The Impact of AI on Data Analytics

In a world of rapid technological advances, organizations now have the power to collect massive amounts of information from countless data sources. But the age of big data has also ushered in key challenges, namely how to make sense of more information than traditional data analysts could sort in a lifetime.

Artificial intelligence (AI) has proven to be at the heart of the answer for many data scientists. Equipped with powerful machine learning algorithms, AI analytics tools now offer solutions ranging from enhanced security to the ability to make data-driven decisions.

Throughout this guide, we’ll explain how data professionals are utilizing the power of AI-driven data analytics and analysis. We’ll explore multiple use cases that examine why AI algorithms are proving such valuable tools for organizations in various industries.

Artificial intelligence and data analytics: interconnected innovation

Before we delve into the many ways that data professionals are using AI, let’s take a moment to get familiar with several key terms you’ll want to understand.

• Artificial intelligence. Refers to a machine or software that has been developed to model data analysis similar to that from a human brain.
• Machine learning. A branch of science that uses algorithms and data to enable AI to improve its own accuracy in a way that’s similar to the human learning process.
• Deep learning. A subset of machine learning that uses more than three layers of neural networks to enable AI to extract increasingly complex concepts from data.
• Data analytics. A broad term that describes the overall process of collecting and interpreting data.
• Data analysis. One of the many steps that data scientists go through to turn unstructured data into valuable insights.

The combination of AI and data analytics allows data professionals to collect and interpret increasingly larger amounts of data faster and more efficiently. In the following sections, we’ll take a closer look at how AI-driven data analytics tools allow organizations to do the following.

1. Improves security

From customer contacts to financial information, businesses now store so much sensitive information online that a single security breach can have devastating consequences. In fact, a 2023 report by IBM revealed that the average data breach can cost a company $4.45 million. In some instances, however, the damage can be even more severe.

Between 2013 and 2016, Yahoo! suffered some of the most infamous data breaches in history. Not only did the incidents compromise the personal information of billions of Yahoo! users, but they also resulted in the company getting hit with a $35 million fine and over 40 class-action lawsuits.

Fortunately, AI has since become an invaluable tool for detecting and preventing real-time security threats. AI models can now use machine learning algorithms that act as virtual security guards by scanning large datasets for abnormalities. These AI solutions can mitigate threats such as malware and shadow data or even assess the threat of login attempts that deviate from standard user behavior.

As AI grows more widespread, so do concerns that tools like ChatGPT may be used for malicious purposes by bad actors. Recent research by Blackberry revealed that many IT decision-makers are turning to AI-driven security solutions to counter the enhanced risk of AI threats. Over 80% of those surveyed said they intend to invest in AI cybersecurity over the next two years.

2. Anticipates outcomes

Predictive analytics uses a blend of historical data, machine learning, and data analytics to predict future outcomes. Predictive AI algorithms have transformed the world of business analytics by making it far easier and less time-consuming for researchers to spot historical trends.

As you can imagine, this AI-driven technology offers a countless range of business use cases. Here are just a few examples of predictive analytics in action.

• Finance. Banks and other financial services have long been using AI to predict fraud and credit risk. Commonwealth Bank, for instance, uses AI analytics that can calculate the fraud risk of a transaction within 40 milliseconds, enabling fraud prevention.

• Retail. Retail companies can use AI analytics to assist with pricing and inventory planning or even to predict whether a promotion will be effective. Walmart uses inventory-scanning floor scrubbers that use machine learning to alert employees when popular items need to be restocked.
• Health care. Predictive health care analytics can be used to prevent health risks posed to both individuals and the public. Intel’s health care solutions can even predict the length of patient stays so that hospitals can plan their resources and staffing accordingly.
• Entertainment. Netflix and other streaming services use predictive analytics to generate personalized suggestions for programs viewers are likely to enjoy.

• Marketing. Digital marketers can use predictive models to direct ads to customers who are the most likely to be interested in them. It’s no accident that users often see social media ads for sites that align with their interests.

3. Increases sales

No matter what products or services a company happens to offer, its consumer data contains plenty of clues into how to increase sales. AI can turn all data into valuable insights that can help salespeople identify high-quality leads or even offer feedback on sales calls and interactions.

AI-powered analytics platforms have also revolutionized how companies engage with customers. Companies can now connect with customers 24/7 via chatbots, interact with users on social media platforms, and distribute ads exclusively to specific target audiences.

Tools like Google Analytics, meanwhile, use powerful algorithms to track the success of online sales and marketing campaigns. By using an analytics platform, marketers and business owners can track performance metrics, all while learning valuable information about their customers.

4. Cuts costs

One of the benefits of artificial intelligence is its ability to handle traditionally mundane tasks like data entry, scheduling, or invoicing. By outsourcing repetitive tasks to AI, employees are enjoying the freedom to concentrate on the more important aspects of their work.

In fact, a recent study from the Nielsen Norman Group found that generative AI tools allowed some businesses to increase employee productivity by up to 66% without hiring additional labor. The drastic efficiency boost was largely due to the ability of AI tools to streamline tasks like drafting routine business documents or resolving customer inquiries.

Generative AI tools like Designs.ai are also lowering the barriers of entry for startups and small business owners who may not have a large content creation budget. The program offers design automation services that allow users to create anything from social media posts to newsletters with no previous design experience.

5. Structures data

While many data sources provide access to valuable information, the process of extracting, cleaning, and structuring that data has traditionally been a painstaking task. Data scientists used to be forced to spend huge amounts of time preparing unstructured data for analysis.

These days, however, AI-powered techniques have dramatically simplified the process of converting unstructured data into manageable formats. Data structuring automation is proving the key to freeing up more time for data professionals to focus on the pivotal tasks of data analysis and decision-making.

6. Enhances business intelligence (BI)

Some studies predict that a staggering 180 zettabytes of global data will be produced by the year 2025. Gone are the days when a well-crafted Excel sheet was enough to tackle a day’s or week’s worth of business analytics.

Luckily, many business intelligence (BI) dashboards now come equipped with AI capabilities that turn data into easily digestible information. BI dashboards like Tableau, Sisense, and Microsoft Power BI use AI to translate data into predefined metrics that can be used for decision-making.

Say, for instance, that a sales manager wanted to make sure that each member of a large team hit their performance goals for the month. Rather than checking in with each person individually, the manager could use a BI dashboard to track the performance of the entire team all in one place.

7. Incorporates data visualization

While very few people would be able to find any meaning in a mass of complex datasets, images like graphs or bar charts tend to be much easier to understand. Data visualization is the art of visualizing important insights revealed by data analysis.

While some BI dashboards are able to automate data visualization, some companies also use a combination of human analysts and AI to produce more specific insights. A large corporation, for instance, might use machine learning algorithms to extract and structure data that data analysts then break down into more targeted information.

8. Upgrades workflows

AI has proven an invaluable tool for data science optimization, particularly when it comes to large datasets. AI solutions such as anomaly detection and data processing automation make it possible for human analysts to work with far more data than they ever could alone.

AI-driven data platforms can now automate the extracting, transforming, and loading (ETL) of information from multiple resources, from APIs to webpages. Not only does this allow data scientists to work with as much data as possible, but it also streamlines their workflow so that they can focus on core tasks such as data modeling and management.

9. Conducts segmentation

Data segmentation is the process of dividing data into various groups based on shared characteristics. Imagine, for instance, that a marketing team for a health care company wants to create a weekly email newsletter.

The problem is that its subscribers span a wide range of ages, interests, and abilities. After all, an elderly subscriber is about as unlikely to be interested in an article on the benefits of competitive boxing as a 25-year-old would be in cholesterol tips.

Rather than attempt to find a topic that works for everyone, the marketer might use machine learning models to segment their audience into various groups. That way, they could deliver pieces on separate topics exclusively to the audiences who are the most likely to find them interesting.

10. Performs sentiment analysis

Sentiment analysis, also known as opinion mining, is a type of business intelligence designed to provide companies with unbiased feedback. Rather than rely on customer surveys, sentiment analysis processes unstructured data from online sources where reviewers aren’t shy about giving their most blunt opinions.

For example, a company that just released a new product might use AI tools like natural language processing (NLP) and machine learning to interpret feedback from social media or other online reviews. Sentiment analysis can also be used to gauge public opinion on anything from a certain politician to the reputation of a brand.

One powerful example of sentiment analysis was Dove’s "Real Beauty Sketches" campaign, in which an FBI-trained sketch artist drew women both as they described themselves and as they really looked. The company then carefully tracked the results of the campaign, which revealed that it really did positively change the way women see themselves.

The campaign ended up being a resounding success, raking up over 70 million views on YouTube alone. Sentiment analysis can be a valuable tool for strategy formulation.

AI tools and platforms

Now that we’ve examined how AI tools and platforms can streamline the data analytics process, let’s look at a few examples. Here are some tools that are popular among today’s data professionals.

• Tableau. Tableau is a popular data analysis and visualization tool that translates datasets into valuable business intelligence insights.
• Power BI. Microsoft’s Power BI is a powerful business intelligence visualization tool that can turn multiple sources of unrelated data into actionable insights for better decision-making.
• Microsoft’s Copilot AI solutions. Microsoft recently released an entire AI-powered suite of tools called Copilot. Powered by the Microsoft Cloud, the platform offers everything from enhanced security to productivity and creativity tools.

• Python. While Python itself is a high-level programming language that can be used for multiple purposes, it also offers a series of open-source libraries and tools that can be used for AI data analytics and machine learning.

The future of AI in data analytics

Gartner recently released a list of top upcoming AI trends as gathered from the 2023 Gartner Data & Analytics Summit in Sydney, Australia. Some trends focused on advancements in data analytics, such as cloud data ecosystems and a shift to data-driven AI models.

Others focused on a growing interest in responsible AI, which centers around ensuring that artificial intelligence is used as a positive force while mitigating any threats it could pose to society. Nonetheless, the trends also forecasted that more businesses plan to invest in AI-powered solutions in the coming years.

Put artificial intelligence to work for your team

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Upwork is not affiliated with and does not sponsor or endorse any of the tools or services discussed in this article. These tools and services are provided only as potential options, and each reader and company should take the time needed to adequately analyze and determine the tools or services that would best fit their specific needs and situation.