Augmented analytics, a term coined by Gartner, refers to the use of enabling technologies such as machine learning and artificial intelligence to assist with data preparation, insight generation, and insight explanation to augment how people explore and analyze data in analytics and business intelligence platforms. It also augments the expert and citizen data scientists by automating many aspects of data science, machine learning, and AI model development, management, and deployment.
Cloud computing, on the other hand, is the delivery of different services through the Internet. These resources include tools and applications like data storage, servers, databases, networking, and software. Instead of keeping files on a proprietary hard drive or local storage device, cloud-based storage makes it possible to save them to a remote database. As long as an electronic device has access to the web, it has access to the data and the software programs to run it.
Definition of Augmented Analytics in Cloud Computing
Augmented analytics in cloud computing refers to the application of AI and machine learning to automate data preparation and enable data sharing and business intelligence. This is achieved by augmenting human intelligence, enhancing decision making, and significantly speeding up data-driven processes. Augmented analytics can help organizations to transform raw data into actionable insights quickly and efficiently.
Augmented analytics in cloud computing is designed to handle data that is too complex for traditional data processing software. It provides advanced capabilities for processing and analyzing large, complex data sets with a user-friendly interface, making it easier for non-technical users to generate and understand advanced analytics results.
Components of Augmented Analytics
Augmented analytics consists of several key components, including data preparation, data discovery, insight generation, and insight explanation. Data preparation involves collecting, cleaning, and consolidating data from various sources. Data discovery refers to the process of identifying patterns and relationships in the data. Insight generation is the process of using algorithms and machine learning models to derive meaningful insights from the data. Insight explanation involves interpreting the results and explaining them in a way that is easy for users to understand.
These components work together to provide a comprehensive view of the data, enabling users to make informed decisions based on the insights generated. The use of AI and machine learning in augmented analytics allows for more accurate and timely insights, improving the efficiency and effectiveness of decision-making processes.
History of Augmented Analytics
The concept of augmented analytics was first introduced by Gartner in 2017 as a future evolution of data analytics. The idea was to use AI and machine learning to automate data preparation and analysis, freeing up human analysts to focus on more strategic tasks. Since then, the field of augmented analytics has grown rapidly, with many companies now offering augmented analytics solutions.
The development of augmented analytics has been driven by the increasing complexity of data and the need for more efficient ways to analyze it. As the volume and variety of data continue to grow, traditional data analysis methods are no longer sufficient. Augmented analytics provides a solution to this problem by using AI and machine learning to automate the analysis process, making it possible to handle larger and more complex data sets.
Evolution of Augmented Analytics
The evolution of augmented analytics has been marked by several key advancements. The first is the development of AI and machine learning algorithms capable of analyzing large and complex data sets. These algorithms have made it possible to automate many of the tasks traditionally performed by human analysts, such as data cleaning and preparation.
The second major advancement is the development of natural language processing (NLP) technology. NLP allows users to interact with the analytics system using natural language, making it easier for non-technical users to generate and understand analytics results. The third major advancement is the development of automated insight generation and explanation technologies, which use AI to generate and explain insights from the data.
Use Cases of Augmented Analytics in Cloud Computing
Augmented analytics in cloud computing can be used in a variety of ways to improve business operations and decision making. One common use case is in the area of business intelligence. By automating data preparation and analysis, augmented analytics can help businesses to gain a deeper understanding of their operations and make more informed decisions.
Another use case is in the field of data science. Augmented analytics can automate many of the tasks involved in developing and deploying machine learning models, freeing up data scientists to focus on more strategic tasks. This can significantly speed up the process of developing and deploying machine learning models, enabling businesses to gain insights from their data more quickly.
Examples of Use Cases
One specific example of how augmented analytics can be used in cloud computing is in the area of customer analytics. By using augmented analytics, businesses can analyze large volumes of customer data to identify patterns and trends. This can help businesses to understand their customers' behavior and preferences, enabling them to make more informed marketing and sales decisions.
Another example is in the area of supply chain management. Augmented analytics can be used to analyze supply chain data to identify inefficiencies and opportunities for improvement. This can help businesses to optimize their supply chain operations, reducing costs and improving efficiency.
Benefits of Augmented Analytics in Cloud Computing
There are several benefits to using augmented analytics in cloud computing. One of the main benefits is the ability to handle large and complex data sets. Traditional data analysis methods can struggle with the volume and variety of data generated by modern businesses. Augmented analytics, on the other hand, is designed to handle this complexity, providing advanced capabilities for processing and analyzing large data sets.
Another benefit is the speed and efficiency of data analysis. By automating many of the tasks involved in data analysis, augmented analytics can significantly speed up the process of generating insights from data. This can help businesses to make more informed decisions more quickly, improving their agility and competitiveness.
Increased Accessibility
One of the key benefits of augmented analytics is its accessibility. The use of natural language processing and automated insight generation and explanation technologies makes it easier for non-technical users to generate and understand analytics results. This can help to democratize data analysis, enabling more people within an organization to make data-driven decisions.
Increased accessibility also means that businesses can make better use of their data. With more people able to access and understand the data, businesses can gain a more comprehensive understanding of their operations and make more informed decisions.
Challenges of Augmented Analytics in Cloud Computing
Despite its many benefits, there are also some challenges associated with using augmented analytics in cloud computing. One of the main challenges is the complexity of the technology. While augmented analytics can handle complex data, the technology itself can be complex to implement and manage. This can make it difficult for businesses without a strong technical background to make full use of augmented analytics.
Another challenge is the need for high-quality data. For augmented analytics to be effective, it needs to be fed with high-quality, clean data. If the data is not clean or is of poor quality, the insights generated by the analytics may be inaccurate or misleading.
Security Concerns
Security is another major concern when using augmented analytics in cloud computing. Because the data is stored in the cloud, it is potentially vulnerable to cyber attacks. Businesses need to ensure that they have robust security measures in place to protect their data.
Privacy is another concern. Businesses need to ensure that they are complying with all relevant data protection regulations when using augmented analytics. This includes ensuring that they have the necessary permissions to collect and analyze the data, and that the data is stored and processed in a way that respects the privacy of the individuals it relates to.
Future of Augmented Analytics in Cloud Computing
The future of augmented analytics in cloud computing looks promising. With the continued development of AI and machine learning technologies, we can expect to see further advancements in the capabilities of augmented analytics. This includes the ability to handle even larger and more complex data sets, and the development of more sophisticated algorithms for generating insights from the data.
As businesses continue to generate more and more data, the demand for tools to analyze this data will only increase. Augmented analytics provides a powerful solution to this challenge, enabling businesses to make sense of their data and make more informed decisions. As such, we can expect to see the use of augmented analytics in cloud computing continue to grow in the coming years.