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Augmented analytics:
an end-to-end overview & best practices
April 25, 2025
Augmented analytics involves the implementation of artificial intelligence and related technologies into the traditional data analytics workflow to automate data collection, processing, and querying, minimizing manual effort and simplifying insight generation for non-technical business users.
With 15+ years of experience in data analytics services, Itransition helps businesses implement augmented analytics capabilities to streamline analytics workflows and support data democratization.
Augmented analytics market overview
19.87%
the forecasted CAGR of the global augmented analytics market for 2025–2033
Research and Markets
x5.2
the expected increase in the global augmented analytics market value from 2024 to 2033
Research and Markets
50%
of organizations use AI-driven tools for automated insight generation
Gartner
Key capabilities of augmented analytics
Augmented analytics encompasses several underlying technologies that accelerate and democratize data analysis across organizations.
Machine learning
A critical technology behind augmented analytics solutions, machine learning enables the processing of raw data and continuous learning from new data as it is analyzed. ML algorithms identify data relationships and patterns and facilitate predictive and prescriptive analytics to forecast market trends and make targeted recommendations for decision-makers.
Natural language processing (NLP)
Natural language querying (NLQ) and natural language generation (NLG) facilitate communication between humans and computers by translating computer code into human language and vice versa. These technologies enable business users to query data and interpret results using familiar terms rather than specialized programming languages like SQL or Python, streamlining data analysis and insights sharing.
Automation
Augmented analytics software automates routine tasks across the data analytics lifecycle, from real-time data collection and data preparation to data exploration and data visualization. ML-driven technologies help automatically detect relevant data attributes, index and group related information, and create user-friendly reports and graphs.
Derive actionable insights from complex data more efficiently with augmented analytics
Augmented analytics use cases
Augmented analytics has various applications, helping both top executives and team members across different departments, including finance, sales, marketing, logistics, and human resources, make informed business decisions.
Strategic decision-making
C-suite executives can use augmented analytics software to analyze business data, such as financial, marketing, and sales performance metrics, without the reliance on IT teams. Augmented analytics software can also help identify emerging market trends and customer needs and assess the potential impact of each decision at all corporate levels, enabling the generation of immersive reports with drill-down and drill-through capability.
Customer intelligence
Marketing teams employ augmented analytics tools to collect and process customer data from scattered data sources, bypassing manual processes. Augmented analytics software helps create 360-degree customer profiles, understand customer behavior, forecast customer churn, calculate customer lifetime value, and identify cross- and upselling opportunities.
Sales forecasting
With the help of augmented analytics’ automation capabilities, sales professionals can process large data sets to create a more comprehensive view of the company’s sales activities, current market conditions, closed deals, and team performance. Powered by ML and generative AI, augmented analytics software also helps forecast a company's revenue, all while providing natural language communication and self-service BI and analytics capabilities.
Supply chain management
Businesses employ augmented analytics capabilities to assess supply chain performance in an automated, self-service manner. Augmented analytics tools help oversee such aspects as inventory turnover, supplier delivery rates, supply expenses, and inventory shortfalls. With such insights at their fingertips, supply chain managers can predict disruptions and supply demands, understand why certain locations have lower delivery rates, and take action to improve supply chain resilience.
Talent management
Augmented analytics solutions automate the analysis of performance reviews, internal communication, and resumes and the generation of HR analytics reports in natural language. They help HR teams derive valuable insights for talent acquisition, development, and retention, providing information about top performers, candidates who better suit a role, employees at risk of leaving, and skill gaps that hinder the company’s goals.
Credit risk assessment
Augmented analytics software eliminates the need to manually slice and dice data for loan approval. It provides financial teams with predictions on customer creditworthiness, analyzing all relevant data points, including age, income, and credit score, assessing the risk, and making suggestions regarding loan amounts.
Fraud detection
ML algorithms automate the analysis of system access, user activity, and transaction data, identify unusual activities and outliers, and flag potential anomalies in datasets. Augmented analytics systems generate alerts in natural language, making it easier for users to detect manipulated figures, forgeries, and falsified signatures and prevent fraud and financial loss.
IT operations optimization
Augmented analytics software helps monitor system and server performance to determine the root causes of unexpected spikes in server and system usage. Using ML capabilities, augmented analytics software can also predict outages for IT specialists to mitigate them and prevent system downtime.
1.5x
faster image processing
An ML solution for brand analytics & reporting
Itransition developed a new machine learning-based brand recognition solution for a sports and entertainment sponsorship valuation company. The solution helps users identify brands and their logo locations in images, generating customizable reports and features a renovated interface with simple navigation capabilities.
15%
model accuracy growth
Custom ML algorithms for an insurance platform
Itransition’s team assisted an insurance company with overcoming its AI prediction model’s scalability and accuracy limitations. We analyzed the existing machine learning model, engineered 100+ new features, and trained the model using 500,000 insurance application records. The implemented solution predicts the likelihood of app users purchasing the insurance policy with 75% accuracy and allows the client to offer targeted user prices and discounts.
15%
increase in collected user data volume
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Itransition provided an ecommerce company with a retail-specific BI platform, as well as developed an AI-powered recommendation engine and a computer vision solution, enabling the client to improve online user behavior understanding, enhance the customer experience with AI-powered personalization, and boost sales.
Benefits of augmented analytics
Faster time to insight
ML algorithms and automation capabilities within augmented analytics software significantly accelerate the process of deriving insights and making business decisions. They help quickly find relationships in datasets, profile and transform data for analysis, generate comprehensive, real-time insights in natural language and interactive visuals, and recommend the best course of action for users.
Democratization of data
Augmented analytics software is equipped with features like natural language processing, ML-powered recommendation engines, and no-code and low-code interfaces that allow users to build queries, reports, and dashboards without writing SQL or scripts. It also automatically generates interactive visuals like charts, graphs, and maps based on the provided data. Such capabilities improve data accessibility for users without advanced technical skills, allowing them to perform data analytics using simple commands and making it easier to exchange the insights with other teams and leadership. More teams become comfortable working with data, improve their data literacy, and perform analysis for their specific use cases.
Operational efficiency
By automating time-consuming data analytics tasks and empowering business teams to work with data without the reliance on IT teams, augmented analytics tools boost the productivity of both regular users and data science teams. Business users can make decisions at the speed of business, while data scientists can focus on advanced analytics projects rather than repetitive, manual data management activities or routine data analysis.
Uncovering hidden insights
Machine learning algorithms powering augmented analytics help users process huge data volumes and surface insights that can be overlooked when using conventional analysis methods. As a result, businesses can factor in every detail during their decision-making process and eliminate errors caused by incorrect interpretation of critical data.
Want to make the most of your augmented analytics platform?
Augmented analytics implementation challenges
Challenge | Solution | |
|---|---|---|
Employee resistance |
Employees can be hesitant to use new technologies due to various reasons, from fearing that AI will replace
them at work to lacking the skills and confidence for using advanced tools.
| For individuals to embrace augmented analytics, they must understand its value and develop data literacy and analytics proficiency. Provide tailored user training, hold workshops, and assign mentors to help employees comprehend key data concepts and how to convert data into informed decisions that benefit the business. Nurture a data-driven culture and demonstrate by example how augmented analytics benefited other team members. |
Data bias & poor data quality |
Augmented analytics results become biased and faulty if the system is fed with irrelevant, contaminated, and
insufficient data.
| Investing enough resources in enterprise-wide data management and data governance practices helps guarantee good data quality. Such practices can involve assigning data stewards responsible for maintaining the quality of an organization’s data or establishing a cross-functional data governance committee to define, encourage, and oversee the implementation of data governance initiatives. Regularly conduct data quality and bias audits using data quality monitoring tools and track data lineage to ensure data accuracy and enhance trust in decision-making. |
Privacy & security concerns |
As augmented data systems process large amounts of sensitive data, it can evoke concerns about data privacy
and security.
| When choosing augmented analytics tools, consider their built-in security mechanisms, such as role-based access control, at-rest and in-transit data encryption, and data masking. Make sure that the augmented analytics solution you implement complies with applicable industry data regulations and international data protection legislation, including GDPR, HIPAA, FISMA, and more. |
Scalability & performance issues |
Augmented analytics requires processing large amounts of data, which can affect system response time.
| Ensure that your data infrastructure (including storage and computing resources as well as servers and telecommunication networks, if you opt for on-premises deployments) can support the growing amount of data and workloads. For on-premises deployment, consider implementing high-performance SSD storage, GPUs, and high-core CPUs. To ensure smooth growth according to your current needs, consider transitioning to cloud solutions that offer auto-scaling capabilities. |
Best practices for successful augmented analytics adoption
Consider iterative implementation
To avoid overwhelming employees, implement your augmented analytics solution gradually, either in a certain department or for a particular use case. Make the necessary changes on a smaller scale, e.g., retrain the models, adjust your data governance policies, develop comprehensive user training programs, or implement the right data integration tool and repeat, moving on to a company-wide scale.
Ensure transparency into system logic
The inability to interpret system results is one of the reasons for mistrust in augmented analytics tools. To avoid the “black box” problem, choose augmented analytics systems that leverage explainable AI techniques, such as visual representations of feature contribution to the result, feature importance estimation, and Local Interpretable Model-Agnostic Explanations (LIME). Incorporating XAI techniques promotes user accountability and trust by assisting employees in comprehending the outcomes produced by the augmented analytics tool.
Incorporate human-in-the-loop feedback
To maintain system accuracy and fairness, human oversight, judgment, and validation of the results are essential. Design a structured review process, defining where human involvement is needed, such as in high-risk decisions like fraud detection, medical diagnosis, and misinformation control. Ensure close collaboration between business users and data scientists to provide nuanced domain knowledge and implement the suggestions. Regularly retrain AI models based on the feedback and provide guidelines to employees on recognizing biases and inaccuracies.
Augmented analytics services we provide
At Itransition, we bring our hands-on data analytics expertise to help companies deploy augmented analytics capabilities, from NLP to automation, and streamline data analytics workflows.
Consulting
We ensure augmented analytics project success within your company, providing advisory support throughout all project stages. We help design solution architecture, assess existing augmented analytics tools, choose the best-suited technological stack for the solution components, from data storage to data visualization, and establish data management policies and practices.
Implementation
Itransition’s team develops custom augmented analytics solutions as well as delivers platform-based ones to suit specific business needs. We implement full-scale solutions as well as separate analytics modules, ensuring their seamless integration with the company’s technical ecosystem. We also take care of the system’s compliance with applicable regulations, implement security mechanisms, and monitor system performance post-launch.
Support
Our support services are aimed at delivering a trouble-free analytics experience to the solution’s end users. We offer both basic and advanced support for augmented analytics solutions, resolving issues and optimizing the system’s usability and query performance to help you avoid costly disruptions and downtimes. Our experts also provide targeted training for effective augmented analytics utilization and offer on-demand support services like system modernization and migration.
Turn data into a competitive edge with Itransition’s guidance
FAQs
Augmented business intelligence involves the integration of artificial intelligence technologies, including machine learning and natural language processing (NLP), into traditional BI tools to enable complex analytics and automate data analytics processes, such as data preparation and data discovery, allowing users to derive meaningful insights from big data volumes.
As part of an analytics solution, AI and ML algorithms analyze large datasets, automate manual tasks like data preparation and data visualization, learn from data, identify patterns and correlations, provide suggestions, make predictions, and support natural language queries to communicate with users.
Automation, a key component of augmented analytics, is the process of using technology to carry out repetitive tasks, from data prep to insight generation, so that humans can focus on their core activities. In contrast, augmented analytics is a broader concept that involves the use of AI and ML algorithms to enhance human intelligence and assist users in finding deeper insights.
Augmented analytics employs predictive analytics to forecast the probability of something happening or the impact of something based on the analysis of historical data. Unlike augmented analytics, predictive analytics as a separate capability doesn’t necessarily involve automated data preparation and visualization and natural language processing.
With self-service analytics, non-technical users can work with data, explore visualizations and dashboards, and ask the system follow-up questions with minimized reliance on IT or data teams. Augmented analytics goes beyond self-service capabilities, combining them with ML-powered recommendations and enabling business users to perform advanced analysis.
Augmented analytics benefits users at every level of the organization, from executives to operational staff across various business functions. It also provides significant value to people with varying levels of data literacy, from non-technical users to professional data analysts and advanced business users who work with complex datasets. Augmented analytics can find its application across diverse industries, including healthcare, manufacturing, and financial services, allowing users to streamline data analysis processes and make data-driven decision-making more accessible.
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