest that AI is here to stay. AI will carry on developing, learning, and expanding, gaining more powers than before. Data analysts also need to adapt, learn, and take the lead in order to keep up. The moment has come to embrace technology rather than turn a blind eye to it. Who knows, after all? Future data analysts may employ AI as the next cutting-edge technique to help them make better business judgments.

Only time will tell. And as we all know, technology advances at an even quicker rate than time. 

Having someone or something to work with is advantageous for data analysts since they are critical thinkers. 

The following areas can be supported by various kinds of AI: 

Pattern Recognition

As we’ve already established, AI is essential for parsing through sets of data to find patterns, trends, and technologies. Some of these might be challenging to identify by hand, which is where AI comes in useful.

 

Support for Communication

AI can assist us in deciding how to most effectively explain our results and in creating the appropriate visuals for that purpose. Additionally, it may provide recommendations on the kind of data to include in reports depending on different target audiences, such as an executive vs a coordinator.

 

Finding Data Sources

AI can assist data analysts in their information search so they can obtain the insights they need, saving them hours of time at the beginning of the process.

Data Preparation

AI can help automate the chores of data cleansing and integration, giving data analysts support when identifying and resolving missing values or discrepancies in their datasets. 

Every Data Analyst can succeed with AI, but only if they have access to the greatest learning resources and opportunities

How Might AI Benefit Data Analysts?

AI gives analysts the tools they need to swiftly and correctly delve through enormous volumes of data. It excels in identifying trends and gleaning information from the data to give a more comprehensive picture. 

Data analysts may now concentrate on repetitive operations that don’t require human insight and discriminating judgment thanks to AI-driven automation. Not to mention, automation lowers the possibility of mistakes, resulting in more reliable and accurate output. Finally, AI provides professionals without data science background or experience with a simple path to managing and comprehending data.

There are several AI-powered solutions available for data analysis, and each one offers something different. Here are a few examples:

Tableau

This well-known tool for data visualization and corporate intelligence aids in the analysis, comprehension, and communication of data. It facilitates the sharing of insights by enabling connectivity to several data sources and the construction of interactive dashboards and visualizations.

The AI technology used by Polymer converts static spreadsheet data into searchable, interactive databases and visual apps.

 

Microsoft Power BI

By integrating cutting-edge AI capabilities, Microsoft Power BI makes interactive dashboards, predictive analytics, and automated data exploration possible.

Tools like these are changing how we understand, analyze, and use data, not simply by making data analysis more accessible. We can anticipate that as AI technology develops and matures, it will continue to improve our capacity to extract useful, practical insights from data.

Use of AI for Data Analytics and Decision-making: Challenges 

AI algorithms primarily rely on the reliability and accessibility of the data they use. Data that is inaccurate, lacking, or biased can provide incorrect analysis and deceptive findings.

Some of the challenges are:

Absence of Human Interpretation

While AI algorithms are excellent at processing and analyzing data, they frequently fall short in providing the context and nuanced interpretation that human analysts provide.

Ethics-Related Matters

AI-driven data analytics poses moral questions about biases in algorithms, data security, and privacy. To ensure ethical AI use, safeguard sensitive data, and minimize any biases that may affect decision-making processes, businesses must traverse the ethical environment.

AI systems are educated on historical data, which may have built-in biases or reflect society preconceptions. If these biases are not properly handled, AI systems may reinforce and magnify them, producing biased results in data analysis and decision-making procedures.

Collaboration between people and artificial intelligence systems is difficult to achieve. Organizational flexibility, skill development, and a clearly defined framework for human-AI cooperation are necessary for integrating AI into current processes and developing a symbiotic connection between human analysts and AI algorithms.

Can AI Replace Data Analysts?

To understand this, we must first classify data science occupations. In the vast field of data science, there are several professions, ranging from data analytics to creating ML solutions.

  • Data Analysts are responsible for collecting data, putting it through different processes, such as data cleaning, and storing it in a way that will make it useful for later analysis.
  • Data Analysts handle the most challenging part. On the data that data engineers have produced, data scientists create models and do exploratory analyses to uncover various solutions.

Some of the threats AI can pose in this field:

  • Putting together and combining data from several sources.
  • Transmission of cleaned data to the right places.
  • Automate the deployment of the models after they have been created.
  • Identifying certain trends in data.
  • Creating variations of certain models.

Despite the risks, Data Analysts still needed to include some of the most crucial tasks:

  • Supporting information while preserving its effectiveness.
  • Building pipelines that are not SQL.
  • Maintaining the fundamental framework for the data

 

Even the most challenging games, like Dota 2, which require quick reflexes and planning, are already under the control of some very advanced AIs. What specifically do they lack that prevents them from carrying out the duties that data scientists are capable of doing?

New challenges are always there for Data Analysts. A Data Analyst’s job is far more diversified than a data engineer’s, who usually does the same set of tasks like gathering data from a certain source or cleaning it. As they always deal with new work in different conditions, they may run into the same problem just a few times.

Sadly, AI struggles with this. Before being employed for a task, artificial intelligence must first be trained for it and only then after achieving a particular level of accuracy.

No matter how sophisticated an AI becomes, it cannot tackle new issues.

For instance, a data analyst could evaluate the data associated with a business challenge as part of their employment. Data analysts will develop a specific model that may be applied to a particular issue inside that problem domain. But the main problem isn’t this, it is that machines do not possess intelligence. 

The primary problem is that a data analyst is coming across this specific circumstance for the first time. Because of his or her understanding of machine learning models and how they are created, he or she will be able to manage the project.

Also, AI Is Lacking in Soft Skills. Let’s imagine for a moment that AI has advanced to the point where it has all the technical skills required to be a data scientist, making data science entirely automatable. Is having technical skills the only need for becoming a data analyst?

Answering it simply and practically is “No.” No matter how sophisticated AI gets, it will never be able to compete with a data analyst’s or even a regular CEO’s interpersonal skills.

Frequently Asked Questions (FAQs) on data Analytics and AI – Will Da be replaced by artificial intelligence? 

Q. Will AI eventually take the position of data analyst?

Even while AI has come a long way in automating some data analytics activities, it is doubtful that it will ever totally replace human data analysts in the near future.

Q. Which is easier to learn- Data analytics or AI?

To comprehend data, data analysts utilize machine learning and deep learning. In general, Data Analytics is easier to learn than AI since it focuses on utilizing math and statistics to analyze data, whereas AI aims to build robots that can mimic human behavior.

Q. Is a career in Data analytics promising?

Data analytics services are in high demand around the globe. A survey states that 250,000 new job vacancies in the field of data analytics are anticipated in 2023, which is over 60% more than the need in 2019–20.

Summing Up

A considerable paradigm change is occurring in traditional practices as a result of the rapid development of artificial intelligence. Numerous professions are threatened by its expansion, and as automation spreads, there are fewer people around.

People are concerned that the great pace that data science is presently experiencing might harm this profession. To find out whether data science can be automated, additional research is necessary, as this article has demonstrated. There are a lot of unmet demands for AI, and it doesn’t seem like that will change very soon. So, the answer is a big NO! 


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