As the volume and variety of data continue to grow, so does the potential for users to leverage this data in new and exciting ways.
Doing so, however, requires businesses to break free from the BI paradigms of the past. Forget the read-only views of siloed, static dashboards. The future lies in embracing augmented analytics—new-age, AI-powered solutions that empower everyone to make data-driven decisions.
In fact, recent research by McKinsey shows companies that leverage next-gen AI-powered analytics are seeing a 20% increase in earnings. Even better, GenAI in data and analytics is helping IT leaders reduce overhead and speed up time to insight. Sound too good to be true? Think again. With the right augmented analytics software, you can turn these possibilities into reality.
Table of contents:
Augmented analytics is a type of analytics that uses Artificial Intelligence (AI) to assist or automatically prepare data, analyze it, identify trends and patterns, and visualize these for end users. This makes it easier for business users, most of whom are not analytics engineers, data analysts, or data scientists, to make better decisions based on data.
Augmented analytics is different from traditional analytics in that it does not require users to have significant experience working with data. This makes it ideal for businesses who want to bring self-service analytics to their organizations and empower business users to conduct more analysis on their own, without requiring extensive training or technical proficiency.
1. Improved decision making
Augmented analytics make it possible for all kinds of users to easily ask data questions, identify patterns and correlations, and share these insights with others. This technique uses machine learning, and deep learning to help users comb through vast datasets and find insights that were otherwise difficult to obtain or missed entirely. When combined with natural language processing (NLP), augmented analytics can help users ask questions and discover AI-powered insights that spell out what’s changed. As a result, businesses that use augmented analytics gain a competitive edge by making better-informed decisions.
Augmented analytics solutions like ThoughtSpot empower all users—from your marketing team to customer support champions—to search and understand their data using natural language. You can simply type in your question and gain interactive data visualizations and AI-powered insights within seconds. Additionally, with powerful AI features like ‘AI Highlights’, you can easily discover trends, patterns, and anomalies, allowing you to get a real-time view of what’s happening in your business.
2. Increased efficiency
As Cristian Felix, VP of product at Accern, put it, “Data and AI are not here to replace humans but to augment and support them to make better decisions.”
Data teams often find themselves swamped by operational tasks like manually preparing data, maintaining dashboards, performing complex analysis, and handling a constant flow of ad-hoc requests. This leaves little time for what truly adds value: uncovering insights and exploring possibilities. Augmented analytics transforms this scenario by automating a wide range of tasks, from data preparation to running statistical analysis. With these advanced analytics tools at your fingertips, you can focus on high-impact analysis, driving efficiency and proactive decision-making across the organization.
A great example of this is ThoughtSpot’s SpotIQ. With a single click, teams can forecast trends and perform iterative change analysis to understand not only what has changed, but also why it has changed. You can also set anomaly-based alerts to instantly take action whenever there is an unusual change in key performance indicators (KPIs). This ability to perform one-click analysis and monitor KPIs with greater precision ensures that your business always stays ahead of the curve.
See how ThoughtSpot simplifies the process of uncovering the root causes of changes in just a few clicks:
3. Contextual insights
Picture this: You’re a supplier in the electronics industry and start noticing a sudden spike in orders for mobile components and demand panels. At first, you might attribute this increase to typical seasonal trends. However, when you begin inspecting your sales order data alongside recent tech release announcements, it becomes evident that this surge is directly tied to the upcoming iPhone launch.
This insight helps you understand the key factors driving demand for your products. With this knowledge, you can strategically plan your inventory, allocate resources effectively, and adjust your production capacity to meet these spikes. Additionally, being aware of future product launches enables you to negotiate better contract terms.
By leveraging augmented analytics, users can tap into insights from their entire data landscape—not just the siloed dashboards they look to on a regular basis. And once you are no longer lost in a sea of disparate dashboards, where half of your data fails to see the light of day, you can create engaging data stories that tell the full picture of your organization. This holistic view empowers you to identify trends, uncover hidden opportunities, and gain contextual insights that drive your business forward.
4. Better understanding of customer behavior
Augmented analytics can help businesses better understand their customers’ behavior by providing a robust, 360-degree view of customer data. With real-time visualizations and advanced analytics, you can take a closer look at customer patterns and trends, identifying opportunities to create personalized journeys that drive loyalty.
This was the case for Wellthy, a company that streamlines the caregiving process. Previously, their care team struggled to proactively identify insights that would help them reduce customer churn. Whenever a team member spotted a data point of concern, they had to reach out to their data team and wait days for the results.
With ThoughtSpot AI-Powered Analytics, Wellthy’s care team could visualize real-time data, filter and drill into specific searches, and pin charts to custom Liveboards. Doing so, Wellthy’s Care Coordinators could offer tailored experiences and personalized support to their customers, increasing loyalty.
Watch this segment from Beyond 2023 to learn how Wellthy is driving customer outcomes with ThoughtSpot.
5. Improved predictions
Not only does augmented analytics help companies understand what is happening today, but it can also illuminate what is likely going to happen next. By using AI to identify patterns in data or incorporating machine learning models, augmented analytics can provide more accurate predictions than traditional methods. In addition, augmented analytics can automate the process of making predictions, making it faster and easier to generate reliable results. As a result, augmented analytics has the potential to revolutionize the field of data analytics and improve predictions across a wide range of applications.
For instance, ThoughtSpot’s augmented analytics engine SpotIQ automatically analyzes large datasets and forecasts trends, helping you explore possibilities to drive outcomes.
1. Data quality
In order for businesses to benefit from augmented analytics, they must have high-quality data. This can be a challenge for businesses that do not have extensive experience working with data. Data quality is essential for augmented analytics because it ensures that the AI and ML algorithms have accurate information to work with. Poor data quality can lead to inaccurate results and suboptimal decision-making, which in turn can hinder user trust. To ensure high-quality data, businesses should adopt the right data governance framework to ensure their data is organized and ready for analysis. Fortunately, many tools in the modern data stack work hand in hand to address data quality concerns. With clean and well-organized data, businesses can reap the benefits of augmented analytics.
2. Complexity
Augmented analytics can be complex, so users need a platform with a familiar interface to drive real adoption. Augmented analytics can be a great asset for any organization, but it's important to understand that if your users cannot use your tools, it doesn’t matter how robust or complex analysis it can handle.
3. Data literacy
In order to get the most out of an augmented analytics tool, users will need to understand the basics of data. This doesn’t mean that users should become technical experts. Instead, they will need to understand the fundamentals of data. By pairing an augmented intelligence platform with data literacy programs, users will be able to get the most out of their investment and reap the benefits of this rapidly growing field.
Augmented analytics infuses AI technologies at every stage of the data analytics workflow—from integration and preparation to discovery and insights generation. By automating complex processes, augmented analytics empowers business users to engage more with their data, while allowing data teams to focus on higher-level strategic initiatives. Here are some common components of augmented analytics:
Streamlined data integration: Manually combining data from various sources can be complicated, but augmented analytics simplifies this task through automated data integration. These AI-powered tools can intelligently map, merge, and harmonize data from disparate systems, creating an easily accessible view of your operations.
Automated data preparation: Unlike traditional data preparation methods, which often depend on manual cleaning and processing, Augmented analytics introduces a layer of smart systems that automatically generate synonyms for all data columns and identify inconsistencies, giving time back to data professionals.
Automated data discovery: By leveraging machine learning and advanced statistical methods, augmented analytics tools can swiftly analyze large datasets to automatically surface patterns, trends, and anomalies in data, revealing insights users might otherwise miss.
Automated visualizations: Creating visualizations is intuitive with augmented analytics tools. There’s no need for SQL knowledge or coding, these solutions automatically generate relevant charts, graphs, and other visual representations of data, making it easier for business users to interpret and present findings.
Predictive analytics: Augmented analytics uses historical data and advanced machine learning techniques to forecast trends, recognize patterns, and make predictions with minimal human intervention. These advanced techniques can also help you analyze how changes in one factor can influence another factor’s outcome.
Prescriptive analytics: By deploying AI models and advanced computational methods, augmented analytics can suggest relevant insights, data points, or next steps based on the user's queries. These actionable outcomes can help you decide the best course of action for your business goal.
Natural Language Query (NLQ): This capability enables users to pose business questions in simple, everyday language rather than complex code. GenAI-powered interfaces make data exploration accessible to everyone, allowing users to easily extract valuable insights without needing technical expertise.
1. Understand user pain points
While AI is on everyone’s radar, not every AI-powered solution is a perfect fit for your business. Before investing, evaluating the solution’s technical fit and overall feasibility is crucial. Conduct an audit to assess how each department and key stakeholders leverage data, noting their unique challenges. Based on your evaluation, define clear goals and identify the specific functionalities needed to address those challenges. This targeted approach will help you select solutions that align with your organizational objectives and deliver measurable results.
2. Encourage training
Change can be hard for organizations, especially when introducing new technologies and processes. That’s because everyone already has a way of doing things and most don’t have the time or energy to learn something completely new. To facilitate a data-driven culture, it’s essential to invest in comprehensive training programs for your team. One that will help users explore the functionalities of their augmented analytics tools in detail. Apart from training, you can also create manuals and other learning resources so users can feel more confident and empowered to explore the platform.
3. Always keep humans in the loop
To gain accurate and up-to-date answers from your AI models, it’s important to strike the right balance between being technology-enabled and human-centric. Having a human-in-the-loop feedback system, as ThoughtSpot has, can prevent hallucinations and biases from hampering decision-making. With human intuition and perspective, you can ensure that insights generated by AI are contextually relevant and actionable and could help users drive company performance.
With all of these benefits, it’s no wonder that augmented analytics is on the rise. If you’re looking for an AI-Powered Analytics solution to help you make better data-driven decisions, be sure to check out ThoughtSpot. Our intuitive search and AI-powered analytics can help you get the most out of your data with little effort. Sign up for a free trial today and see what live analytics can do for you.
