Augmented Analytics, one of the technologies revolutionizing data analytics, is radically changing the way organizations derive value from data. Improving business processes by reducing technical complexity, automating data analysis and delivering more accurate insights, this innovative approach provides a significant advantage in today's competitive landscape. With the increasing strategic importance of data in the business world, Augmented Analytics solutions stand out where traditional analytical approaches fall short.
Augmented Analytics is a next-generation analytics approach that automates and enriches data preparation, insight discovery, and sharing processes using artificial intelligence (AI) and machine learning (ML) technologies. The concept was first defined by Gartner in 2017 and expressed as “next-generation analytics that automate and democratize data science and machine learning capabilities.”
In traditional analytical approaches, data preparation, modeling, and interpretation processes are usually carried out manually, requiring technical expertise. Augmented Analytics automates these processes, making it easier for data analysts and data scientists, while allowing non-technical users to benefit from data analytics.
The scope of Augmented Analytics consists of three main components:
This technology increases the efficiency of the entire analytical process, from data preparation to analysis and interpretation, enabling organizations to make faster and more accurate decisions.
Augmented Analytics solutions work with the integration of various advanced technologies. These systems are based on machine learning algorithms, natural language processing (NLP), and natural language generation (NLG) technologies.
The principle of operation of Augmented Analytics can be expressed in the following steps:
Augmented Analytics is an ecosystem made up of a combination of many innovative technologies. At the heart of this ecosystem are the following technologies:
At the heart of Augmented Analytics are artificial intelligence and machine learning algorithms. By detecting complex relationships in large data sets, these algorithms automate data preparation and analysis processes. Different machine learning techniques, such as deep learning, decision trees, random forests, and linear regression, are used in data modeling.
Natural language processing technologies allow users to create queries in natural language and interact with the system. For example, a user can make a query like “Show the change in sales over the past year”, and the system understands this, performing the appropriate analysis.
Natural language production, on the other hand, transforms analytical results into understandable text, enabling even non-technical users to grasp complex analysis results.
Cognitive processing technologies understand the semantic meaning of data, enabling more accurate responses to users' queries to be generated. These technologies improve the accuracy of query results by learning organizations' data models and business terminology.
Augmented Analytics solutions enable more understandable and effective presentation of analysis results using advanced data visualization techniques. These visualizations can be interactive and allow users to conduct in-depth reviews within the data.
According to IDC's “Worldwide Augmented Analytics Software Forecast, 2021-2025” report, the augmented analytics market will expand at a compound annual growth rate of 24.3% through 2025, with a significant portion of this growth due to advances in NLP and NLG technologies.
Augmented Analytics improves data-driven decision-making processes, providing organizations with several advantages:
Automating data preparation and analysis processes enables data analysts and data scientists to work more efficiently. Analyses, which can take days or weeks in traditional methods, can be completed in hours with Augmented Analytics.
Augmented Analytics enables non-technical business users to benefit from data analytics as well. Natural language interfaces and user-friendly designs make data analysis accessible to everyone.
Machine learning algorithms can detect relationships and trends that human analysts may overlook. In this way, more accurate and comprehensive insights can be obtained.
Real-time and accurate insights enable organizations to make more informed decisions. This helps improve business performance and gain competitive advantage.
Automating manual data analysis processes helps organizations reduce costs. In addition, the ability to perform more analysis with fewer technical personnel optimizes human resources costs.
Augmented Analytics creates value with a variety of applications in different industries:
Financial institutions use Augmented Analytics for risk assessment, fraud detection, customer segmentation, and determination of investment strategies. For example, JPMorgan Chase's COIN (Contract Intelligence) platform analyzes credit agreements using artificial intelligence technologies, eliminating 360,000 hours of manual workload per year.
Retail and e-commerce companies use Augmented Analytics to analyze customer behavior, develop product recommendation systems, and make demand forecasts. For example, Walmart uses augmented analytics solutions to analyze customer behavior, thereby optimizing inventory management.
Manufacturing companies use Augmented Analytics for quality control, maintenance planning, and supply chain optimization. For example, Bosch has managed to reduce manufacturing errors by 25% by using augmented analytical solutions in its factories.
Telecommunications companies use Augmented Analytics to optimize network performance, predict customer losses (churn), and improve service quality. Using enhanced analytics solutions, Vodafone has managed to reduce customer losses by 15%.
Healthcare institutions use Augmented Analytics for disease diagnosis, treatment planning, and optimization of health care. Using augmented analytics solutions, Mayo Clinic optimizes patient care processes and reduces the risks of in-hospital infections.
According to Deloitte's “State of AI in the Enterprise” report, the highest return sectors for augmented analytics applications were finance (41%), retail (38%) and manufacturing (35%), respectively.
Despite the advantages offered by Augmented Analytics, organizations can face several challenges when adopting this technology:
The effectiveness of Augmented Analytics solutions depends on the quality of the data used. Incomplete, inconsistent or incorrect data may affect the accuracy of the analysis results. Furthermore, the integration of data from different systems can also create technical challenges.
The adoption of Augmented Analytics may require a cultural shift in organizations. Employees who are accustomed to traditional methods of analysis may resist new technologies, in which case change management becomes critical.
Augmented Analytics solutions often process sensitive data, which can lead to security and privacy concerns. Data protection regulations such as GDPR can restrict organizations' use of data and impose additional compliance requirements.
Technical expertise may be required to install, configure, and maintain Augmented Analytics solutions. Many organizations may find it difficult to find competent staff in this area.
The return on Augmented Analytics investments can be difficult to measure. In particular, calculating the financial value of indirect benefits (such as improving decision-making processes) can be complicated.
In the field of Augmented Analytics, several trends are expected to stand out in the coming years:
The integration of generative artificial intelligence (GenAI) technologies with Augmented Analytics solutions will enrich the user experience and enable more natural interactions. Integration of ChatGPT-like models into analytics platforms will allow users to understand their queries in more detail and generate more comprehensive responses.
Integration of Augmented Analytics solutions with edge computing technologies will strengthen real-time analytics capabilities. In this way, it will be possible to analyze data from IoT devices instantly and make quick decisions.
Augmented Analytics solutions will deliver stronger predictive analytics capabilities in the future. In this way, organizations will not only be able to analyze historical data, but also predict future trends and risks.
Augmented Analytics solutions will become more user-friendly in the future, thereby appealing to a wider audience of users. The proliferation of low-code/no-code platforms that allow analysis without writing code will accelerate this trend.
Artificial intelligence ethics and explainable AI will become important in Augmented Analytics solutions. Organizations should be able to explain how the algorithms they use work and what factors they make decisions based on.
Augmented Analytics is radically changing the way organizations derive value from data, as a revolutionary approach to data analytics. It automates data preparation, analysis and interpretation processes with the integration of artificial intelligence and machine learning technologies, enabling organizations to make faster and more accurate decisions. This innovative technology, which replaces traditional analytical approaches, democratizes data analysis, making it possible for non-technical users to derive value from the data as well.
Despite the advantages offered by Augmented Analytics, organizations can face various challenges when adopting this technology. Issues such as data quality, organizational adaptation, security, and technical expertise must be carefully addressed for a successful Augmented Analytics strategy.
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Sources:
GPT-4.5 is the latest artificial intelligence language model developed by OpenAI. This model, which comes as a continuation of GPT-4, aims to make the user experience more efficient and intelligent by taking its natural language processing (NLP) capabilities even further.
AI Agents are artificial intelligence systems that can autonomously navigate complex workflows in digital environments using large language models. These systems possess the capability to plan, execute actions, and utilize various digital tools far beyond the information retrieval and content generation capabilities of traditional chatbots.
Autoregressive models are a powerful method used especially in artificial intelligence and time series analysis. These models are developed to predict future values using historical data.
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