Augmented Analytics Benefits and its Future

The Future of Augmented Analytics and its Advantages

In the year 2022, the global augmented analytics market achieved a valuation milestone, reaching USD 15.84 billion. Projections indicate a notable upswing, foreseeing the market's value to ascend from USD 18.68 billion in 2023 to an impressive USD 66.54 billion by 2030. This anticipated expansion is underpinned by a substantial compound annual growth rate (CAGR) of 19.9% throughout the forecast period. The driving forces behind the market's growth encompass the escalating intricacy and abundance of data, a burgeoning demand for decision-making grounded in data, and the imperative to democratize analytics across a spectrum of organizations.

What does the term "Augmented Analytics" mean?

Augmented analytics automates Data Analytics using machine learning and natural language processing. It minimizes the need for specialized skills and makes data exploration accessible to a broader audience. Users possess the capability to streamline tasks like data preparation and produce valuable insights that can result in significant business outcomes.. Augmented analytics offers context-based insights, explanations, and recommendations. It democratizes data-driven decision-making with natural language interfaces, such as chatbots and voice assistants, making analytics more efficient and inclusive.

Why are augmented analytics important?

• Data as a Key Asset

Recognizing data as a critical asset in the modern economy acknowledges its central role in shaping business strategies and outcomes. However, the vastness and complexity of data pose challenges for human analysis alone.

• Automation and Enhancement through AI and Machine Learning

AI and machine learning are used in augmented analytics to automate and improve the analytics process, including data collection and generating insights, to efficiently handle large amounts of data.

• Reducing Dependence on Scarce Data Scientists

The scarcity and cost associated with data scientists can be mitigated through augmented analytics. These tools automate mundane tasks, freeing data scientists and empowering other roles to perform better.

• Faster Discovery of Deeper Insights

In a landscape dominated by data, businesses can secure a competitive advantage by leveraging augmented analytics to derive timely and strategic insights, enabling them to make informed decisions.

The Evolution of Analytics

Data and analytics have undergone a significant transformation in recent years. Advanced tools once reserved for experts are now accessible to everyone thanks to machine learning.

1. Traditional Analytics: Traditional analytics is usually driven by the IT department or specialists with the expertise and skills to handle complex data and tools. Users cannot explore and analyze data independently and rely on specialists to perform most tasks. The tools used for traditional analytics are advanced and designed for data and analytics professionals. Traditional analytics also produces large-scale reports covering various topics and metrics.

2. Self-Service Analytics: One of the trends in analytics is the emergence of business-led analytics, where business users take a more active role in driving the analytics process. This requires user-friendly tools that enable users to conduct analytics tasks independently without relying on IT or data experts. The goal is to empower users to generate insights and make data-driven decisions.

3. Augmented Analytics: Augmented analytics is a new paradigm that combines AI and machine learning-led technologies with complete user independence. Users can leverage AI tools and guided workflows to drive the analytics process without relying on experts or IT. Augmented analytics enables fast, deep, and previously hidden insights that can transform business decisions and outcomes.

Key Components of Augmented Analytics

The cloud-based platform of augmented analytics, also known as AI analytics, effectively merges data and analytics. By streamlining data management and empowering various types of analytics, it offers a simplified approach to data analysis.

• Data Collection and Preparation: AI automates data collection, cleaning, and integration from diverse sources. It handles big data sets, identifies potential errors, and recognizes specific attributes. The automated nature of AI ensures rapid and accurate data processing, improving overall efficiency. It minimizes manual effort for routine tasks, allowing data professionals to focus on more complex aspects of analysis and decision-making.

• Data Exploration and Visualization: The incorporation of sophisticated algorithms for analysis and visualization greatly enhances the process of data discovery, mining, and representation. Utilizing Artificial Intelligence (AI) techniques such as clustering and anomaly detection allows for the identification of hidden patterns and anomalies within the data. AI-driven visualization tools enhance data exploration by providing interactive and user-friendly visual displays. These tools allow users to understand data and extract actionable insights comprehensively.

• Machine Learning and Statistical Analysis: Machine learning (ML) constructs models by analyzing historical data in order to forecast patterns and make informed choices with limited human involvement. These models are used in AI data analytics applications, such as recommendation systems and search-based analytics in natural language processing. The best-augmented analytics tools are equipped with AutoML capabilities, enabling custom ML models to be created without requiring extensive training.

• Natural Language Processing (NLP) and Natural Language Generation (NLG): NLP and NLG are crucial tools for data analysis. They help computers and humans communicate effectively, extract critical information from large amounts of text-based data, and perform sentiment analysis to detect emotional tone. With NLG, analytics tools can provide easy-to-understand responses and generate reports in your language. This automated process enhances efficiency and conserves valuable resources, enabling compliance teams to efficiently extract crucial information from organized data. Sophisticated natural language generation (NLG) tools equipped with machine learning capabilities have the ability to provide comprehensive responses to intricate inquiries.

• Predictive Analytics: AI allows for the development precise prediction models that handle complex data, nonlinear relationships, and intricate features. Algorithms like ensemble learning and deep neural networks improve the accuracy and reliability of these models even further.

• Prescriptive Analytics: Prescriptive analytics uses advanced techniques to analyze data and provide specific recommendations for the optimal action or strategy to take in the future. It helps companies optimize inventory levels, pricing strategies, marketing campaigns, or customer service. It can also suggest the best actions in other applications, such as CRM or ERP software, based on data and rules. It answers the question, "What is the best course of action?" using mathematical models, algorithms, and simulations.

The benefit of augmented analytics and augmented analytics tools

1. Agility: AI-powered augmentation streamlines the search space, presents relevant data, and suggests productive analysis paths. This agility lets users answer data questions faster, freeing time for more strategic tasks. 

2. Accuracy: Machine learning and AI technologies ensure accuracy by efficiently handling repetitive tasks. Augmented analytics provides a comprehensive view, reducing the risk of confirmation bias and enabling users to make informed decisions based on thorough analysis.

3. Efficiency: Augmented analytics leverages machine learning and AI to automate specialized, repetitive tasks in data preparation, discovery, and statistical analyses. This automation enhances efficiency, saving users time and energy.

4. Confidence: Augmented analytics tools are designed to be user-friendly, making data analysis more approachable for a broader audience. Integration into business workflows allows seamless exploration of specific questions. The context-aware nature of augmented analytics instills confidence in conclusions, leveraging users' industry expertise 

Challenges of augmented analytics tools

Adopting Augmented Analytics is a significant step in transforming the data and analytics market, offering the potential for a competitive advantage. However, several challenges and resistances may impede its integration. Key challenges include:

Adoption Challenges

• Outcome Expectations: Organizations often expect quick and favorable results from augmented analytics. However, technology has its lifecycle, utilizing AI computation, analytics, and automation techniques. Maturation and improvement in analysis and outcomes require time.

• "Black Box" Image: Augmented analytics is perceived as a "black box," lacking transparency in decision-making. Trust in recommendations and insights is hindered without clear explanations, impacting user confidence. 

• Dependency on Legacy Platforms: Organizations may face challenges due to a reliance on legacy analytics platforms, acting as barriers to adopting augmented analytics. 

• Job Security Concerns: Automation in analytical operations raises concerns about job security as traditional workloads and processes undergo significant revamping. 

• Resistance from Business Leaders: Business leaders may resist adapting to changing scenarios, preferring intuition and traditional decision-making practices over augmented analytics. 

• Misconception of Linear Progression: There's a misconception that augmented analytics maturity follows a linear, progressive path, implemented once a robust foundation is established.

Governance Challenges

• Need for AI Governance: There's a critical need to emphasize AI and analytics governance and collaboration between analysts and data scientists to support the growing presence of citizen data scientists within business units. 

• Governance for Analytic Content: Leaders must establish rules to govern analytic content and insights generated by augmented analytics. This ensures accuracy and validity and manages bias levels in findings and recommendations.

Use cases for Augmented Analytics

Different Sector	Benefits
Finance	Financial Forecasting: Augmented analytics leverages historical data and external factors for forecasting revenue, expenses, cash flow, and profitability. It facilitates scenario and what-if analysis for evaluating diverse outcomes and risks. Financial Reporting: Augmented analytics generates interactive, dynamic reports and dashboards to concisely present financial performance and key metrics. It also offers natural language explanations and narratives.  Financial Risk Management: Augmented analytics identifies and monitors financial risks like credit, market, liquidity, and operational risks. It provides alerts and suggestions for mitigating or preventing potential losses.
HR	Turnover and Engagement: Identify turnover and engagement factors for informed retention strategies.  Talent Acquisition: Analyze resumes, skills, and job fit to find optimal candidates. Automate tasks for streamlined hiring processes.  Learning and Development: Use data analytics to assess workforce skills gaps, guiding targeted learning programs.  Diversity and Inclusion: Detect and reduce bias in HR decisions with AI, fostering workplace diversity and inclusion.  Employee Performance: Utilize data analytics to set SMART goals for employees.  Feedback and Recognition: Provide timely, personalized feedback and recognition for enhanced employee performance
Retail	Optimize Pricing and Promotions: Utilize augmented analytics to optimize pricing and promotions, considering customer behavior and demand patterns.  Customer Segmentation and Personalization: Segment customers and personalize marketing using data-driven insights from augmented analytics.  Demand Forecasting and Inventory Management: Forecast demand, manage inventory, and reduce stockouts with predictive analytics powered by augmented analytics.  Fraud Detection and Prevention Employ machine learning and anomaly detection from augmented analytics to detect and prevent fraud and anomalies.  Customer Loyalty and Retention: Increase customer loyalty and retention by leveraging sentiment analysis and natural language processing from augmented analytics.
Health Care	Forecasting Health Outcomes: Augmented analytics utilizes patient data and AI algorithms to forecast health outcomes, enabling healthcare professionals to tailor treatment plans to individual patient needs.  Enhancing Patient Care: Augmented analytics analyses data trends and provides healthcare providers with deeper insights into patient health, facilitating better decision-making and proactive care.  Predicting Disease Outbreaks: Augmented analytics contributes to predicting disease outbreaks and enhancing clinical trials by applying data analysis and natural language processing techniques.
Sales and Marketing	Optimizing Pricing Strategies: Augmented analytics analyzes customer behaviour and demand patterns to optimize pricing strategies, increasing profitability and competitiveness.  Enhancing Customer Segmentation: Augmented analytics segments customers based on preferences, needs, and potential value, enabling personalized and targeted marketing and sales efforts.  Predicting Customer Churn and Retention: Augmented analytics predicts customer churn and retention rates, allowing personalized interventions to retain valuable customers and improve satisfaction.  Improving Lead Generation and Qualification: Augmented analytics, employing natural language processing and sentiment analysis, enhances lead generation and qualification for more accurate targeting based on customer sentiments and needs.  Measuring Marketing Campaign Effectiveness: Augmented analytics provides insights to measure the effectiveness of marketing campaigns, allowing for data-driven improvements and strategic adjustments.
Manufacturing	Enhanced Logistics and Resource Management: Improves logistics and resource management by estimating shipping timings, production capabilities, and warehouse space availability through data analysis from multiple sources.  Proactive Decision-Making with Sensor and Machine Data: Augmented analytics utilizes data from sensors and machines to optimize production processes, enabling proactive decision-making and minimizing downtime.  Improved Manufacturing Operations:  Quality Control Enhancement: Augmented analytics enhances quality control processes, ensuring products meet specifications.  Predictive Maintenance: Predicts maintenance needs, allowing for proactive scheduling and reduced downtime.  Optimized Production Schedules: Enhances efficiency and reduces lead times through data-driven insights in production scheduling.  Margin Increase: Supports cost optimization, resource efficiency, and strategic decision-making, leading to higher margins.

Conclusion

The functionalities of augmented analytics have transformative applications across all sectors, streamlining business operations and improving access to valuable data. Successful adoption requires a holistic approach, addressing challenges in strategy, people, process, data, and technology components.

Read more about Top Real-Time Analytics Use Cases

Learn more about Real-time Analytics: Transforming Data into Instant Insights

Explore more about Augmented Analytics in Healthcare and its Use Cases