Meaning of Guided Analytics
Analytics guided by business users based on their needs and use cases is an approach to analytics. It allows data analysts and business users to create visualizations that serve as decision-making tools. You can use guided analytics directly or as blueprints for building your own custom applications.
Business managers and traditional BI approaches are struggling to keep up with today's ever-changing data environment, enhance efficiency, generate ROI, and boost business value. Organizations must bridge the gap between data scientists, business analysts, and the machines that do the work in the middle in order to maximize the value of data, predict future outcomes, and make better business decisions.
The benefits of guided analytics
With guided analytics, end-users are able to quickly comprehend insights and information presented in dashboards. Dashboards must be informative and user-friendly so that anyone can dive in and understand the story quickly. Providing consumers with multiple options for slicing and documenting data allows them to gain a deeper understanding of the data.
Guided analytics also helps keep teams on the same page by centralizing all business data in a central place where everyone can access it.
As a standalone application or embedded analytics product, guided analytics can also be used to design apps. An embedded analytics product integrates real-time reporting, interactive data visualization, and/or advanced analytics directly into an application's interface, including AI and machine learning.
Incorporating analytics into your apps has many benefits, one of which is that you save your developers' time and effort on developing analytics from scratch for each application.
Additionally, guided analytics enables users to explore the entire data set in a secure environment, boosting their confidence to extract the actionable insights they need to drive their decision-making.
Use cases for guided analytics
Guided Analytics in the Banking Industry
In the banking industry, data analytics help banks consolidate internal and external customer data to build a predictive profile of each customer. As a result, banks are able to identify potential risks associated with lending money to customers with poor credit scores. Additionally, banks use analytics to understand how their consumers use their accounts and identify trends that can help them create and maintain user-friendly services.
Guided Analytics in the IT Industry
Financial analytics can reveal a company's performance, predict financial situations based on a variety of scenarios laid out for the system to analyze, and much more in the financial sector. How can data analytics benefit the IT industry?
By bridging the gap between data, insights, and action, IT companies can better serve each of us in today's modern technology world. By leveraging real-time data insights, IT departments can make better, data-driven decisions to keep their services and offerings competitive by understanding trends in operations.
Guided Analytics in the Manufacturing Industry
Manufacturing and business processes hold tremendous potential for business development. Manufacturing analytics, including machine learning algorithms, advanced analytics, and other data analytics methods and tools, present manufacturers with a wide range of opportunities. The objectives include but are not limited to, preventative maintenance to reduce equipment maintenance expenses, maximizing equipment effectiveness, improving logistics for customer service, automating machine operations to minimize downtime, and increasing sales and marketing accuracy.