Global Augmented Analytics Market Size, Outlook By Product, Trends and Forecast To 2024-2032

Augmented Analytics Market And Forecast

Augmented Analytics Market size was valued to be around USD 6.91 Billion in 2023 and is anticipated to reach USD 8.96 Billion by 2031, growing at a CAGR of 31.6% from 2024 to 2031.

• Augmented analytics is a method that uses machine learning and natural language processing to automate data preparation, insight discovery, and decision-making processes within organizations. It goes beyond traditional analytics by incorporating artificial intelligence and automation to help human analysts interpret data, identify patterns, and generate actionable insights.
• This approach streamlines analytical workflows, reduces time and effort, and uncovers valuable insights from complex datasets. Its intuitive interfaces and natural language capabilities enable users with varying technical expertise to interact with data effectively. Augmented analytics empowers organizations to make data-driven decisions and drive innovation.
• The Augmented Analytics Market uses advanced analytics techniques like machine learning and AI to improve data analysis processes. It streamlines data preparation, exploration, and insight generation by automating repetitive tasks, providing actionable insights, and enabling data-driven decision-making. Solutions often include natural language processing, predictive modeling, and data visualization.
• The market is characterized by rapid innovation and adoption due to the increasing volume, variety, and velocity of business data, and the growing demand for data-driven insights to gain competitive advantages and drive business outcomes.

Global Augmented Analytics Market Dynamics

The key market dynamics that are shaping the global Augmented Analytics Market include:

Key Market Drivers:

• Increasing Data Complexity: Traditional analytics tools find it difficult to extract actionable insights from the rapidly expanding amount of data coming from a variety of sources, including social media, IoT devices, and sensors. Advanced algorithms and automation are provided by augmented analytics to effectively manage the volume and complexity of data.
• Real-time insights: They are becoming more and more necessary for businesses to make prompt decisions. Platforms for augmented analytics process data in real-time using AI and ML algorithms, giving businesses a competitive advantage and the ability to react swiftly to shifting market conditions.
• Lack of Data Science Skills: There is a deficiency in data science experts who can draw conclusions from intricate datasets. By automating operations often completed by data scientists, augmented analytics solutions democratize data analytics by enabling business users with less expertise.
• Emphasis on Data-driven Decision Making: Businesses are realizing how crucial it is to use data to inform decisions in order to increase operational effectiveness and spur corporate expansion. Natural language processing and user-friendly interfaces offered by augmented analytics enable decision-makers at all levels to easily access and comprehend data insights.
• Quick Developments in AI and ML Technologies: As these fields continue to grow, augmented analytics platforms are becoming more and more capable. Prescriptive and predictive analytics are made possible by these technologies, which give firms the ability to efficiently discover opportunities, foresee future trends, and manage risks.

Key Challenges:

• Data Integrity and Quality: A major component of augmented analytics is the use of high-quality data from multiple sources. It can be difficult to ensure data quality and integrate different data sources, particularly when working with partial or unstructured data.
• Data Privacy and Security: It is crucial since sensitive and personal data is being used for analytics more and more. This raises privacy and security concerns. Processing data becomes more challenging when regulations like the CCPA and GDPR are followed.
• Skill Gap: Data science, machine learning, and domain expertise are needed to implement augmented analytics solutions. Nevertheless, firms find it challenging to efficiently use augmented analytics technologies due to a lack of qualified personnel in these fields.
• Interpretability and Explainability: Complex models produced by AI and ML algorithms used in augmented analytics are frequently challenging to understand and interpret. Gaining users’ and stakeholders’ trust and adoption of these models depends on ensuring their explainability and openness.
• Resistance to Change in Management: Organizational adjustments to workflows, procedures, and culture are necessary for the adoption of augmented analytics. Adoption and implementation may be hampered by stakeholder buy-in and resistance to change.

Key Trends:

• Combining AI and Machine Learning: In order to automate the processes of data preparation, analysis, and insight generation, augmented analytics platforms have been incorporating sophisticated AI and machine learning algorithms more and more. These technological advancements reduced the amount of human labor needed by data analysts by enabling quicker and more accurate insights from data.
• Natural Language Processing (NLP): NLP features, which enable users to engage with data via natural language queries, are increasingly seen in augmented analytics products. This trend increased the accessibility of data analysis for consumers lacking sophisticated technical abilities, allowing a wider range of stakeholders to gain insights from data.
• Automated Data Preparation: In traditional analytics workflows, data preparation is frequently a time-consuming task. To expedite this process and shorten the time to insight, augmented analytics platforms were integrating automated data preparation capabilities, such as data transformation, integration, and purification.
• Embedded Analytics: It is typical of incorporating enhanced analytics features into other business applications. Through the direct integration of analytics into operational systems like marketing automation platforms, ERP, and CRM, companies may enable users to make data-driven decisions inside their current workflows.
• Emphasis on Citizen Data Scientists: The goal of augmented analytics platforms is to enable business users with domain knowledge but no technical proficiency—known as citizen data scientists—to independently evaluate data and draw conclusions. Important elements of this movement included automatic insights creation, guided analytics features, and user-friendly interfaces.

Global Augmented Analytics Market Regional Analysis

Here is a more detailed regional analysis of the global Augmented Analytics Market:

Asia-Pacific

• Asia-Pacific is dominating the Augmented Analytics Market owing to the nation experiencing rapid digital transformation.
• Also, the emphasis on data-driven decision-making for competitiveness is enabling the various industries integrate augmented analytics into their operational activities. Augmented analytics solutions enable efficient data analysis, enabling businesses to adapt to digital disruptions and capitalize on new opportunities.
• The Asia-Pacific region is witnessing a growing tech-savvy population, boosting the demand for augmented analytics solutions, as companies leverage employee skills for efficient data analytics use.
• This region is witnessing a surge in e-commerce, driven by the growing middle class, rapid cell phone usage, and rising internet penetration, necessitating the need for augmented analytics solutions.

North America

• In North America, the Augmented Analytics Market is maturing and will continue to grow over the forecast period.
• North America, particularly the US, is a hub for technological innovation in data analytics, machine learning, and AI, leading to the development of sophisticated augmented analytics platforms for various industries.
• North American enterprises, including SMBs and large corporations, are rapidly adopting augmented analytics solutions to enhance data-driven decision-making in industries like finance, healthcare, retail, and e-commerce, gaining a competitive edge.
• North America’s favorable regulatory environment encourages innovation in analytics technologies, while data privacy regulations like GDPR and CCPA provide opportunities for businesses to improve data governance and risk management.

Global Augmented Analytics Market: Segmentation Analysis

The Global Augmented Analytics Market is segmented on the basis of Component, Deployment Mode, Organization Size, and Geography.

Augmented Analytics Market, By Component

• Software
• Services
  1. Training and Consulting
  2. Deployment and Integration
  3. Support and Maintenance

Based on Component, the market is a divided into Software and Services. The software segment is projected to dominate the market. It includes core platforms that enable businesses to analyze data, generate insights, and make data-driven decisions. Services like training, consulting, deployment, and maintenance help organizations optimize their analytics workflows and ensure successful adoption. These components form a comprehensive ecosystem that empowers organizations to harness the full potential of augmented analytics, drive innovation, and achieve competitive advantage in today’s data-driven business landscape.

Augmented Analytics Market, By Deployment Mode

• On-Premises
• Cloud-Based

Based on Deployment Mode, the market is divided into On-Premises and Cloud-Based. The cloud-based segment is projected to dominate the market as it is known to offer flexibility and scalability. On-premises deployment is preferred for businesses with strict data control, while cloud-based deployment is gaining traction due to its scalability, agility, and cost-effectiveness. Both deployment modes cater to diverse business objectives and budget considerations.

Augmented Analytics Market, By Organization Size

• Small And Medium-Sized Enterprises (SMEs)
• Large Enterprise

Based on Organization Size, the market is divided into Small And Medium-Sized Enterprises (SMEs) and Large Enterprise. The market is growing rapidly, catering to both SMEs and large enterprises. SMEs are leveraging augmented analytics to gain actionable insights from their data, enabling them to make informed decisions and optimize operations. Large enterprises, on the other hand, are leveraging augmented analytics to unlock the full potential of their vast data, enabling them to streamline data analysis processes, enhance decision-making, and drive innovation. As businesses prioritize data-driven decision-making and invest in advanced analytics technologies, the demand for augmented analytics solutions is expected to rise.

Key Players

The “Global Augmented Analytics Market” study report will provide valuable insight with an emphasis on the global market. The major players in the market are Domo, Inc., International Business Machines Corporation, Microsoft Corporation, Oracle Corporation, QlikTech International AB, Salesforce, Inc., SAP SE, Sisense, Inc., ThoughtSpot, Inc., TIBCO Software, Inc. and Yellowfin International Pty Ltd (Idera, Inc.).

Our market analysis also entails a section solely dedicated to such major players wherein our analysts provide an insight into the financial statements of all the major players, along with product benchmarking and SWOT analysis.

Key Developments

• In May 2023, TrinityLife Sciences and WhizAI have partnered to provide life sciences companies with AI-powered insights, enabling them to generate and share these insights quickly and easily, enhancing their enterprise reporting platforms for more stakeholders.
• In January 2023, Seerist, Inc. has introduced new capabilities to enhance its augmented analytics solution for threat and security professionals, enabling users to gain contextual intelligence, extract meaning from data noise, and customize the solution for critical areas.

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