Author: Spiralmantra

What is Augmented Analytics: How will it benefit your business?

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.

What is Predictive Analytics and its importance in a business?

Advanced data analytics has a subfield called Predictive Analytics that makes the use of historical data with statistical modeling, data mining, and machine learning to forecast the upcoming events. Businesses that grow at the same time as big data systems have subfields of data science called predictive and augmented analytics. This occurs because generating predicted insights for bigger, more expansive data sets allow for more data mining operations

Techniques of Predictive Analytics 
Regression Analysis 

Regression is a statistical analytic technique for determining the relationships between variables. Regression makes it simpler to identify patterns in large datasets in order to determine the relationship between inputs. It functions best when applied to continuous data with a known distribution. Finding the link between one or more independent variables and another, such as the effect of price increases on product sales, is a common use of regression analysis.

Decision Trees

Decision trees are classification models that organize data according to discrete factors. This method is most effective when trying to understand how someone makes decisions. The concept is modeled like a tree, where each branch represents a potential course of action, and the leaf of the branch indicates the decision’s consequence. In general, decision trees are easy to understand and work effectively with datasets that include a large number of missing variables.

Neural Networks

Machine learning methods such as neural networks can be useful in predictive analytics modeling exceedingly complex relationships. In essence, they are incredibly potent engines for pattern detection. Neural networks are particularly useful in detecting nonlinear correlations within datasets when there is no well-established mathematical technique for data processing. Neural networks can be used to validate the output of regression models and decision trees.

Benefits of Predictive Analytics in a business

Predictive capabilities can be quite helpful in a variety of business circumstances. It can be used by sales and marketing teams for lead scoring, opportunity scoring, closing time prediction, and numerous other CRM-related scenarios. It can assist manufacturers and retailers in projecting consumer demand, optimizing the distribution network, and investigating the addition of new products to their assortment. It can be used by HR to determine whether an offer will be accepted by candidates and how best to modify compensation and perks to align with the candidate’s beliefs. Also, businesses can utilize it to research costs and alternatives for office space. These are but a handful of the possible situations. 

  • Fraud detection

Predictive analytics tracks every move made on a business network in real time, looking for anomalies that signal fraud and other weaknesses.

  • Predicting conversion and purchase

Companies can use data to forecast a higher possibility of conversion and purchase intent, so they can take steps like retargeting online ads to visitors.

  • Mitigation of risk

Predictive analytics is used in credit scores, insurance claims, and debt collections to evaluate and estimate the probability of future defaults.

  • Enhancement of operations

Predictive analytics models are used by businesses to manage resources, forecast inventory, and run more smoothly.

  • Segmenting customers

Marketers can utilize predictive analytics to make forward-looking decisions and customize content for distinct audiences by segmenting their client base into distinct groups.

  • Forecasting maintenance

Businesses use data to forecast when regular maintenance is needed for their equipment, allowing them to plan it before an issue or malfunction occurs.

Use cases from the predictive analytics in different industry

Predictive analytics can be used for a range of business problems in a variety of industries. Here are some examples of industry use cases that show how decision-making in real-world scenarios can be influenced by predictive analytics.  

  • Banking: To forecast its prospects and clients, financial services use quantitative methods and machine learning. Banks can use this data to respond to inquiries about loan default rates, high- and low-risk consumers, most profitable customers for marketing and resource allocation, and instances of fraudulent expenditure. 
  • Healthcare: In the field of medicine, predictive analytics is utilized to track particular illnesses like sepsis and to identify and manage the treatment of people who are chronically sick. Geisinger Health mined medical information using predictive analytics to discover more about the diagnosis and treatment of sepsis.
  • Human Resources (HR): Businesses may save hiring expenses and boost employee happiness by combining quantitative and qualitative data, which is especially helpful in unstable labor markets.
  • Sales and marketing: Although sales and marketing teams are well-versed in using business intelligence reports to comprehend past sales figures, predictive analytics allows businesses to interact with customers more proactively throughout the customer lifecycle.
  • Supply chain: Predictive analytics is frequently used by businesses to control product inventories and establish price policies. Businesses may meet client demand without overstocking warehouses by using this kind of predictive analysis. Additionally, it helps businesses to evaluate the investment and yield of their products over time. 
What are 5 Real-world examples of predictive analytics?

Amazon suggests products that are likely to meet the demands of its customers based on information about their purchasing patterns. 

Capital One assesses credit risk using big data and machine learning. The business has historically used common datasets, such as an individual’s credit score and credit history. 

Walmart forecasts demand, anticipates inventory needs, and employs artificial intelligence and neural networks to prevent overstocking and item shortages. 

Allstate uses information on an individual driver’s age, gender, and prior driving history to estimate their risk and determine the appropriate premium. Allstate even established a brand-new business named Arity with a focus only on data analytics.  

One excellent example of a utility business using predictive analytics and weather forecasts to anticipate the location and extent of upcoming power outages is PSEG Long Island. The business makes use of the data to allocate staff and resources in advance of major disruptions.

Conclusion: 

Decision making processes can be enhanced by utilizing forecasts of future events generated by predictive analytics. Predictive Analytics is adapted by numerous industries like marketing, retail, healthcare, and finance. Techniques of predictive analytics include neural networks, decision trees, and regression analysis.