Augmented Analytics Market Is Projected To Reach USD 81.4 Billion By 2030

Richmond, United States, 2024-Apr-05 — /EPR Network/ —

The augmented analytics market refers to the use of artificial intelligence (AI) and machine learning (ML) technologies to enhance data analytics processes, enabling organizations to uncover deeper insights, automate repetitive tasks, and improve decision-making capabilities. Augmented analytics solutions leverage AI and ML algorithms to assist users in data preparation, analysis, visualization, and interpretation, thereby augmenting human intelligence with the power of automation and predictive analytics. Here’s an overview of the augmented analytics market, including key points, trends, and recent developments: Augmented Analytics Market was valued USD 18.7 Billion in 2022 and projected to reach USD 81.4 Billion by 2030, growing at a CAGR of 20.2% during the forecast period of 2023-2030.

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Major vendors in the global Augmented Analytics Market:

• Salesforce
• SAP
• IBM
• Microsoft
• Oracle
• Tableau
• MicroStrategy
• SAS
• Qlik
• TIBCO Software
• Sisense
• Information Builders
• Yellowfin
• ThoughtSpot
• Domo
• Birst
• Logi Analytics
• Looker
• Pyramid Analytics
• Board International
• Others

Key Points:

• Market Growth: The augmented analytics market has experienced significant growth in recent years, driven by the increasing volume and complexity of data, growing demand for actionable insights, and advancements in AI and ML technologies. Augmented analytics solutions are enabling organizations to extract valuable insights from data more efficiently and accurately, leading to improved decision-making, operational efficiency, and competitive advantage.
• Technological Foundations: Augmented analytics solutions leverage a combination of AI, ML, natural language processing (NLP), and automation technologies to automate data preparation, analysis, and visualization tasks traditionally performed by data scientists and analysts. These solutions enable users with varying levels of technical expertise to access and analyze data, discover patterns and trends, generate insights, and make data-driven decisions without requiring advanced analytical skills or coding knowledge.
• Integration with Business Intelligence (BI) and Analytics Platforms: Augmented analytics capabilities are increasingly being integrated into existing BI and analytics platforms, enabling organizations to leverage augmented analytics features alongside traditional reporting, dashboarding, and data visualization capabilities. BI vendors are embedding AI-driven features such as automated insights, smart data discovery, natural language querying, and predictive analytics into their platforms to enhance usability, productivity, and value for users.
• Key Players: The augmented analytics market is characterized by the presence of established BI and analytics vendors, as well as specialized augmented analytics providers offering standalone solutions. Key players in the market include established BI vendors such as Tableau (now part of Salesforce), Microsoft, SAP, IBM, and Qlik, as well as specialized augmented analytics vendors such as ThoughtSpot, Sisense, Alteryx, and Domo, among others. These vendors offer a variety of augmented analytics solutions tailored to the needs of different industries and use cases.
• Business Applications: Augmented analytics solutions are being used across various industries and functional areas to address a wide range of business challenges and opportunities. Common applications of augmented analytics include sales and marketing analytics, customer analytics, financial planning and analysis (FP&A), supply chain analytics, human resources (HR) analytics, risk management, and fraud detection. By leveraging augmented analytics, organizations can gain deeper insights into their data, improve decision-making processes, and drive business performance.

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Key Trends:

• Natural Language Processing (NLP) and Conversational Analytics: NLP and conversational analytics capabilities are increasingly being integrated into augmented analytics solutions to enable users to interact with data using natural language queries and conversational interfaces. NLP-powered chatbots, voice assistants, and virtual analysts allow users to ask questions, receive answers, and explore data insights using everyday language, making analytics more accessible and intuitive for non-technical users.
• Automated Machine Learning (AutoML): Automated machine learning (AutoML) is gaining traction in the augmented analytics market, enabling organizations to automate the process of building, training, and deploying ML models without requiring expertise in data science or coding. AutoML platforms streamline the end-to-end ML lifecycle, from data preprocessing and feature engineering to model selection and deployment, allowing users to generate predictive models and actionable insights with minimal manual intervention.
• Explainable AI (XAI): Explainable AI (XAI) is becoming increasingly important in augmented analytics, particularly in regulated industries and applications where transparency and interpretability of AI models are critical. XAI techniques enable users to understand how AI and ML algorithms arrive at their predictions and recommendations, making it easier to trust and validate the results and comply with regulatory requirements such as GDPR and CCPA.
• Augmented Data Preparation and Data Quality Management: Augmented analytics solutions are enhancing data preparation and data quality management processes by automating data cleansing, normalization, enrichment, and integration tasks. These solutions leverage AI and ML algorithms to identify and correct data errors, detect anomalies, and improve data quality, enabling organizations to streamline the data preparation process and ensure the accuracy and reliability of insights derived from analytics.
• Embedded Analytics and Application Integration: Augmented analytics capabilities are increasingly being embedded into enterprise applications and business processes to deliver insights directly within the context of user workflows. By embedding analytics into CRM, ERP, HR, and other applications, organizations can provide users with real-time insights and recommendations to support decision-making and drive productivity. Additionally, augmented analytics solutions are integrating with collaboration tools, workflow automation platforms, and productivity suites to enable seamless integration of analytics into everyday business processes.

Recent Industry Developments:

• Acquisitions and Partnerships: BI and analytics vendors are acquiring or partnering with AI and ML startups to enhance their augmented analytics capabilities and accelerate innovation. For example, Salesforce acquired Tableau to integrate its AI-driven analytics features with Salesforce’s CRM platform, while Google Cloud partnered with Looker to integrate its AI-powered analytics capabilities with Google Cloud’s infrastructure and services.
• Advancements in Natural Language Processing (NLP): Recent advancements in NLP technologies, such as transformer models like BERT and GPT-3, are enabling more advanced natural language querying and conversational analytics capabilities in augmented analytics solutions. These advancements allow users to ask more complex questions, receive more accurate answers, and interact with data in more intuitive ways using natural language interfaces.
• Focus on Citizen Data Scientists: Augmented analytics vendors are increasingly targeting non-technical users, or “citizen data scientists,” by providing user-friendly interfaces, guided workflows, and automated tools that simplify the process of data analysis and model building. By empowering citizen data scientists with augmented analytics capabilities, organizations can democratize access to data and analytics, foster data-driven decision-making across the organization, and alleviate the burden on data science teams.
• Cloud-Native and SaaS Offerings: Augmented analytics vendors are offering cloud-native and software as a service (SaaS) offerings to provide organizations with flexible deployment options and scalability. Cloud-based augmented analytics solutions enable organizations to access analytics capabilities without the need for on-premises infrastructure or IT support, making it easier to scale resources, collaborate with remote teams, and adapt to changing business requirements.
• Focus on Explainability and Trustworthiness: Augmented analytics vendors are placing increased emphasis on explainability and trustworthiness of AI and ML models to address concerns related to bias, fairness, and ethical use of data. By providing transparency into the decision-making process of AI models and enabling users to understand how predictions are generated, augmented analytics solutions help build trust and confidence in the insights derived from analytics.

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In conclusion, the augmented analytics market is experiencing rapid growth and innovation, driven by advancements in AI and ML technologies, growing demand for actionable insights, and the need to democratize data and analytics across organizations. Key trends such as NLP and conversational analytics, AutoML, XAI, augmented data preparation, embedded analytics, and focus on citizen data scientists are shaping the future of the augmented analytics market. Recent industry developments highlight the integration of augmented analytics capabilities into existing BI and analytics platforms, advancements in NLP technologies, acquisitions and partnerships, cloud-native and SaaS offerings, and focus on explainability and trustworthiness driving innovation and growth in the augmented analytics market.