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AI-powered augmented analytics: capabilities and benefits for business

By Aliona Surovtseva, Innovation Analyst
Published on

In 2023, the amount of data generated by businesses and individuals reached 120 zettabytes. In the coming years, it is expected to grow at a staggering 150% rate, hitting the 180 zettabyte mark by 2025.

Traditional business intelligence (BI) and data analytics tools struggle to efficiently manage large amounts of data. The issue first became apparent during the pandemic, prompting 83.9% of organizations to increase spending on advanced data analytics solutions.

One such solution is augmented analytics—a powerful combination of traditional analytics tools enhanced with artificial intelligence capabilities.

In this article, we’ll examine what augmented analytics really is, how it works under the hood, and in what ways it could transform your analytics flow.

So, what is augmented analytics?

Let’s start our review with the augmented analytics definition.

Gartner coined the term “augmented analytics” in 2017 to describe any data analytics solution that uses artificial intelligence (AI) or its subsets, such as machine learning (ML) and natural language processing (NLP), to help people make better data analytics decisions.

As the name suggests, augmented analytics enhances a human’s ability to interact with data, leaving the actual decision-making to a human rather than replacing it—”enhances rather than replacing” being the operative words.

This is the first and foremost thing you must keep in mind when considering using augmented analytics in your company: the technology will not replace your employees. Instead, it will help reveal insights that were previously hidden in large amounts of your operational data.

Let’s proceed to augmented analytics’ inner workings.

Essentially, in one of its use cases, augmented analytics allows users to interact with databases using natural language instead of writing SQL queries. This way, your non-technical employees can become as effective at data analytics as your in-house IT department. Augmented analytics can also be helpful for data analytics professionals, enabling them to perform more impactful analytics activities at a faster pace.

Let’s compare these abilities with those of traditional business intelligence (BI), self-service BI, and data analytics tools.

Augmented analytics vs. traditional BI and data analytics solutions

The shortcomings of traditional business intelligence and data analytics solutions compared to augmented analytics tools include:

  • Over-reliance on IT specialists. Traditional BI and data analytics tools often lack easy-to-use self-service capabilities. To generate reports, business users thus need to enlist the help of internal IT departments, waiting for actionable insights for weeks on end.

  • Manual data preparation. When working with traditional BI and data analytics solutions, data scientists need to collect and clean up raw data manually. This eats up too much time and opens up the possibility of human error.

  • Data consolidation challenges. Data sources may number in the hundreds—the average number of data sources used by enterprises has already reached 400. It takes a good deal of time and effort for enterprises to merge multiple sources and bring terabytes of siloed data together into a single view to leverage them for insights in traditional BI tools.

  • Business decisions based on guesswork rather than data. Conventional BI systems respond exactly to the queries business users make. Being retrospective, these systems analyze past data in static formats, such as dashboards, charts, and graphs, and surface only those insights that decision-makers are querying, leaving no room for hidden insights, smart predictions, or unexpected results.

  • Inability to analyze unstructured data. BI and data analytics tools lacking AI capabilities only work with structured data, which represents roughly 10% of all the data any company has. To draw insights from unstructured data, such as emails, documents, visual content, and sensor readings, you need to either structure it manually or spice up your system with AI.

Augmented analytics vs. self-service BI tools

Unlike traditional data analytics tools—and pretty much like augmented analytics—self-service BI solutions are designed to make business intelligence accessible to non-tech users on demand:

  • Owing to easy-to-navigate interfaces and strong analytics capabilities, self-service BI tools help non-tech users create ad hoc reports with minimal or no assistance from a technical person.

  • Self-service BI solutions allow users to ask follow-up questions and explore the insights manually for more personalized results.

With this in mind, the benefits self-service BI tools bring seem quite obvious. These include the democratization of data, reduced time to insight and accelerated time to action, cost-efficiency, more informed decision-making, and personalized reporting for more relevant insights.

Yet, though a world away from traditional data analytics tools, self-service BI tools aren’t free from shortcomings. For one, many self-service BI solutions require manual data ingestion and human analysis. Given the explosive growth of data and its complexity, drag-and-drop visualization interfaces make it next to impossible even for expert data scientists to use self-service BI tools to prepare and efficiently analyze larger volumes of data manually.

Augmented analytics tools have emerged as a viable solution to these problems, surpassing their traditional and self-service counterparts by a large margin.

Let us summarize the benefits AI-powered analytics tools bring to the table.

Benefits of augmented analytics

  • Automation of data identification, preparation, and analysis through recommendations

    Unlike traditional BI tools, augmented analytics platforms are capable of handling multiple data sources—live data streams, Excel spreadsheets, cloud storage, in-house databases, numerous apps, and more—thereby making it possible to process structured and unstructured data. Some AI and machine-based analytics technologies can automatically identify certain attributes of data, such as phone numbers or email addresses. Furthermore, one of the key benefits of augmented analytics is the ability to provide users with AI-driven recommendations. Automated recommendations span data discovery, cleansing, and analysis. For example, an augmented analytics system can recommend which datasets to include in analyses and suggest other datasets that might be useful for getting the results users expect. Likewise, powerful analytics algorithms can index and group related words during data preparation, thereby reducing the manual labor of searching for fields and values. Simply put, AI engines do much of the heavy lifting by automating manual and labor-intensive tasks, thus supercharging the entire analytics process and accelerating insight to action.

    However, in the context of business process automation, we need to reemphasize that augmented analytics does NOT replace people—it is designed to augment human efforts by combining the power of automation and human expertise.

  • Higher accuracy and reduction of bias

    Alongside a high degree of automation, advanced analytics algorithms help extract highly reliable and accurate real-time insights to underpin confident and unbiased decision-making. Unlike humans, AI systems can draw hidden and unobvious yet critical insights that business users might not even be aware they need by identifying and suggesting all possible relationships in data that lie under the surface. Looking under every tree and automatically querying billions of data points, ML models help humans build confidence and avoid faulty assumptions and biases in their conclusions that are typical of manual forecasting.

    There is one caveat, though: while AI has gone far in accuracy, a machine learning model can’t be 100% accurate. To achieve a higher level of confidence and make sure the results aren’t due to chance, the estimates may require additional validation and verification on the part of the user.

  • Data democratization

    Your company’s key decision-makers are frequently non-technical users. Your organization will be unable to respond quickly to market changes unless these non-technical users can explore, analyze, and act on your operational data. Augmented analytics systems that use ML, NLP, and natural language generation (NLG) technologies reduce the need for data analysts by allowing non-IT personnel to interact with data directly through conversational queries and receive results in the form of simple visualizations or text summaries.

    Furthermore, these systems free up data scientists‘ time for more complex tasks by eliminating the need for them to participate in basic data preparation and routine reporting.

    However, putting insight at the fingertips of anyone who needs it raises data security and privacy concerns; we’ll be discussing this in more detail in the augmented analytics challenges section.

  • Predictive and prescriptive analysis

    Simply being data-driven is not enough anymore. Businesses must adopt a forward-thinking mindset in order to make strategic, future-oriented decisions. Next-generation AI-augmented analytics extend traditional insights by providing real-time predictive analysis. This allows business users to make informed decisions quickly by employing statistical and machine-learning algorithms to understand causes, predict outcomes, and identify trends. Augmented analytics provide instant, reliable recommendations for the next steps, allowing for informed, future-focused decision-making.

Augmented analytics in action: using Power BI’s Copilot to turn data into actionable insights

There are several out-of-the-box augmented analytics solutions available on the market that offer the outlined capabilities to non-tech teams. Among the most popular tools are Einstein Discovery for Tableau, Copilot for Power BI, and Insight Advisor and AutoML in Qlik Sense.

Let’s delve into the use cases and capabilities of augmented analytics by using Power BI Copilot as an example. Introduced by Microsoft, Copilot for Power BI has proven to be the most robust and powerful tool currently available on the market.

Copilot for Power BI is an integration of OpenAI’s language model technology, specifically designed to enhance the capabilities of Power BI, a business analytics service by Microsoft. Its key capabilities span natural language query processing, data insight generation, reporting, and continuous learning, among others. By combining generative AI with your data, the tool helps uncover and share actionable insights in an instant.

Below, you’ll find a detailed overview of Copilot’s functionality mapped against its use cases in business.

1. Using Copilot to create or explain reports

As someone who isn’t a data analytics expert, you can still effectively use Copilot in several ways:

  • Creating reports and visualizations

    If you’re unsure how to start with a report, simply ask Copilot to recommend content or create a report based on your instructions. For example, you might say, “Show me how sales of different products and categories vary by region and the impact of discounts,” and Copilot will generate relevant report pages with visualizations for you.

  • Generating narrative summaries

    For any report you have, Copilot can produce a clear text summary that makes the data easy to understand. Just ask it to summarize a whole report, specific pages, or even particular charts or graphs.

Remember, while AI can be a powerful tool, it has its limits. It’s important to be clear in your requests and to check the AI’s output to ensure accuracy and relevance.

2. Using Copilot to spot trends and anomalies in your data

Power BI has a built-in feature that helps find insights in data. Practically, insights are based on the fluctuations of data points and KPIs that look “unusual” and thus may cause interest.

Technically, the insights are split into three categories: anomalies, trends, and KPI analysis. Once an insight is found, the feature provides a notification and an explanation of this insight.

Insights are auto-generated every time you interact with a report:

  • Anomalies

    These are unexpected spikes and dips in the time-series data— i.e., any values that fall outside of the expected or normal range. Anomalies are scored and categorized as “significant” (based on the score) and/or “recent” (based on timing). For example, there was a consistent growth in sales from January to September. However, in September, the sales suddenly declined by 70%. Copilot can detect and flag this unusual drop, prompting further investigation into potential causes such as supply chain disruptions or shifting consumer preferences.

  • Trends

    Power BI has an algorithm that determines whether a prolonged increase or decrease in time-series data is significant. If found to be significant, it is classified as a trend. The trends are then categorized as Long, Steep, Recent, or Trend Reversal. For example, the augmented analytics solution might highlight that sales of a particular product have been steadily increasing over the past quarter, suggesting growing market interest or effective marketing strategies.

  • KPI analysis

    Depending on whether KPI analysis includes a target, a Power BI algorithm either calculates the variance of the current KPI value to its target or compares the current value itself to other data segments. For instance, Copilot can compare the financial performance of different products or services, identifying bestsellers and underperformers. It can calculate the average revenue, cost, and profit for each product category, giving insights into which categories are most profitable or cost-intensive. This insight can then guide strategic planning, inventory management, and promotional efforts.

Once an insight—an anomaly, a trend, or a KPI variance—is identified, Power BI proposes an explanation, which is a textual description. For an anomaly, it estimates a correlation with available dimensions in a data model to find the factors that influenced—and probably caused—the anomaly. For a trend explanation, Power BI identifies the categories that influenced the increase or decrease in the trend. As for a KPI analysis explanation, the tool identifies the categories that have significantly high or low values.

3. Using Copilot’s Q&A feature for natural language queries

Copilot has a feature that lets you ask questions about your data just like you would talk to a person. For example, you can ask, “What are the total sales by quarter and product category?” and it will show you a chart or graph that answers your question. This is really useful for finding specific information or putting together reports without needing to know a lot about data or computers.

When you ask a question, Copilot figures out what you mean and finds the right information from the data it has. It even picks the best way to show you the answer, like using a line chart or a map, based on what you’re asking about.

Augmented analytics use cases

Here are some augmented analytics use cases with real-world augmented analytics examples of how it can help companies and enterprises across industries win big.


Success in sales hinges largely on the ability to grasp customers’ current wants and needs and promptly refocus your offering to meet them. This is where augmented analytics might make a big difference. In the retail sector, augmented analytics use cases revolve around generating real-time insights to help understand customer behavior, preferences, and churn rate, fine-tuning marketing campaigns, optimizing bidding, enhancing inventory management, and a lot more.

ITRex has an outstanding example of how a leading retail corporation adopted an AI-driven big data and self-service data analytics solution to provide their almost 3 million internal users with hassle-free on-demand access to almost 3 petabytes of data collected every hour.


Electronic medical records (EMRs) and electronic health records (EHRs) have made the healthcare industry incredibly data-centric. Yet, many healthcare professionals struggle to analyze this wealth of data manually. Augmented analytics provides a solution, enabling clinicians to offer timely care and make faster, more accurate clinical decisions. Some other augmented analytics examples revolve around using the technology to improve diagnosis accuracy, enhance patient care with personalized treatments, optimize clinical trials, monitor adverse effects, boost patient engagement, improve operational efficiency, and predict disease outbreaks.

A notable application is the AI-powered platform for cancer treatment developed by ITRex for PotentiaMetrics. It uses predictive analytics and extensive patient data to help make precise treatment decisions.

Finance and banking

The financial sector is another industry that benefits the most from AI-powered analytics. By implementing advanced analytics techniques, financial firms can unlock massive gains in efficiency, risk management, and customer satisfaction. One of the most common augmented analytics use cases in banking is reviewing loan applications to assess risks and minimize the likelihood of payment defaults. Augmented data analytics makes it possible for financial lenders to thoroughly analyze every combination of data points and identify the customer attributes and behaviors associated with higher credit risk in next to no time. ML algorithms automatically sift through a wide range of data points, such as the applicant’s age, credit score, and payment history, to assess the applicant’s creditworthiness and the probability of default.

By leveraging the AI-driven Contract Intelligence platform, known as COiN, JPMorgan Chase & Co., a global financial services giant, automates the process of interpreting complicated commercial loan agreements. Reportedly, the platform allows the firm to dramatically reduce the time spent on completing regular tasks from 360,000 hours to mere seconds.

These examples of augmented analytics use cases clearly illustrate the endless opportunities AI-driven analytics creates for industries and businesses to revolutionize the way they operate. Yet, many businesses still make do with conventional BI solutions or are sitting on the fence, having no clue as to what pitfalls might come their way when adopting augmented analytics.

Let’s explore the key challenges companies can encounter and the best practices for navigating them.

Augmented analytics challenges and adoption best practices

As is the case with any novel technology, augmented analytics comes with certain considerations and challenges and requires careful adoption to extract maximum value from the benefits we outlined above.

Here are some common hurdles:

  • Misunderstanding of augmented analytics' cognitive capabilities

    Inflated expectations of what machines can actually do can lead to wasted investment. Augmented analytics lacks human qualities like intuition, creative thinking, emotional intelligence, and common sense, which are critical for data analysis. It operates within predefined limits and relies on the data provided, emphasizing the importance of human oversight in drawing insights.

  • The 100% accuracy illusion

    The belief that machine learning models can achieve perfect accuracy is unrealistic. Errors and inaccuracies are inevitable because these models cannot fully understand complex systems. This misunderstanding can lead to misplaced trust in AI’s capabilities and potentially flawed decisions.

  • Improper data management

    Effective data and model management are essential. Poor-quality data results in inaccurate predictions and recommendations, underscoring the importance of good data practices.

  • Lack of data governance

    While augmented analytics promotes data accessibility, it also raises data privacy and security concerns. Balancing user autonomy with data integrity and security is a critical challenge. Trading control for availability is prohibitive.

  • Having it all immediately

    Trying to implement augmented analytics across an entire organization at once can lead to massive disruption and resistance. Overambitious rollouts make no sense.
    A phased, considered approach is more likely to succeed.

In summary, successful augmented analytics adoption requires managing expectations, integrating human judgment, maintaining high-quality data, ensuring secure data governance, and adopting a gradual implementation strategy.
Below, you will find the essential augmented analytics implementation tips from the ITRex data consultants:

  • Do not overestimate the powers of augmented analytics, shifting the focus from human intelligence to machine intelligence. Keep in mind that people are your main asset and must remain such, as machines are unable to replace the human brain’s power to create, improvise, consider and interpret context, and think critically. With this in mind, fostering data literacy and the development of essential critical thinking skills across your organization should be your top priorities. The way your employees and machines interact determines how efficient the results will be. Teach your people to think outside the box and beyond the numbers, and incentivize them to develop those skills that are harder for machines to handle. Make sure your employees have a precise understanding of how AI works and how it can be used best to serve human interests. You may try building immersive training programs like Bloomberg, Guardian, and Adobe did to help employees across all business roles learn how to leverage data analytics tools and predictive analytics to capitalize on business opportunities.

  • Set realistic expectations for machine learning and AI—uncertainty exists no matter what. It’s important to recognize that ML models can’t achieve a 100% accuracy level. Nor can they identify all data sets and formats. Deciding whether the data output is accurate and statistically significant is usually a human’s prerogative. Bear in mind that a fair share of data discovery work will anyways fall on the shoulders of your human users. This includes validation of data outputs, analysis of complex data sets, or data transformations. By having a realistic understanding of a machine model’s capabilities, you can turn its constraints into opportunities.

  • Make sure you train ML models using high-quality, unbiased, and error-free data and regularly update the algorithms as your data changes. Note that AI is as unbiased as the data it has been trained on. See to it that you have solid data management practices in place that empower your users with accurate and reliable insights derived from well-managed data assets.

  • In the pursuit of giving more users access to augmented analytics tools, it’s mission-critical that you also remember to provide efficient data security. Establishing an effective and clear framework for sustainable data governance within your organization is pivotal to upholding data privacy, security, and compliance while enabling hassle-free access to meaningful data across departments. This will allow you to drive maximum value from your data in a controlled and compliant manner.

  • Start small; think big. It helps to break down your big data analytics initiative into feasible implementations and consider the end-user experience every step of the way. That is to say, it’s a good practice to start with a certain department or a use case that has high business value and incrementally tune the system based on user feedback while also having a long-term strategy in place. Once you’ve implemented augmented analytics on a small scale and have got end-user buy-in, you may then extend it to broader use cases throughout the organization. It’s critical that you start small to prove the value before scaling the system company-wide.

Key Takeaways

Augmented analytics is well on its way to revolutionizing the BI landscape. By combining artificial intelligence, machine learning, and natural language processing, it sets the stage for data exploration and analysis at speed and scale. As the Gartner report states, “Having to manually find patterns in the data is like looking for a needle in the haystack. Augmented analytics helps find the needle faster by acting like a giant magnet hovering over the hay.” (Gartner, Augmented Analytics Is the Future of Analytics, Rita Sallam, Carlie Idoine, 30 October 2019)

However, it’s critically important to understand that augmented analytics is not a magic bullet capable of solving all analytics problems overnight. Note that any new technology wreaks havoc early on in the implementation phase and usually reveals its true value only after some time. The same holds true for AI-driven analytics. Through augmenting human intelligence across the entire data analytics life-cycle and democratizing data science, augmented data analytics allows for making smart, confident, and informed decisions at the speed of business. Less manual intervention, and hence errors. Little to no dependency on tech experts. Fewer decision-making biases. No more second-guessing. Augmented analytics is poised to do away with this. Once and for all.

So, what is augmented analytics?Augmented analytics vs. traditional BI and data analytics solutionsAugmented analytics vs. self-service BI toolsBenefits of augmented analyticsAugmented analytics in action: using Power BI’s Copilot to turn data into actionable insights1. Using Copilot to create or explain reports2. Using Copilot to spot trends and anomalies in your data3. Using Copilot’s Q&A feature for natural language queriesAugmented analytics use casesRetailHealthcareFinance and bankingAugmented analytics challenges and adoption best practicesKey Takeaways
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