Augmented Analytics in BI

Augmented Analytics and its Role in Business Intelligence

Have you ever wondered how businesses navigate the vast sea of data in today’s digital age, where every click, purchase, and interaction generates valuable insights? In this blog, we we will discuss the transformative power of augmented analytics and its role in revolutionizing traditional Business Intelligence (BI). Join us as we explore how augmented analytics leverages cutting-edge technologies to unlock the full potential of data, empowering decision-makers with actionable insights in real-time.

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The Emergence of Augmented Analytics

Augmented Analytics merges the latest advancements in artificial intelligence with data storytelling and automation to deliver actionable intelligence to decision-makers promptly. Coined by Gartner in 2017, Augmented Analytics automates data management, processing, and insight delivery, as defined by the research firm. Forrester categorizes these solutions as “Augmented BI Platforms,” enabling last-mile intelligence for business professionals and democratizing business insights.

Key Components of Augmented Analytics

Augmented analytics integrates various components to enhance data processing, insight generation, and decision-making capabilities. Let’s explore each of these components in detail:

1. Machine Learning and Artificial Intelligence

Machine learning (ML) and artificial intelligence (AI) form the backbone of augmented analytics. These technologies enable systems to learn from data patterns, identify trends, and make predictions without being explicitly programmed. In the context of augmented analytics, ML and AI algorithms analyze vast datasets, detect anomalies, and uncover hidden insights that may not be apparent through traditional analysis methods. By continuously refining their models based on new data inputs, ML and AI algorithms enhance the accuracy and relevance of analytical insights over time.

2. Natural Language Generation

Natural Language Generation (NLG) is a crucial component of augmented analytics that facilitates the automatic generation of human-readable narratives from data. NLG algorithms transform complex data sets, statistical analyses, and visualizations into coherent and understandable language. By providing users with plain-language descriptions of insights, NLG enhances communication and comprehension, making it easier for decision-makers to interpret and act upon analytical findings. This component bridges the gap between data analysis and decision-making by presenting insights in a format that is accessible to non-technical users.

3. Automation

Automation plays a pivotal role in augmented analytics by streamlining repetitive tasks, accelerating data processing, and enhancing overall efficiency. Automated processes, such as data preparation, cleansing, and modeling, enable organizations to rapidly transform raw data into actionable insights. Additionally, automated insight generation and delivery mechanisms ensure that decision-makers receive timely and relevant information without manual intervention. By automating routine analytical workflows, organizations can focus their resources on higher-value activities, such as strategic analysis and decision-making.

4. Data Visualization and Exploration

Data visualization and exploration tools enable users to interactively explore and interpret data through visual representations such as charts, graphs, and dashboards. These tools facilitate intuitive data discovery, pattern recognition, and hypothesis testing, empowering users to gain deeper insights into their data. In the context of augmented analytics, advanced visualization techniques enhance the presentation of analytical findings and facilitate interactive exploration of complex data sets. By visualizing data in meaningful ways, organizations can uncover trends, correlations, and outliers that may inform strategic decisions and drive business outcomes.

Advantages of Augmented Analytics

Augmented analytics shifts advanced analytical power to information consumers, offering capabilities previously limited to analysts and citizen data scientists. The primary benefits include:

  1. Unlocking Data Potential: Algorithms perform data analysis, providing contextual suggestions and uncovering granular insights beyond simple queries.
  2. Fostering Trust: Machine learning technologies personalize recommendations based on user interactions, enhancing trust in business intelligence and data.
  3. Improving Data Literacy: Data-centric businesses benefit from improved decision-making across all organizational levels through enhanced data literacy and self-service analytics.

Transforming Business Intelligence with Augmented Analytics

Augmented Analytics represents a paradigm shift in the realm of Business Intelligence (BI), elevating data consumption and decision-making capabilities to new heights. Let’s explore how Augmented BI revolutionizes traditional BI methods:

1. Integration of AI and ML with Data Analytics

Augmented BI integrates advanced technologies such as Artificial Intelligence (AI) and Machine Learning (ML) with data analytics, amplifying the value extracted from vast data repositories. AI and ML algorithms analyze complex datasets, detect patterns, and generate predictive insights that go beyond the capabilities of traditional BI tools. By leveraging these technologies, organizations can uncover hidden correlations, forecast future trends, and make data-driven decisions with unprecedented accuracy and efficiency.

2. Democratization of Data through Augmented Analytics Tools

Augmented Analytics tools democratize access to data and insights, reducing reliance on technical expertise and empowering a broader spectrum of users. Unlike traditional BI solutions that require specialized skills to navigate complex data models and queries, Augmented Analytics platforms offer intuitive interfaces and self-service capabilities. Business users can explore data, generate insights, and derive actionable recommendations independently, fostering a culture of data-driven decision-making across the organization.

3. Interactive, Visual-Based Analytics with Automated Insight Discovery

Augmented BI enhances data consumption through interactive, visual-based analytics combined with automated insight discovery mechanisms. Visualizations such as charts, graphs, and dashboards provide intuitive representations of complex data sets, enabling users to explore and interpret information effortlessly. Automated insight discovery algorithms proactively identify patterns, anomalies, and trends within the data, presenting users with actionable insights in real-time. This combination of visual-based analytics and automated insight discovery streamlines decision-making processes and enhances the accessibility of insights for stakeholders at all levels.

4. Real-Time Updates and Relevant Insights for Expedited Decision-Making

Augmented Analytics platforms deliver real-time updates and relevant insights to decision-makers, enabling rapid and informed decision-making. By leveraging AI-driven algorithms, these platforms continuously monitor data streams, detect changes, and generate insights instantaneously. Decision-makers receive timely notifications, alerts, and personalized recommendations based on evolving data patterns, enabling them to respond swiftly to changing market dynamics and emerging opportunities. This real-time intelligence empowers organizations to stay agile, competitive, and proactive in today’s fast-paced business environment.

5. Reduction of Time-Consuming Report Creation with Machine Learning Algorithms

Augmented BI reduces the time and effort required for report creation and optimization through the use of machine learning algorithms. Traditional BI processes often involve manual data extraction, analysis, and report generation, which can be time-consuming and resource-intensive. Augmented Analytics platforms automate these tasks, leveraging ML algorithms to generate personalized reports, dashboards, and visualizations tailored to the specific needs of users. By automating routine reporting workflows, organizations can free up valuable resources, accelerate decision-making cycles, and focus on strategic initiatives that drive business growth.

Essential Features of Augmented Analytics Products

Selecting the right augmented analytics product is crucial for organizations seeking to unlock the full potential of their data. Here are the essential features to consider when evaluating augmented analytics solutions:

1. Augmented Data Preparation

Augmented data preparation capabilities are fundamental for streamlining the process of cleansing, transforming, and enriching raw data. Advanced data preparation tools leverage AI and ML algorithms to automate tedious tasks such as data profiling, normalization, and outlier detection. By intelligently guiding users through the data preparation process and suggesting transformations, augmented analytics platforms accelerate data preparation workflows and ensure that data is clean, accurate, and ready for analysis.

2. Cloud Analytics Support

Cloud analytics support is essential for organizations seeking to leverage the scalability, flexibility, and cost-effectiveness of cloud-based infrastructure. Augmented analytics products that offer native integration with leading cloud platforms such as Amazon Web Services (AWS), Microsoft Azure, and Google Cloud enable seamless deployment, management, and scaling of analytics solutions in the cloud. Cloud-based analytics also facilitate collaboration, data sharing, and accessibility across geographically dispersed teams, empowering organizations to harness the full power of their data assets.

3. Data Source Connectivity

Data source connectivity is critical for accessing and integrating data from disparate sources, including databases, data warehouses, cloud storage, and third-party applications. Augmented analytics platforms that support a wide range of data connectors, APIs, and protocols enable organizations to ingest and analyze data from heterogeneous sources without friction. Robust data integration capabilities ensure that users have access to comprehensive, up-to-date data sets for analysis, reporting, and decision-making.

4. Automated Insights Generation

Automated insights generation capabilities enable augmented analytics platforms to proactively identify trends, patterns, and anomalies within large and complex data sets. By leveraging AI-driven algorithms, these platforms analyze data in real-time, detect meaningful insights, and generate actionable recommendations without manual intervention. Automated insights empower users to make informed decisions quickly, capitalize on emerging opportunities, and mitigate risks proactively, enhancing organizational agility and competitiveness.

5. Dynamic Data Visualization

Dynamic data visualization features enhance the presentation and interpretation of analytical insights through interactive charts, graphs, and dashboards. Augmented analytics platforms that offer rich visualization capabilities enable users to explore and visualize data in intuitive and insightful ways. Interactive visualizations facilitate data exploration, hypothesis testing, and scenario analysis, empowering users to uncover hidden insights and derive actionable conclusions from complex data sets. Dynamic data visualization also enhances communication, collaboration, and decision-making across organizational stakeholders.

6. Data Storytelling Capabilities

Data storytelling capabilities enable augmented analytics platforms to transform raw data into compelling narratives that communicate key insights, trends, and findings to diverse audiences. By combining interactive visualizations with narrative techniques, these platforms create engaging and persuasive data stories that resonate with stakeholders and drive action. Data storytelling facilitates effective communication, knowledge sharing, and decision-making by contextualizing data within broader business contexts and illustrating its implications for strategic initiatives and operational performance.

7. Comprehensive Data Catalog

A Comprehensive Data Catalog provides a centralized repository of all available data assets within an organization. It offers metadata management, data lineage tracking, and data discovery capabilities, allowing users to easily locate and understand relevant data sources. With a robust data catalog, organizations can improve data governance, promote data transparency, and facilitate collaboration across teams by providing a unified view of enterprise data assets.

8. Natural Language Query (NLQ)

Natural Language Query (NLQ) enables users to interact with data using natural language commands and queries. NLQ capabilities leverage natural language processing (NLP) algorithms to interpret user queries, understand intent, and retrieve relevant data insights. By eliminating the need for complex query languages or technical expertise, NLQ empowers non-technical users to access and analyze data effectively, democratizing access to analytical insights across the organization.

9. Natural Language Generation (NLG)

Natural Language Generation (NLG) automatically generates human-readable narratives and summaries from data insights and analysis results. NLG capabilities transform statistical data, visualization outputs, and analytical findings into coherent and understandable language, facilitating communication and decision-making. By providing plain-language descriptions of complex data insights, NLG enhances data literacy, fosters collaboration, and enables stakeholders to derive actionable insights from data analysis reports.

10. Reporting Abilities

Reporting Abilities encompass a range of features that enable users to create, customize, and distribute reports and dashboards based on analytical insights. Augmented analytics platforms should offer flexible reporting tools, including interactive dashboards, ad-hoc reporting, and scheduled report delivery capabilities. Reporting features should support a variety of data visualization formats, advanced charting options, and dynamic filtering functionalities to meet diverse reporting needs across business functions and user roles.

11. Security Capabilities

Security Capabilities are critical for safeguarding sensitive data and ensuring compliance with regulatory requirements. Augmented analytics products should implement robust security features, including data encryption, access controls, role-based permissions, and audit trails. Security measures should be integrated across all layers of the analytics stack, from data ingestion and processing to report generation and distribution, to mitigate the risk of unauthorized access, data breaches, and information security threats.

12. Manageability for Usage Tracking and Information Sharing

Manageability features enable administrators to monitor usage metrics, track performance, and manage access permissions within the augmented analytics platform. Usage tracking capabilities provide insights into user behavior, data consumption patterns, and system performance, enabling organizations to optimize resource allocation and improve user experience. Information sharing functionalities facilitate collaboration, knowledge sharing, and decision-making by enabling users to share reports, insights, and dashboards with colleagues and stakeholders securely.


In conclusion, augmented analytics represents a transformative approach to data analysis and decision-making, revolutionizing traditional business intelligence practices. By integrating advanced technologies such as artificial intelligence, machine learning, and natural language processing, augmented analytics empowers organizations to extract maximum value from their data assets, democratize access to insights, and drive innovation and growth.

Augmented analytics platforms offer a comprehensive suite of features, including augmented data preparation, cloud analytics support, automated insights generation, dynamic data visualization, and data storytelling capabilities. These features enable organizations to streamline data analysis workflows, enhance data-driven decision-making, and derive actionable insights from complex data sets with unprecedented speed and accuracy.

Moreover, augmented analytics products prioritize security, compliance, and manageability, ensuring that organizations can safeguard sensitive data, track usage metrics, and facilitate information sharing securely. By selecting the right augmented analytics solution, organizations can unlock the full potential of their data, foster a culture of data-driven decision-making, and gain a competitive edge in today’s fast-paced business environment.

As businesses continue to navigate the complexities of the digital age, embracing augmented analytics will be essential for staying agile, responsive, and resilient in the face of evolving challenges and opportunities. By harnessing the power of augmented analytics, organizations can transform data into a strategic asset, drive innovation, and achieve sustainable growth in an increasingly data-driven world.

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