Leveraging Augmented Analytics for Enhanced Business Intelligence

Understanding Augmented Analytics

Augmented analytics represents a category of analytical techniques empowered by artificial intelligence (AI) and machine learning (ML), facilitating enhanced interaction with data at a contextual level. This category encompasses various tools and software solutions designed to extend analytical capabilities, including recommendations, insights, and query guidance, to a broader user base.

According to insights from Gartner, augmented analytics involves leveraging enabling technologies such as machine learning and AI to support data preparation, insight generation, and insight explanation. This augmentation aims to enhance how individuals explore and analyze data within analytics and business intelligence (BI) platforms.

According to Forbes, data scientists spend nearly 80% of their time on data preparation and cleaning. Despite efforts to streamline data processing, delivery times often fall short of expectations.

Key Components of Augmented Analytics

1. Machine Learning (ML): ML, a subset of artificial intelligence, employs algorithms to swiftly sift through historical data, detect patterns, identify anomalies, and provide insights and recommendations. These ML models excel in handling vast amounts of both structured and unstructured data, continuously learning from new information without requiring human intervention. The foundation of most augmented analytics capabilities lies in ML models.
2. Natural Language Technologies: Natural language processing (NLP) and natural language generation (NLG) facilitate seamless communication between humans and computers. NLP interprets human language for computers, while NLG translates computer code into human-readable language. This enables business professionals to engage in intuitive, question-and-answer sessions with machines, using familiar industry-specific terms and terminology.
3. Automation: Augmented analytics leverages automation powered by ML technologies to streamline manual tasks throughout the data analytics lifecycle. This automation drastically reduces the time required for building, training, and deploying ML models. For instance, with the assistance of automatically generated prompts, individuals—regardless of technical proficiency—can swiftly discover and prepare raw data. Furthermore, toward the end of the lifecycle, text-based reports are automatically generated and distributed according to user-specified schedules, expediting the sharing of insights.

The Three Generations of Business Intelligence

Traditional Business Intelligence

Traditional business intelligence represents the earliest iteration of BI, characterized by its reliance on legacy infrastructure and manual data processing. IT professionals were responsible for aggregating data, structuring OLAP cubes, and generating static reports and dashboards. This approach led to delays in delivering critical insights, as IT teams needed weeks to design models and reports. Additionally, traditional BI systems only provided retrospective views on queries, lacking predictive capabilities, which left decision-makers relying on intuition for new opportunities and solutions.

Self-Service Business Intelligence

Self-service business intelligence solutions offer notable advancements over traditional BI by empowering data analysts and business users to create their reports and visualizations. Accessible to all employees, self-service analytics enables ad hoc reporting and deeper data exploration. However, these solutions still face challenges in addressing the last-mile problem, as their visualization interfaces are complex for many users. With the increasing volume and complexity of data, manual analysis becomes impractical even for expert data analysts.

Augmented Analytics

Augmented analytics represents the next evolution in BI, enhancing the self-service analytics model with artificial intelligence. By automating data preparation and offering a conversational UI for ad hoc reporting, augmented analytics simplifies data analysis for users. Moreover, it leverages natural language generation to deliver insights and visualizations in a user-friendly format, providing a deeper understanding of the underlying reasons behind the data. Over time, the algorithm adapts to user intent, offering more targeted and nuanced responses to complex inquiries.

Benefits of Augmented Analytics

1. Enhanced Depth of Data Analysis: Augmented analytics simplifies the analysis process by identifying key factors driving outcomes through a comprehensive examination of data combinations.
2. Swift Results: Business users receive instant responses to their inquiries, eliminating the need to wait for data analysts and accelerating decision-making processes.
3. Optimized Resource Allocation: With augmented analytics handling routine queries, data analysts can dedicate more time to in-depth research, contributing to increased revenue generation for the company.
4. Actionable Insights: Augmented analytics streamlines the data analysis process, enabling users to extract crucial insights that inform strategic business decisions.

Applications Across Industries

Finance and Banking

• Fraud Detection and Risk Management: Advanced machine learning algorithms will identify and prevent fraudulent transactions in real time, safeguarding financial institutions and customers.
• Automated Loan Processing: Streamlined lending processes powered by AI will enable faster loan approvals and improve access to finance for individuals and businesses.
• Blockchain for Secure Transactions: Blockchain technology will ensure secure and transparent financial transactions, boosting trust and efficiency within the financial ecosystem.

Healthcare and Life Sciences

• Precision Medicine: AI-driven analysis of vast medical data will enable personalized treatment plans and drug development, tailoring healthcare to individual patient needs.
• Remote Patient Monitoring: Wearable devices and sensors will collect real-time health data, allowing for remote patient monitoring and proactive interventions.
• Automated Medical Diagnosis: AI algorithms will assist medical professionals in analyzing medical images and diagnosing diseases with greater accuracy and speed.

Retail and E-commerce

• Dynamic Pricing and Inventory Management: Real-time data analysis will optimize pricing strategies and ensure optimal inventory levels, boosting profitability and customer satisfaction.
• Chatbots and Virtual Assistants for Customer Service: AI-powered chatbots will answer customer queries and resolve issues instantly, providing 24/7 support and improving customer satisfaction.
• Automated Logistics and Warehousing: Self-driving vehicles and robots will streamline logistics and warehousing operations, leading to faster deliveries and reduced costs.

Manufacturing and Supply Chain

• Predictive Maintenance: AI algorithms will analyze sensor data to predict equipment failures, enabling proactive maintenance and preventing downtime.
• Optimized Supply Chain Management: AI-powered analytics will optimize transportation routes and inventory levels, improving resource allocation and reducing costs.
• End-to-End Visibility and Traceability: Blockchain technology will ensure transparency and traceability throughout the supply chain, building trust and reducing fraud

Implementation Strategies

Data Preparation

Data preparation encompasses the tasks necessary to ready data for querying and analysis. It involves collecting, cleaning, connecting, and validating datasets, often requiring the expertise of developers and data scientists.

Augmented analytics tools streamline data preparation by automating tasks and facilitating integration with various data sources, including data warehouses, cloud platforms, web services, and analytics platforms. Once data and metadata are incorporated into the pipeline, processes such as data cleaning and dataset unification are executed automatically, allowing data scientists, engineers, and developers to focus on crafting new analyses and deepening insights.

Insight Discovery

Insight discovery involves analyzing data using predefined models to uncover answers to queries such as quarterly revenue or customer acquisition rates. Traditionally, manual development of models by data scientists may result in insights lacking specificity.

With augmented analytics, insight discovery becomes more accessible and comprehensive. Natural language and voice inputs can initiate queries, replacing the need for hyper-specific keyword entries. Machine learning algorithms can analyze extensive datasets to unearth detailed, targeted insights regardless of data volume, enhancing the depth of understanding.

Insights Sharing

Insights sharing entails delivering insights to end-users via reports or visualizations. Traditionally, IT teams generate data reports and manually create charts and graphs for interpretation by business users.

Augmented analytics significantly reduces time to insights and human effort. Leveraging natural language generation, these platforms deliver real-time insights accessible through online dashboards. Insights provided include direct answers to natural language queries along with the rationale behind the answers, enhancing comprehension and decision-making.

Top Augmented Analytics Tools

1. Tableau

2. Microsoft Power BI

3. Qlik

4. AnswerRocket

5. Oracle Analytics Cloud

6. SAP Analytics Cloud

7. Sisense 

8. ThoughtSpot

9. Domo

10. IBM Cognos Analytics 

Future Trends of Augmented Analytics in BI

With the evolving business intelligence technology, a transformative force reshaping data analysis and interpretation is AI-augmented analytics. This revolutionizes how enterprises derive insights, make informed decisions, and foster innovation. Following are the future trends of augmented analytics in business intelligence.

The Ascendancy of AI in Business Intelligence

Artificial Intelligence (AI) has become a prominent force across diverse industries, including BI. AI’s capacity to process extensive datasets, discern patterns, and make forecasts has unlocked unprecedented analytical possibilities. By integrating AI into BI tools, organizations can harness these capabilities to glean deeper insights and unearth latent opportunities.

Streamlined Data Preparation and Cleansing

AI-augmented analytics optimizes the data preparation process by automating tasks such as data cleansing, normalization, and transformation. This automation diminishes the time and effort required to prepare data for analysis, allowing analysts to concentrate on extracting meaningful insights rather than grappling with data manipulation.

Natural Language Processing (NLP) for Querying

A notable advancement in AI-augmented analytics is the incorporation of Natural Language Processing (NLP). This enables users to interact with BI tools using everyday language. Instead of crafting intricate queries, users can pose inquiries like, “What were our Q3 sales figures?” and receive prompt, pertinent responses. This democratizes data access, empowering individuals across the organization to make data-driven decisions.

Predictive Analytics and Forecasting

AI-powered algorithms excel at identifying patterns and trends in data, particularly in predictive analytics and forecasting. By analyzing historical data and considering various factors, AI-augmented analytics can produce accurate predictions about future trends, enabling businesses to proactively strategize and adapt to evolving market dynamics.

Automated Insights and Anomaly Detection

AI-augmented analytics tools automate the identification of significant insights and anomalies within datasets. This ranges from uncovering unexpected trends to flagging potential data discrepancies. By automating this process, organizations can swiftly pinpoint critical information that may otherwise have gone unnoticed.

Personalized Recommendations

AI-augmented analytics offers personalized recommendations based on individual user behavior and preferences. For instance, it can propose relevant visualizations, reports, or insights tailored to specific user requirements. This enhances user engagement and ensures that individuals receive pertinent information tailored to their decision-making processes.

Envisioning the Future of BI with Augmented Analytics

The ability to swiftly attain meaningful insights is paramount for establishing and sustaining a competitive edge. Augmented analytics tools facilitate the rapid generation of pertinent information, ensuring readiness when required.

With augmented BI, individuals within organizations interact with data through personalized, conversational interfaces, enabling exploration from diverse perspectives and the generation of role-relevant insights. Furthermore, leveraging augmented analytics capabilities alongside business analytics tools provides a clearer understanding of events and trends, empowering proactive planning rather than reactionary responses.

Through prompt and precise data analysis, organizations enhance decision-making processes and formulate more effective strategies. Consequently, they foster sustained growth and increased revenues over time.

Frequently Asked Questions about Augmented Analytics in BI

1. How does augmented analytics differ from traditional BI?

Traditional BI tools require manual effort for data preparation, analysis, and interpretation. Augmented analytics automates these tasks using ML algorithms, allowing users to derive insights faster and with less expertise in data analysis.

2. What are the benefits of augmented analytics?

Augmented analytics streamlines the BI process, reduces the need for manual data manipulation, enables faster decision-making, uncovers hidden patterns in data, and provides more accurate insights.

3. What types of tasks can be augmented using augmented analytics?

Augmented analytics can assist in various tasks including data cleansing, data modeling, trend analysis, anomaly detection, predictive modeling, natural language query processing, and data visualization.

4. How does augmented analytics improve data quality?

By automating data preparation tasks such as data cleansing and normalization, augmented analytics helps improve data quality by reducing human error and ensuring consistency across datasets.

5. Is augmented analytics suitable for all businesses?

Augmented analytics can benefit businesses of all sizes and industries, particularly those with large volumes of data and complex analytical needs. However, the extent of its usefulness may vary depending on specific business requirements.

6. What role do data scientists play in augmented analytics?

Data scientists are still essential in the development and optimization of augmented analytics algorithms. They are responsible for training ML models, fine-tuning algorithms, and ensuring the accuracy and reliability of insights generated.

7. How does augmented analytics handle data privacy and security?

Augmented analytics platforms should adhere to strict data privacy and security standards, such as GDPR and HIPAA, by implementing robust encryption, access controls, and anonymization techniques to protect sensitive data.

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