Organizations are constantly searching for fresh approaches to enhance their data analysis processes as the area of data analytics continues to grow. In the field of analytics, augmented analytics is a phrase that has recently gained popularity.
By fusing traditional data analytics methods with the capabilities of artificial intelligence (AI) and machine learning (ML), this emerging field of analytics promises to completely transform how we evaluate data.
What precisely is it, therefore, and how does it function in real life?
An important turning point in the rapidly developing field of data analytics was the introduction of augmented analytics and predictive analytics. It was about taking the whole process to a new level, where machines could help humans produce better insights with less work, rather than just crunching information and making pretty data charts. The shift from traditional to augmented analytics is similar to using calculators instead of manual computations to save time and money and speed up decision-making. This shift has been nothing short of revolutionary, particularly given the exponential growth of data in today’s world.
Augmented Analytics is defined by Gartner as “the use of enabling technologies such as machine learning and AI to assist with data preparation, insight generation and insight explanation to augment how people explore and analyze data in analytics and BI platforms” .
It facilitates the automation of key analytics procedures and the streamlining of data preparation, insight discovery, and dissemination. For companies that rely heavily on data, it’s revolutionary since it makes data mapping possible across the entire organization with minimal need for specialized analytics knowledge.
What is the relationship between Machine Learning, Artificial Intelligence, and Augmented Analytics?
In the field of technology and data analysis, terminology like machine learning, artificial intelligence (AI), and augmented analytics are often used interchangeably. Although these technologies are different, they are connected and frequently utilized in tandem to produce more precise and practical data insights.
Let us first look at each term’s definition. Through the use of data analysis techniques like machine learning, computers are able to continuously increase their capacity for result prediction. The goal of the computer science discipline of artificial intelligence is to build machines that can mimic human intelligence. Conversely, augmented analytics uses a combination of machine learning and artificial intelligence to extract useful information from data.
Although machine learning and artificial intelligence (AI) can be utilized separately, their combination can produce more accurate and practical insights. AI can be used, for instance, to find patterns in data, and machine learning can be used to forecast results based on these patterns.
To improve the effectiveness and efficiency of data analysis, augmented analytics integrates machine learning and artificial intelligence approaches. After identifying patterns in the data using machine learning algorithms, the technology applies artificial intelligence approaches to produce tailored and useful insights. As a result, businesses are able to make more strategic choices.
The Advantages of Augmented Analytics for Business Development
Integrating augmented analytics into the data-driven process might be advantageous for businesses. Among the advantages are:
Better Ability to Make Decisions: Through the use of augmented analytics tools, companies can discover the why behind the what. These products’ AI and ML capabilities allow you to examine your company’s performance in greater detail and pinpoint the underlying reasons of particular outcomes. For instance, you can speak with an augmented data analysis tool to learn why your sales decreased by 11% in the first quarter. You can gain a better picture, comprehend the root of the problem, and make more informed judgments with this skill.
Savings of Time and Resources: You can build a feature to automate data preparation and other procedures using enhanced data analysis. This method gives data analysts more time to concentrate on other important tasks. Additionally, when you have a project involving a lot of data, it removes the need to hire additional specialists or provide team members overtime compensation. In the end, you enhance company-wide collaboration, increase productivity, and save time, all of which contribute to better product development processes. Long-term revenue increases as a result of timely and effective service delivery.
Quicker Development of New Products: Development teams can quickly find relationships between data from various sources by using augmented analytics. By extracting knowledge from these connections, they can considerably shorten the time it takes to design a new product. The augmented analytics methodology is faster and lets you test more modeling strategies than standard modeling methods. In a similar vein, it gives you the freedom to inquire about development data in more ways than would have been feasible using conventional techniques.
Increased User Accessibility to Analytics: Generally speaking, data analysis calls for some education and expertise as a data analyst. The requirements slow down access to insights for the entire organization and establish a dependency on the data team. Augmented analytics, on the other hand, can democratize data access by making it simple for departments like development, marketing, and sales to draw conclusions from data. Instead of writing SQL queries, users can write questions in plain English and receive results in simple language regarding various data performances. This indicates that the results are simple to comprehend, and if you would need a clearer explanation, just ask.
Augmented Analytics and Business Intelligence (BI)
Two tools that assist businesses in deriving meaningful conclusions from data are augmented analytics and business intelligence (BI). Despite the frequent overlap in usage, there are several significant distinctions between these phrases.
Business intelligence (BI) is a conventional method of data analysis that gathers, examines, and presents data graphically using various tools and approaches. Large datasets can be mined for valuable information using business intelligence (BI) technologies, which helps organizations make better decisions.
However, augmented analytics is a more sophisticated method of data analysis that automates the process and offers individualized insights by fusing approaches from machine learning (ML) and artificial intelligence (AI). The system employs AI methods to produce insights that can be put into practice after using ML algorithms to find patterns in data.
Even though BI can offer insightful information, data analysis is frequently done by hand and can take some time to complete. Furthermore, augmented analytics is more effective than business intelligence (BI) at spotting patterns and trends in big datasets.
On the other hand, Augmented Analytics optimizes the utilization of the company’s time and resources by automating a large portion of the data processing process with machine learning techniques. Moreover, the technology has the ability to recognize distinct trends and offer customized insights that are exclusive to the organization in issue.
Tools for augmented analytics
The market is now filled with a wide variety of augmented analytics products. Among the well-known instances are:
Tableau Tableau is a program for data visualization with additional analytical features. Advanced predictive analytics tools are provided, and users can query data using conversational language thanks to natural language processing.
For instance, Tableau’s Explain Data function automatically analyzes data and offers insights and explanations using machine learning.
Power BI for Microsoft Recently, Microsoft Power BI, a business intelligence tool, gained capabilities for augmented analytics. With the help of its AI-driven insights function, customers can instantly spot anomalies and trends in their data.
Because of its interaction with Azure Machine Learning, it also provides sophisticated predictive analytics capabilities.
Qlikview Qlikview is a tool for data analytics and visualization that may be used for augmented analytics. It offers machine learning-powered insights and enables users to ask inquiries in simple English using natural language processing.
For instance, its AI-powered Insight Advisor Chat analyzes data and makes recommendations for more analysis utilizing conversational analytics.
Cognos Analytics from IBM Predictive modeling, natural language querying, and automated data preparation are examples of enhanced analytics features included in IBM Cognos Analytics, a business intelligence and data analytics application.
Additionally, it provides sophisticated insights and improves user experience with AI technologies like Watson Assistant.
Boost Business Growth with Spiral Mantra’s data driven solutions
Analytics is now more important than ever and demands faster access to richer information. Conventional approaches fall short in this regard, but firms can gain insight into the “why” behind data performance by utilizing augmented analytics solutions, which go beyond the “what.” With Spiral Mantra, begin delving deeper into data analysis and producing insightful findings for expansion. Experience AI-powered, individualized data analytics solutions by connecting with us.