Focus On Results Not Process: Augmented Analytics Guide

Data is now like air. It’s all around us. Every manager, front-line employee, and business user needs to be able to breathe it in, break down its composition, and leverage it to inform their decision making. Most importantly, they need to do it fast and organically to capitalize on fleeting opportunities.

Augmented analytics makes it easier for data scientists to nail transformational discoveries — while also accelerating decision intelligence for everyone, without a data background. With 50% of business decisions predicted to be augmented or automated by AI agents by 2027, the early adopters stand to reap the biggest rewards.

What is augmented analytics and why is it a step above traditional analytics?

Augmented analytics is the type of advanced data analytics that builds on artificial intelligence and machine learning to democratize business insights, take on busywork from data science teams, and provide tailored suggestions to users based on their roles, preferences, and past behaviors. Augmented analytics also automatically generates such complex capabilities as forecasting and model building.

Unlike conventional analytics, augmented analytics tools don’t just follow instructions. They go out of their way to anticipate your needs based on the contextual and behavioral cues pulled over time and offer you the insights you never knew you needed. This transformative approach to business intelligence stems from the following enabling technologies:

• Machine learning — as the core engine for augmented analytics, machine learning sets the overall framework for intelligent automation, allowing such tools to autonomously level up based on new structured and unstructured data. Machine learning is also where advanced analytics functions like predictive analytics come from.
• Conversational AI — the combination of natural language processing (NLP) and natural language generation (NLG), enhanced by detailed prompt-engineering, enables analytics solutions to build on internal and external context (RAG), turn complex data into a clear, concise narrative digestible for anyone, and go out of their way to offer insights proactively.
• Automation — the final piece of the puzzle that enables these solutions to handle routine tasks across the data analytics lifecycle, such as data preparation, cleaning, and integration.

Augmented analytics grows up: agentic analytics

Augmented analytics is an ever-evolving field that has lately collided with AI agents, programs capable of autonomously performing tasks on behalf of a user or another system. While augmented analytics is focused on enhancing human decision intelligence, agentic analytics tools aim to proactively identify problems, generate solutions, and even take action based on the insights — and do so with no or minimal human intervention.

Also, agentic analytics solutions can actively go out and tap external environments beyond their initial training set to handle complex, multi-step analytics tasks.

Watch augmented analytics software in action

Your CRM system, accounting software, ERP platform, and other business software jot down real-time actions and changes. They are awash with data that could help you save money and boost profits. But that’s not happening because traditional analytics tools are a tough lift for everyone but its savvy users. AI augmented analytics fixes that.

Automating data management

To become insights, your data — that comes from a variety of sources — needs to be consistently formatted and cleaned. But if it comes in wildly different forms and shapes, your data analytics team will spend days scrambling to understand the structure before they can get down to analysis.

One of the biggest benefits of augmented analytics is that it introduces automations throughout the entire data management lifecycle:

• Collecting data from multiple, sometimes complex sources, automatically identifying different data types and relevant attributes within datasets, including outliers. 
• Cleaning and preparing the data (filling in missing values, identifying outliers, removing duplicate records, etc.).
• Indexing and clustering data (semantic indexing, patterns/trends/segments identification).
• Revealing hidden connections in datasets, generating hypotheses from these connections, and automatically building predictive models.

Democratizing access to data

When strategizing their efforts, every company grapples with three core questions: What happened? Why? What’s next? Classical analytics with its learning curve makes finding those answers, let alone easily accessible ones, a challenge. Not every person on your team can boast the knowledge of statistical or analytical methods — or SQL commands.

Augmented data analytics allows users to ask questions in plain English instead of writing a complex query. The NLP engine inside acts as a translator and intermediary, making data insights more intuitive, approachable, and accessible to everyone. In simple words, the interaction boils down to the user asking the tool, “What were our sales in Q3?” and the system breaking down the stats in response.

Giving recommendations to users

Another reason why big data and augmented analytics tools are billed as the new era of insight generation is because they speak your industry semantics and can broadly track user behaviors. It means that over time, they sync with the user’s chain of thought and proactively suggest actionable insights, metrics, and insights that matter most to them and their work, curated into a custom digest.

Users might not ask explicitly, but augmented analytics solutions anticipate their needs and surface relevant information before they even ask. It’s less about asking the right question and more about the platform already knowing what you might need. 

Visualizing data

Augmented analysis systems don’t just explain the why behind certain trends. If married with multimodal systems, they can also visualize data on demand, choosing visualizations that best communicate the key insights. Not only that, but visualizations in this case can incorporate deeper insights across multiple modalities, such as images of popular products and snippets of customer reviews.

With augmented analytics, you can also endlessly get into the nitty-gritty details of your data with no pre-defined drill paths or data aggregation required.

Breaking down the what, why, and how with no effort

100% automated insights, 0% busywork — that’s likely the biggest benefit you can gain from augmented analytics. It redefines the way companies approach analytics, making the whole process hassle-free. Users can simply ask the system for insights and let it determine the best approach to dissect data. 

Whether it’s forecasting, goal seeking, scenario analysis, or any other data task, augmented analytics takes the heavy lifting from a human and returns insights in a ready-to-use form. And if the system comes across any complementary data, it’ll surface it, too.

What augmented analytics can do that BI cannot

Essentially, traditional BI tools empower users to analyze data, while augmented analytics tools empower users to understand it, with or without the necessary technical background. 

The mechanics of augmented analytics tools 

The foundation of augmented analytics is a complex orchestra of interconnected components that parlay into a holistic system, uniting data, technology, and human understanding. It all begins with a robust data integration layer that connects a myriad of structured and unstructured data sources, including SQL/NoSQL databases, CRMs, ERPs, streaming data sources, IoT devices, external systems, and more. The system retrieves the relevant data points and transforms them into a consistent and usable format on its own.

Augmented analytics relies on AI and machine learning to automate data exploration, build predictive models, and generate deeper insights. Based on the insights and predictions, the system can recommend an optimal decision path — or even automate decisions based on the analysis if it’s operating within well-defined and repetitive scenarios.

Who can benefit from augmented analytics?

Regardless of the use case, augmented analytics capabilities do not aim to replace human judgment — they amplify it. In this case, the entire paradigm is being shifted: instead of dealing with the tedium of data collection, data analysis, and insight explanation, humans become strategic supervisors.

• Business users like marketing teams can shorten the time-to-insight, improve their data literacy by tapping into insights previously locked behind technical expertise, and focus on taking action. For example, marketers can monitor social media sentiment along with website traffic to see how their campaign is faring.
• Instead of requesting a report from other departments, executives can ask their augmented analytics platform about sales trends, conversion rates, and campaign performance.
• Data analysts can leverage augmented analytics features to reduce the iteration loop in their data analysis activities, automating time-consuming tasks like data prep, model building, and report generation.

One of our clients, a wealth management firm, spent months exploring the reason behind quarterly customer churn. Our developers built a custom augmented analytics solution that allows the VP of wealth management to peer into the trends on their own and give the data analytics team more time to focus on higher-value initiatives like strategic recommendations. The time-to-insight reduced from months to days, and data preparation time decreased from weeks to minutes.

Augmented intelligence — augmented challenges?

Although augmented analytics ushers in a new era of accessible data insights, it brings in new challenges, too. Some of the challenges are conditional and purely mechanical, while others occur due to the technology’s ongoing evolution and refinement.

Maintaining data quality

The accuracy and strategic potential of your augmented analytics solution hinges on the accuracy and reliability of the underlying data. But the sheer scale and variety of such data requires a significant manual effort from companies to cleanse, tag, and enrich it.

Although the lion’s share of tagging and cleansing is automated, data teams still need to actively participate in the process — like developing tagging and cleansing strategies to deal with the subjective nature of data such as customer sentiment or product reviews. 

Privacy and bias concerns

As data analytics transitions from fact-finding to conversation, companies enter the grey zone of data ethics. More underlying data means more potential biases, higher risks of data inference and profiling, as well as expanded vulnerability surface.

Augmented analytics companies should take a multi-faceted approach to address the ethics risks that includes robust data governance frameworks, bias detection and mitigation strategies, and human-in-the-loop oversight. Adversarial conversational AI solutions can also improve the robustness of augmented analytics solutions against malicious attacks. 

Expensive conversion processes 

Although companies get an AI data-savvy assistant, this capability comes with a hefty subscription fee. Cloud infrastructure, increasing networking costs, and the dedicated effort required to enable augmented analytics capabilities, although not overweighing the ROI, calls for substantial money injections. So does the ongoing need for LLM recalibrating, model retraining, and system updates. 

Good news is that companies can at least bring down the cost of solution development and model retraining. For example, augmented analytics vendors can build off pre-trained models and leverage AutoML tools to reduce development time and costs. MLOps tools like AWS SageMaker, Azure Machine Learning, or Google Cloud AI Platform can automate the entire model retraining workflow.

Non-determinism of the model

Less of a challenge, more of a consideration, the non-determinism of an augmented analytics solution occurs when the same input leads to different results each time. It doesn’t inherently cause less accurate insights, but may jeopardize the credibility of the solution and difficulties in reproducibility. 

When non-determinism is not intentional, it could signal a bug or default in the system. In other cases, varying results crop up due to contextual changes, real-time updates, or stochastic algorithms at the solution’s core.

New roles and capabilities

Sometimes, augmented analytics projects go off track simply because companies fail to build up their own capabilities or repurpose necessary skills. Whether it’s a lack of AI and machine learning capabilities, solid data infrastructure, or even the meager organizational capacity, the result is often the same: companies abandon the initiative without unlocking “alpha”.

Solutions with this level of promise require companies both to supplement existing AI and data roles with skills like DataOps, vector database development, and others — and acquire new talents such as AI ethics stewards, prompt engineers, or unstructured data specialists. 

How to put your best foot forward with augmented analytics

Whether you’re in for quick wins or want to lay a foundation for broader-scale transformation, augmented analytics necessitates a strategic roadmap.

Customizing models

To make sure you can bet your bottom dollar on your augmented solution, you need to train the underlying LLM or SLM on your data grounds and fine-tune it through prompt engineering. Keep in mind that however advanced, this analytics type is only as good as the data it’s fed. Ensure the foundational data is clean, consistent, accurate, and reflects the up-to-date business context.

Establishing data governance and compliance strategies

Augmented analytics tools can benefit any industry, even compliance-heavy ones like finance and healthcare, provided data governance considerations have been properly addressed. Role-based access control, comprehensive data security strategy, metadata management, and a clear data lineage framework are crucial for building trust in the insights generated.

Since many regulations push for transparency in AI systems, organizations also need to set up XAI techniques to make the model’s decision-making process more understandable and traceable.

Treating augmented analytics as an engineering discipline 

If you value long-term success, treat augmented analytics as an engineering discipline, not just a business project with defined budgets and timelines. Focus on a robust and easily scalable foundation with modular components that can be reused for other use cases and support the multitude of business needs over the long term.

Embracing a culture of continuous improvement will enable your team to enhance the solution over time and easily calibrate it to your evolving business needs.

Promoting AI-enabled, but data-literate culture

Although augmented analytics bestows users with unmatched self service capabilities, the final decision is on humans. Keeping the potential non-determinism of the augmented analytics technology in mind, it’s important to instill a data-literate culture where your teams can challenge AI insights instead of just going with them. 

This level of discernment can only stem from an AI-ready organizational culture, supportive of change management — including training and upskilling, stakeholder engagement, and augmented analytics advocacy team to navigate the human side of the innovation. 

AI is the new BI

Regardless of economic headwinds, companies that can swiftly translate data into insights will not just survive, they will tower above others. This makes augmented analytics technology by far one of the most salient capabilities for high performers. 

Despite its imminent benefits, augmented analytics is more than just flipping a switch. The technology itself is only part of the equation. The real challenge lies in beefing up the necessary technical skills and updating the organizational AI governance roadmap. 

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