Empower your organization with the right analytics approach—Guided Analytics or Self-Service Business Intelligence (BI)—to unlock the true potential of your data. Discover the benefits and find your perfect fit, whether you prefer expert-guided insights or self-exploration, enabling your team to make data-driven decisions and drive transformative outcomes.

1. The Future of Business
A Dive Into Guided
Analytics

2. Through businesses’ haze, guided analytics came in the darkest days and navigating
businesses in this windy maze.
Guided analytics is a data analytics approach that provides users with step-by-step
guidance to help them navigate complex data analysis tasks. With this, users are presented
with a series of predefined steps or workflows that guide them through the analysis
process, making it easier for them to identify trends, patterns, and anomalies in the data.
This approach is especially useful for organizations that want to democratize data and
empower non-technical users to make data-driven decisions. The goal is to empower users
to conduct sophisticated analyses without requiring them to have specialized technical
skills or knowledge.
With scratching the surface on guided analytics now the foremost question that comes to
mind is what is necessary for a group of data scientists to pool their knowledge and create
a collaborative application that is interactive and perhaps even adaptive. Applications that
provide precisely the appropriate amount of direction and interactivity to business users?
Generally, such technology needs a very balanced and structured environment. A
structured environment requires a few properties, so let us start off with an environment
of guided analytics.
Introduction

3. The insights derived from the guided analytics
process need to be communicated effectively to
stakeholders. It is required for presenting the
insights in a clear and visually compelling manner
using data visualization tools.
01
Communication and collaboration
To ensure that the insights generated from the data are
accurate and dependable, it is important to have a data
quality management process in place. This may involve
data validation, data profiling, and data cleansing
activities. Overall, the environment surrounding guided
analytics is dynamic and complex and requires a range of
skills and expertise to effectively derive insights from data.
03
Data Quality Management
To guarantee the analytics environment is
performing optimally, it is important to monitor
key performance metrics, such as query response
time, resource utilization, and system availability.
02
Performance Monitoring
The quality and reliability of the data sources used
in it are crucial for the accuracy and effectiveness
of the process. Data sources
04
Data Sources
Environment Surrounding The
Guided Analytics
“Creating an environment for guided analytics involves careful planning and consideration of the various components
required. By focusing on the following key components organizations can create a powerful analytics environment that
enables users to gain valuable insights from their data.”

4. It should allow for flexibility in the analysis process.
Users should be able to customize the analysis to
meet their specific needs and goals.
01
Flexibility
It should be interactive, allowing users to explore the data
and analyze it in real time.
03
Interactivity:
The analysis process should be transparent, with
clear explanations of the methods and
assumptions used in the analysis.
02
Transparency
The analysis process should be automated as
much as possible, reducing the need for manual
intervention and minimizing the potential for
errors.
04
Automation
Guided Analytics Principles
Developing an environment for guided analytics is one thing but to remain in that environment, it is a completely different
task. Here are a few principles for implementation can follow that can help organizations to remain in the environment and
these principles also help organizations to drive data-driven decision-making, and increase operational efficiency.
It should be scalable to handle large and complex
data sets, while still providing fast and responsive
analysis.
05
Scalability

5. Two sides of Guided Analytics:
Advantages and Disadvantages
Adv Disadv
Reduces complexity Bias and Prejudice
Increases productivity Lack of Expertise
Supports data governance Difficulty of Integration
Reduces costs Limited Scope
Enables predictive analytics
Limitations of
customization

6. Future of Guided Analytics
The future of guided analytics is just like the starry night sky, infinite! As data continues to grow in volume and
complexity, and as organizations increasingly rely on data to inform their decisions, and is likely to become even
more important.
Guided analytics tools are likely to become more automated, with machine learning algorithms and artificial
intelligence increasingly used to analyse data and generate insights which will make it easier for non-technical
users to access and analyse data and could lead to more accurate and timely insights.
It is likely to continue to grow and evolve and so will the self-service BI, which is expected to become more
sophisticated, user-friendly, and accessible, thanks to advances in technology and changing user needs. With the
increasing availability of data and the growing need for organizations to be data-driven, self-service BI tools are set
to become more widely used in the coming years, with an ever-expanding user base.