Decoding the Rise of Augmented Analytics in Business Intelligence
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All the hype surrounding Generative AI has set off a new wave of unprecedented innovations across industries.
We see business leaders trying to get their teams to think and act “data and AI-first.” From automating legacy process workflows to doubling productivity and achieving medical breakthroughs—AI-introduced possibilities are endless, and organizations are in a race to get the most out of AI.
The truth is—without access to the right data, the potential value of AI remains out of reach.
The data landscape globally is expanding, with reports from Statista confirming that the total amount of data created, captured, and consumed worldwide will reach 149 zettabytes by the end of 2024.
Four years from now, this figure is expected to surpass 394 zettabytes. With data complexity on the rise, traditional Business Intelligence (BI) tools struggle to keep up and provide the depth and accuracy needed.
Companies today are at a crossroads—to either limit their analysis to a few key variables or invest a considerable amount of time and resources in data collation and analysis for deeper insights.
Both these options are no good—significantly slowing down decision-making and hindering progress in a data-driven world.
Augmented analytics can tackle these challenges by leveraging AI technologies—and automating and refining complex processes such as data preparation, insight generation, and data visualization.
With augmented analytics, tasks that previously required the expertise of data scientists can now be taken over by analysts and business users—democratizing data-driven decision-making.
In this article, we’ll cover:
- What is augmented analytics?
- Why it matters to businesses today
- Breaking down augmented analytics
- Benefits you can’t ignore
- Real-life scenarios
- Challenges in adoption
- How to get started with augmented analytics
- The future of augmented analytics
- Building a data-first culture
What is augmented analytics?
Using advanced technologies like Natural Language Processing (NLP) and machine learning—augmented analytics enhances data exploration and analysis.
What this means is that with augmented analytics, you can break down complex information with ease, drawing deep insights from your business intelligence platforms and data sitting siloed across various systems. The best part? You can understand data and draw actionable insights through intuitive, user-friendly augmented analytics tools.
Why does it matter to businesses today?
We’ll understand this better with an example.
Let’s say your sales data shows a 20% increase in revenue quarter over quarter.
This means significant growth over the past few months—which is great news!
But what remains unclear is how this was achieved:
- Did your sales team manage to hit their quotas and outperform expectations?
- Or was it just a growth driven by a jump in customer demand?
- It could also be a new marketing strategy that has yielded results.
Traditional BI tools may not be powerful enough to help you get to the bottom of this—given how this calls for extensive data preparation and analysis.
This is where augmented analytics comes into play.
From automating data analysis and offering user-friendly workflows—augmented analytics allows teams to uncover insights and factors that actually led to the 20% spike in revenue.
Beyond sales, this level of data analysis is beneficial across the board—for marketing, HR, finance, and procurement teams.
As a result, augmented analytics is gaining prominence, with the global augmented analytics market expected to reach USD 91.46 billion by 2032.
Gen AI has helped accelerate the proliferation of augmented analytics platforms. Today, there is a greater availability of Large Language Models (LLMs), empowering users with no technical knowledge to query data and interpret actionable insights from raw data.
DataBrain is enabling teams across sales, marketing, C-suite, and finance to derive insights on their own in a matter of seconds.
Breaking down augmented analytics
How businesses analyze data has been redefined with augmented analytics, making it a lot more accessible and actionable. Employing advanced technologies, augmented analytics simplifies complex processes, translating data into meaningful insights for quicker, smarter decisions.
Let’s take a closer look at the key components of augmented analytics:
- AI-powered tools: Transforming raw data into insights
AI-powered data analysis tools are automating the end-to-end data processing task—spotting patterns and trends with greater precision. They are no match for traditional tools and workflows that end up taking hours of manual effort. These tools analyze massive datasets in seconds, delivering deep insights packed with key metrics for sustainable business growth.
For instance—predictive analytics help businesses forecast market trends, while anomaly detection identifies irregularities, empowering businesses to act proactively and be prepared for any possible disruptions. Backed by the speed and accuracy of AI tools, businesses can skip the manual bottlenecks and spend time on strategy rather than data crunching.
- Interactive dashboards: Bridging the gap between data and users
While deriving insights from raw data is quite the task, presenting them in an easy-to-understand and visually appealing manner is another aspect that most businesses prioritize today.
Augmented analytics uses interactive dashboards that are designed to pull up insights from datasets and present them as charts, tables, heatmaps, and other visually engaging formats. This simplifies data representation and makes it easy to consume for users with zero or limited technical knowledge.
Moreover, customizable features and add-ons like drill-down and filtering options help users look up specific metrics or do a comparative analysis, further ensuring informed decision-making. Augmented analytics turns abstract data into clear narratives, ensuring stakeholders at all levels are equipped with the insights they need to make data-backed decisions.
- NLP interfaces: Asking questions in plain language
Additionally, augmented analytics uses NLP interfaces, which makes querying data as basic as posing simple questions. Instead of figuring out complicated process workflows and systems, users can input questions like—“What were last quarter’s sales figures?” and receive understandable, on-point responses.
This removes the need to be equipped with specialized knowledge, enabling data-driven insights to be accessible to anyone on the team. What augmented analytics does is that it democratizes insights, and with the help of NLP, ensures that data becomes a powerful tool for just about everyone in the organization and not just the experts.
Augmented analytics benefits that you can’t ignore
Augmented analytics offers businesses the opportunity to unlock deeper insights faster and more efficiently. By simplifying data processes, organizations can move beyond traditional analytics and make smarter decisions that lead to better outcomes.
- Faster insights for everyday decisions
With augmented analytics, platforms bring multiple data sources together in a unified view. This allows businesses to gain a clearer picture of what is happening, why it is happening, and when it will occur, enabling better insights into market events and performance issues.
Let's say a customer support team faces a sudden rise in ticket volume, making it hard to identify the root cause. Augmented analytics scans support data from multiple sources, such as chat logs and email interactions, to spot patterns. It reveals that a recent software update led to configuration issues for many users. The team uses this insight to fix the problem faster, reducing incoming complaints and improving service quality.
- Smarter decisions at all organizational levels
Getting into the details of your data to extract valuable insights has always been a challenge. With augmented analytics, you can drill down to the most granular level of your data.
Automatically querying billions of data points across multiple variables in seconds provides accurate insights and helps answer the “why” behind results. This empowers all levels of an organization to make more informed predictions.
Below is how smarter decisions can be made at various levels:
- Reducing reliance on complex data teams
Data analytics traditionally requires highly skilled data scientists to process large datasets and extract value. Augmented analytics changes that dynamic by simplifying complex processes.Now, analysts and business users can analyze data, generate insights, and make decisions without needing deep technical expertise, reducing the reliance on specialized data teams.
- Keeping pace with market trends through predictive insights
According to a report by EY:
- Nearly two-thirds of CEOs expect a rise in their revenues and profits
- 58% of them are scaling up their business transformation plans
- And 41% are keen to adopt AI technologies to drive efficiencies and significantly enhance business performance
To be able to achieve this, businesses need to anticipate trends, predict possible outcomes, proactively optimize resources, and strategize to tap into future growth opportunities. This is where predictive insights gained from augmented analytics will ensure that businesses are on track to achieving their revenue and profit targets while evolving with market trends to stay ahead of the competition.
- Handling large datasets with ease and accuracy
Traditionally, when businesses had to work with large datasets, they had to build custom models that were tailored to specific queries. Building these models took time and called for technical expertise. It also led to siloed insights that would remain with technical experts, limiting collaborations and resulting in teams overlooking critical insights.Augmented analytics has brought considerable change in this area by enabling a shared collection of data and output of insights derived—ensuring information is accessible to all in an organization. Moreover, by collecting data from teams across the organization like sales, marketing, and customer support—augmented analytics keeps everyone on the same page, working with up-to-date information.
- Bridging the gap between technical and non-technical users
One thing that is commonly seen across organizations with growing volumes of data is delayed insights. When insights are delayed for weeks—businesses miss their window of opportunity.With augmented analytics, businesses can solve this by analyzing vast datasets at speed and providing detailed explanations and in-depth analysis for every metric—ensuring that both technical and non-technical users can access the information they need to make decisions.
DataBrain’s intelligent semantic layer establishes a single source of truth for marketing teams, ensuring reusable metrics, definitions, and transformations are applied consistently and in a standardized manner across the organization.
How functions across organizations are using augmented analytics
Augmented analytics is reshaping how businesses make decisions. Let’s take a closer look at some real-world cases of functions like marketing, HR, and finance employing augmented analytics to optimize their operations and make better, data-backed decisions.
- Marketing teams optimizing campaigns
From analyzing a growing treasure trove of customer data to optimizing campaigns and targeting the right audience—marketing teams can use augmented analytics to ensure they are fine-tuning their strategies to resonate better with their customers.
Augmented analytics helps marketers analyze past behaviors and identify trends and patterns in customer usage. They can also use this information to predict future trends and further refine their messaging to make it more apt and contextual to their user base.
For example, with augmented analytics, marketing teams can understand which email campaigns have resulted in the highest conversion rates and further adjust strategies to maximize engagement.
Here are a few other cases where augmented analytics can be used by marketing teams.
- HR professionals forecasting hiring needs
In the current market that is marked by trends like the ‘Great Resignation’—HR professionals are at their wits end with it when it comes to predicting staffing needs and tapping into the right talent.Augmented analytics can be leveraged to process data across multiple sources, such as industry trends, employee performance, and turnover rates, to project future hiring needs. This also helps hiring teams understand which roles to prioritize, when to hire, and how to retain employees over the long run.
DataBrain’s HR dashboard provides specific insights into employee trends, talent gaps, and performance parameters on an individual and team basis. By further customizing this dashboard—HR teams can track hiring progress, monitor workforce performance in real time, and spot opportunities to reduce turnover.
- Finance teams detecting anomalies in transactions
When it comes to finance teams—they can use augmented analytics to analyze transaction patterns in real time and quickly detect irregularities or fraudulent activities. With the help of machine learning models, finance teams can spot unusual spending behavior or any discrepancies that would be otherwise hard to identify manually. This level of proactive detection helps finance teams mitigate risks and improve the accuracy of their financial reports.
DataBrain’s financial dashboards empower finance teams by offering them customizable visuals to analyze transactions and monitor anomalies. This way, financial professionals can stay on top of their spending, ensure it is aligned with their budgets, and tackle any potential issues before they escalate. That’s not all; DataBrain’s embedded analytics enable finance teams to gain quick insights and share them in a consumable manner with other stakeholders in the company.
Challenges in adoption
Adopting augmented analytics comes with hurdles such as resistance to change, complexity, and the need for data literacy. Users must embrace new tools and concepts to unlock the potential of these platforms and drive value across functions.
- Overcoming resistance to change
A major challenge is helping users move past their reluctance to adopt new technology. While users don’t need to become technical experts, understanding data basics is key to making augmented analytics work. Pairing analytics tools with data literacy programs can ease this transition, empowering users to gain meaningful insights.
Intuitive platforms with customer-facing analytics features can further reduce resistance by simplifying adoption.
- Complexity of dealing with augmented analytics
The robust nature of augmented analytics can be overwhelming for some users. Tools that are too complex may fail to gain traction, regardless of their capabilities.
Platforms with familiar interfaces and straightforward features are critical for encouraging adoption. Simplifying access to insights allows users to focus on decision-making rather than wrestling with the tool itself.
- Data literacy and analytics proficiency
Augmented analytics relies on users' ability to interpret data accurately. Machines can identify patterns, but human expertise is needed to understand causation.
Providing training and resources to bridge the skills gap is essential. Without proficiency, even the most advanced recommendations might not translate into actionable decisions.
- Understanding the boundaries of augmented analytics
Despite its capabilities, augmented analytics has limitations. It may struggle with scalability when handling complex datasets or fail to account for external factors, such as industry nuances.
Unrealistic expectations of AI tools can further hinder adoption, as some users might expect results beyond what the technology can deliver. Educating users on these boundaries is necessary for setting realistic goals.
How to get started with augmented analytics
Starting with augmented analytics requires preparation and a structured approach.
Following these steps can help you build a solid foundation for success.
Your checklist for adoption:
✓ Evaluate your data readiness
Assess whether your organization has the data infrastructure and quality needed for augmented analytics. Identify gaps in data collection, organization, or access that might limit the tool’s effectiveness.
✓ Choose the right analytics platform
Look for a platform that aligns with your business needs. Prioritize intuitive tools that are easy to use and can integrate with your existing systems.
✓ Upskill your workforce for analytics adoption
Invest in training programs to improve data literacy and analytics proficiency. Equip teams with the knowledge to interpret insights and apply them effectively in their roles.
The future of augmented analytics
Augmented analytics is set to redefine how businesses operate, with advancements in AI, self-service capabilities, and transparency. These developments are making analytics more accessible and reliable, addressing concerns about usability and data security.
- AI integrating deeper into daily workflows
Artificial intelligence is becoming integral to everyday business functions. According to a McKinsey survey, 65% of organizations now use generative AI in at least one function, compared to just a third last year.
This shift highlights how AI is evolving from a strategic tool to an essential part of routine operations, driving smarter and faster decisions.
- Evolution of self-service analytics
Self-service analytics tools are becoming more advanced, enabling users without technical expertise to access insights easily. By offering user-friendly interfaces and automated features, these tools empower employees across departments to make data-driven decisions without relying on specialized teams.
- Tools becoming more intuitive and inclusive
Analytics platforms are increasingly focusing on inclusivity by catering to diverse user needs. This includes simpler interfaces, customizable features, and natural language processing capabilities that allow users to interact with data conversationally.
- Addressing growing concerns about data security
As analytics tools become more powerful, protecting sensitive data is a growing priority. Organizations are investing in robust encryption, access controls, and compliance with regulations to safeguard information.
- Greater emphasis on transparency in AI decisions
Transparency in AI-generated insights is crucial for building user confidence. Businesses are focusing on explainable AI, ensuring users understand how decisions and recommendations are made. This emphasis on clarity helps mitigate biases and aligns AI outcomes with business objectives.
Building a data-first culture
As businesses move toward data-driven operations, adopting a data-first mindset is becoming essential. Accenture predicts a future where data will be recognized as a tangible asset on balance sheets and a driver of market value. CEOs must treat data as strategically as human, financial, and intellectual capital. This means focusing on acquiring, refining, protecting, and deploying data to stay competitive.
Augmented analytics from a customer analytics perspective
Customer-facing analytics is a cornerstone of augmented analytics, offering businesses the ability to anticipate customer needs and deliver tailored experiences. By adopting hyper-personalized marketing strategies driven by data and AI, companies can respond dynamically to customer preferences.
Take McDonald’s as an example. Facing declining performance, the company initially partnered with UberEATS to drive online delivery, generating $4 billion in annual sales by 2019. Recognizing the need for deeper transformation, McDonald’s restructured its operations around data. The company used machine learning and mobile technology to personalize orders, adapt to external factors like weather and events, and fast-track product development. This data-centric approach allowed McDonald’s to meet customer expectations and stay ahead of competitors.
With augmented analytics, businesses can adopt similar strategies by analyzing customer behavior patterns, identifying trends, and responding with precision. From predicting product preferences to optimizing marketing campaigns, the potential for customer analytics is vast.
DataBrain empowers organizations to embrace a data-first culture and harness customer analytics. With its intuitive BI tools, business teams can uncover actionable insights, and product teams can craft tailored embedded analytics for customers. Whether it's sales, finance, marketing, or leadership, DataBrain delivers insights in seconds, transforming decision-making processes and enabling high-performance outcomes.
Take the first step to a data-rich company with DataBrain
Augmented analytics is transforming how businesses make decisions, turning data into actionable insights with minimal effort. By integrating AI and analytics into workflows, organizations can simplify complex processes, uncover new opportunities, and stay competitive. The ability to derive real-time, data-backed insights is no longer optional—it’s essential for driving growth and innovation.
As the adoption of augmented analytics accelerates, businesses that prioritize its integration will gain a clear advantage. Investing in tools that simplify analytics and empower teams with self-service capabilities can unlock new potential. Now is the time to embrace this transformation and lead the way in data-driven strategy.
Shape a data-first culture in your organization with DataBrain’s advanced AI-powered embedded analytics.
