In a nutshell:
- AI has become a hot topic in data analytics, sparking curiosity and questions among professionals.
- AI enables data analysts to automate tasks, analyze data, and make accurate predictions.
- Data analysts are best equipped to leverage AI tools, enhancing their capabilities and productivity.
- AI is not likely to replace data analysts but will enhance their work processes.
- Low-code AI tools are democratizing data analytics, making it more accessible and productive for professionals.
Once upon a time, we would chat with colleagues about the weather, our favorite music, or what we did over the weekend.
But since late 2022, we’ve mostly been talking about that email or trip itinerary we created using ChatGPT, or complaining about our LinkedIn feed being inundated by people looking to capitalize on the AI craze.
AI – and specifically generative AI – has emerged as an incredibly hot topic, sparking curiosity and questions among anyone who has seen it in action. Chances are, you’re already using it in some way. And like many other professionals, you may be wondering or even concerned about how it will affect your work and career.
In this guide, we’ll address some common questions about AI and the impact it’s likely to have on the world of data analytics and data science. We’ll then explore the concept of low-code software and how certain platforms leverage AI to help data analysts succeed.
AI is exciting, but there’s also a need for clarity and guidance—and we’re here to help. Keep reading to explore the many opportunities AI offers data analysts.
What is AI?
As a data analyst, you’re undoubtedly familiar with the term, but let’s dive deeper into what it means.
AI stands for artificial intelligence and refers to the development of computer systems capable of thinking, learning, and performing tasks like humans but in a more specialized and efficient way. Generally, AI systems are used to recognize complex patterns, classify data, make predictions, and even generate human-like responses.
There are countless real-world applications of AI, and you regularly encounter them in your day-to-day life, even if you’re not aware of it – from the facial recognition technology that’s used to unlock your phone, to the way content is prioritized in your social feed, to the way you search for images in Google Photos.
AI is commonly achieved through neural networks, which are algorithms designed to mimic how our neurons communicate and process information. Put simply, they receive input data, perform mathematical operations on that data, and produce outputs that are passed on to additional nodes for “training.” This way, neural networks can detect and learn complex patterns and relationships from data, and use them to make sophisticated predictions or decisions.
You may also be familiar with the term “machine learning.” This subset of AI enables systems to automatically learn and improve without being explicitly programmed. Machine learning algorithms use vast quantities of training data to learn from examples, improving their performance over time (e.g., by minimizing errors and optimizing the model’s results for a particular objective).
The Google Photos search function is a good illustration of this. The app uses machine learning algorithms to automatically understand and categorize the content of images. By extracting patterns, features, and visual cues, the system has learned to recognize objects, people, and scenes within photos with a high degree of accuracy. This is why you can now search for specific objects (a.k.a. cats!) or specific people’s faces within your photos. And the more the system is used (the more data it processes), the better it becomes at identifying photos based on their content.
Why would data analysts want to use AI?
The field of AI has been rapidly advancing and is already revolutionizing the field of data analytics. How will these changes affect your role as a data analyst?
For starters, AI tools can be used to automate many parts of the data analytics process, from data preprocessing to exploratory data analysis to data visualization. But one of the most important benefits is that AI enables data analysts and other business professionals to analyze data, extract powerful insights, and make accurate predictions about customer behavior based on vast amounts of data. In other words, you can use it to quickly train and deploy highly accurate predictive models.
Wondering how this differs from current approaches to data analytics? Well, traditional business intelligence (BI) and rule-based models are limited in a number of ways:
- They’re only able to analyze what happened in the past and make educated guesses about what might happen in the future, assuming that specific trends will continue.
- They can only detect patterns you choose to look for (or that are shown to you), which creates bias in your analysis.
- They can only detect correlations between two or three variables at a time.
- They require a great deal of manual, repetitive, and time-consuming work on the part of the data analyst.
In contrast, by using AI-powered software, analysts can leverage machine learning models that can analyze massive datasets, detect complex patterns and correlations, and uncover relationships within data that would not be detectable through human efforts.
And here’s where AI truly shines: predictive analytics. Predictive analytics platforms use machine learning algorithms to harness the power of historical data and predict future business outcomes.
Predictive analytics is a game-changer for analysts and the companies they work for, as it allows them to anticipate trends, forecast demand, and make data-driven decisions that significantly impact business strategy.
How do BI and predictive analytics compare?
There are many intriguing business use cases for AI. By understanding them, applying them to your organization, and integrating predictions into directly existing business workflows, you can unlock your value as a data analyst and take your career to new heights.
Who should use AI for data analytics?
Until recently, using AI tools to create predictive models required a combination of coding skills, advanced analytics skills, and a deep understanding of data science. As such, it demanded the specialized skills of data scientists and machine learning engineers, who would cooperate over many months.
But with the emergence of newer, smarter, and easier-to-use AI tools, the opportunity has widened to data analysts, who can now use their existing skills to create predictive models without an extensive background in data science.
Which skills do you need to do predictive analytics?
There are several reasons why data analysts are the best-equipped people to leverage AI tools, especially when they are integrated into business teams.
By working closely with stakeholders and understanding specific business goals and KPIs, they can apply AI techniques to solve real-world business problems. Data analysts can deploy and manage predictive models aligned with the business context, interpret the results, and provide actionable insights that drive strategic decision-making.
Thanks to analysts’ existing domain knowledge and capabilities, together with AI tools that leverage automation to make predictive modeling more accessible, data science skills are no longer a prerequisite. Data analysts are perfectly capable of using AI tools to build accurate and reliable predictive models.
In fact, despite their specialized skills and knowledge, data scientists are often unable to deliver the same type of business impact. This is typically a result of the “data science disconnect,” where there is an insurmountable distance between them and the relevant business stakeholders or decision-makers within an organization.
Will AI replace data analysts?
As soon as millions of knowledge workers witnessed the ability of ChatGPT to churn out intelligent, human-like responses, they began to wonder how soon machines would replace their jobs. As such, no discussion about this powerful technology would be complete without considering the long-term impact of AI on the field of data analytics.
Historically, technological advancements have led to a gradual shift in the job market rather than complete job displacement. And with the emergence of AI, we expect this trend to continue.
Although AI can automate routine tasks like data cleaning and preprocessing, and even creative tasks like statistical analysis and visualization, it can’t replicate human creativity, strategic thinking, and the need for a human touch.
Therefore, instead of replacing data analysts, AI is much more likely to enhance their capabilities and productivity. By leveraging AI to complete laborious and time-consuming tasks, as well as highly complex data processing, analysts will have more time to focus on higher-value initiatives. This refers to tasks that require critical thinking and domain expertise, such as developing business use cases, interpreting results, communicating insights to stakeholders, and providing strategic recommendations.
In addition, just as new technologies have consistently spawned new jobs and even whole industries, we’re beginning to see new roles specifically designed to harness the power of AI. You’ll soon see job titles like “AI Data Analyst,” “AI Model Validator,” and “AI Strategy Consultant” in your LinkedIn feed. Indeed, a whole new array of opportunities, startups, and software have already begun to emerge in the field of AI for data analytics.
The future is bright, and the impact of AI on your career will depend on how you adapt and leverage its potential. By embracing and using it as a tool for innovation, you can navigate the shifting landscape and ensure a long, meaningful, and rewarding career. The next question is…
How can data analysts get started with AI?
As AI tools gain widespread adoption across industries, they are becoming increasingly proficient at automating many data-related tasks. However, analysts will play a crucial role in leveraging these tools effectively and in strategic new ways.
If you’re a data analyst who wants to stay relevant and competitive in your field, you will need to stay up to date on AI technologies, upskill in certain areas, learn how to use existing AI tools, and identify and experiment with AI use cases so you can add new value to your organization.
Leveraging AI within your role is how you’ll be able to stay ahead of the curve, increase your organizational impact, and foster a long and successful career as a data analyst. There are many ways to get started, from joining online communities to enrolling in online courses.
But if you’re looking for an immediate way to make a more significant impact in your role, we recommend adopting a predictive analytics platform. Within a matter of days, you’ll be up and running with AI models that detect complex patterns and correlations in your historical data, and make accurate predictions about future business outcomes.
These predictions can then be integrated directly into your relevant business system (like a CRM, ERP, or BI tool), so they can be leveraged in your business colleagues’ day-to-day decisions and activities. And as we mentioned earlier, all of this can be done without any data science expertise. If you’d like to see it for yourself, why not give it a try?
The value of low-code software
In today’s fast-paced digital world, organizations must be agile to remain competitive. Low-code development is one of the critical factors that allows businesses to keep up with rapidly evolving trends and customer demands, streamline operations, and maximize productivity.
This is because low-code software makes it possible to build applications and solve business challenges without extensive coding or software engineering skills. And this is why it has conquered many corners of the business world.
From data analysts building their advanced analytics models and visualizations, marketing teams building their own websites, and solopreneurs creating online courses and webinars, virtually everyone is using such software regularly.
And when it comes to data analytics, the primary advantage of low-code software is that it allows you to focus on extracting meaningful insights from data – instead of spending most of your time learning complex data science concepts and programming languages.
Since you’re not required to possess specific specialized skills, you can quickly generate and test data-driven insights, leading to faster, more efficient decision-making and quicker time to value.
What are the benefits of low-code software?
In addition to the well-known benefits listed above, low-code platforms typically provide an intuitive visual interface that allow for easy customization and modification of use cases. This contrasts with traditional approaches to development, which can be highly time-consuming and rigid.
This kind of agility enables you to adapt to changing business requirements swiftly and efficiently – such as by minimizing the time and effort required to test new ideas, make updates, and add new functionalities.
In addition, a secure low-code platform will reduce the likelihood of human error, bugs, and security vulnerabilities. For example, robust security measures protect against unauthorized access and data breaches. Not only does this ensure the confidentiality, integrity, and availability of your data, but it also helps you comply with data privacy and regulatory requirements, and fosters trust and credibility among your customers.
By bridging the gap between technical requirements and business goals, low-code platforms provide a powerful solution for organizations that want to unlock the potential of their workforce.
Low-code AI tools for data science
The prevalence of low-code AI software is steadily increasing, reshaping the landscape of many domains, and transforming how countless professionals approach their work. Thanks to these platforms and their user-friendly interfaces, data analysts and data scientists can harness the power of data without the need for extensive coding expertise.
This has democratized the data analytics process: lowering the barrier to entry for aspiring data professionals, promoting collaboration between technical and non-technical stakeholders, and allowing data analysts to contribute to data-driven decision-making.
Here are some key ways the “low-code movement” in AI has impacted the productivity and work processes of data analysts:
- Data science and machine learning are much more accessible to individuals and organizations. Businesses that would never have been able to pursue such initiatives, whether due to a lack of resources or talent, can now pursue predictive analytics and implement data-driven decision-making.
- Low-code software can automate some of the most tedious, complex, and labor-intensive tasks – such as data prep, feature engineering, and the machine learning process itself.
- By being “freed up” from the above tasks, analysts can focus on more strategic and complex matters, such as choosing the right business questions and designing the right solutions to deliver real business impact.
- Low-code software enables speed in experimentation rather than waiting days, weeks, or months to see the results of manual adjustments to data or models. This enables rapid scaling to solve business problems and makes it possible to keep up with changes in the company, industry, or customer base.
- Statistical models are much more accurate and free from human bias. This is because machine learning algorithms can process infinitely more data points, features, and potential correlations than possible through traditional statistical methods.
All that being said, there are some shortcomings regarding low-code AI tools.
For example, these solutions may sometimes be less flexible or customizable than desired, forcing users to operate within the confines of the available features. And if the vendor decides to make certain changes to their product, it may call for corresponding changes to your data infrastructure and workflows.
Low-code solutions aren’t available for every use case. For instance, you may have difficulty finding a platform to deal with complex matters like computer vision or natural language processing. Or maybe you find a relevant solution, but it doesn’t support real-time data, which is essential for your needs. Or perhaps, for particular initiatives, hand-coded models may be the only feasible solution.
Using AI to elevate your impact as a data analyst
As tools and technologies in AI continue to evolve, artificial intelligence is increasingly the key to unlocking your full potential as a data analyst. By leveraging it to detect hidden patterns in your data and make accurate predictions that drive business success, you can contribute to strategic business decisions and your company’s bottom line.
This will not only make you more valuable to your organization but also help you stay one step ahead in a dynamic and competitive business environment. Despite concerns about the future of data professionals, artificial intelligence will not replace data analysts.
Instead, it will augment their work processes and provide them with greater opportunities to focus on strategic and impactful high-level initiatives.
To wrap things up, here’s a summary of steps you can take to begin leveraging AI in your profession:
- Stay current on the latest AI technologies and trends relevant to data analytics, such as automated machine learning (AutoML).
- Upskill or re-skill in areas like data mining, machine learning, and programming languages commonly used in AI development.
- Look into online courses, webinars, and other educational resources relevant to AI, data analytics, and data science. We’ve linked a few resources here, and here’s a comprehensive list of the best artificial intelligence courses and certifications on Coursera.
- Enhance your proficiency, productivity, and creativity as a data analyst by learning to use existing AI tools like ChatGPT.
- Encourage a culture of experimentation on your team, with the shared goal of informed data-driven decision-making.
- Identify use cases where AI can be used to improve efficiency and add value to your organization (such as by reducing churn, increasing upsells, or optimizing inventory management).
Want to start increasing your impact as a data analyst today? If so, you’ll want to try out Pecan’s predictive analytics platform – designed specifically for analysts and other data professionals who may not possess specialized data science skills.
With native connectors to a wide range of data sources, Predictive GenAI to tailor your custom model, and automated machine-learning processes that do all the heavy lifting, you can have high-performing predictive models up and running within days.
Ready to start your free trial?
