The Future of Business: A Dive Into Guided Analytics

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    • KartikMarketing Consultant
      Always look for insight as to how you can better structure data within your business, there's surely a nugget of wisdom out there for you
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    Editor's Note: In the ever-evolving business landscape, guided analytics emerges as a beacon of clarity. Witness how this approach revolutionizes data analysis, providing users with step-by-step guidance to uncover valuable insights. Understand the key differences between guided analytics and self-service business intelligence and explore how organizations can leverage it to drive data-driven decision-making.


    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.

    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.”

    Communication and collaboration: 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.

    Performance Monitoring: 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.

    Data Quality Management: 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.

    Data Sources: The quality and reliability of the data sources used in it are crucial for the accuracy and effectiveness of the process. Data sources include data warehouses, data lakes, and external data sources such as social media feeds, third-party data providers, and public data sources.

    Scalability: As the amount of data being analysed increases, it is important to ensure that the analytics environment can scale to meet the growing demand. Upgrading hardware, increasing storage capacity, or leveraging cloud-based solutions are the key elements for scalability.

    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.

    Flexibility: It should allow for flexibility in the analysis process. Users should be able to customize the analysis to meet their specific needs and goals.

    Transparency: The analysis process should be transparent, with clear explanations of the methods and assumptions used in the analysis.

    Interactivity: It should be interactive, allowing users to explore the data and analyze it in real time.

    Automation: The analysis process should be automated as much as possible, reducing the need for manual intervention and minimizing the potential for errors.

    Scalability: It should be scalable to handle large and complex data sets, while still providing fast and responsive analysis.

    Two sides of Guided Analytics: Advantages and Disadvantages

    Now before we learn to implement guided analytics, let us look at some of it's advantages and disadvantages. It improves the accuracy and reliability of insights by enabling business users to work with high-quality data and analytics models. It has several advantages that makes it an attractive approach to data analytics, some of them are:

    Reduces complexity: By providing a unified interface that integrates with other business applications and processes, guided analytics can streamline workflows which helps in eliminating the need to switch between different tools and systems. It can also help with reducing complexity by simplifying data analysis, streamlining workflows, automating data preparation, providing customizable user experiences, and improving decision-making.

    Increases productivity: With the advantages like, simplified data access and faster decision-making by automating some of the time-consuming tasks enhance productivity. Users can focus extra on strategic tasks, such as interpreting and communicating insights with the help of a user-friendly interface, customization options and fostering a culture of collaboration and innovation.

    Supports data governance: One of the key benefits is of standardizing data which ensures data consistency across the organization and provides a sole source of truth which reduces errors and inconsistencies. Ensuring data security, automating data validation, and enabling auditing and compliance provide security to data and improve the overall quality of data.

    Reduces costs: With guided analytics in the picture, it reduces need for specialized expertise and can automate repetitive and time-consuming tasks involved in data analysis which in turn reduces the need for dedicated data analysts or IT resources. It can also help organizations save money by providing faster analysis and decision-making, better cost control, improved resource allocation and avoiding of costly mistakes.

    Enables predictive analytics: Automated Data Preparation, Interactive Visualization and predictive models help in reducing time and effort and reduce the learning curve for users who are new to predictive analytics. The tools can provide access to machine learning algorithms that can be used to build predictive models and make it easier to deploy predictive models into production environments which helps in accelerating the time-to-value of predictive analytics projects.

    Does Qlik sense makes sense for our business?

    With the Components Like Active Intelligence, Data Sources, Embedded Analytics, and Qdi, Backed With an Associative Engine Can Help Users Convert Their Data Into Insights Seamlessly and in Real-time.

    While guided analytics has numerous benefits but there are 2 sides of a coin. There are also some potential disadvantages to consider:

    Bias and Prejudice: The tools rely on data to provide insights and recommendations, and if the data is biased or reflects preconceived notions or stereotypes, the insights and recommendations generated by the tool may also be biased or prejudiced. This can lead to incorrect or misleading insights and recommendations that can negatively impact decision-making and organizational outcomes.

    Lack of Expertise:  It creates a false sense of security for users who lack expertise in data analytics as users may rely too heavily on the guided aspects of the tool and fail to understand the underlying data, analysis methods, or statistical techniques. Additionally, users who lack expertise may not fully understand the limitations of the tool and may misinterpret or overestimate the accuracy of the results.

    Difficulty of Integration: It can be difficult integrating guided analytics with existing data systems and workflows. Many organizations have complex data architectures and processes in place, and integrating guided analytics tools with these systems can be a challenging and time-consuming process, which can result in delays, increased costs, and frustration among users who may be unable to access the data they need when they need it.

    Limited Scope: While guided analytics tools can be effective at analysing structured data and providing insights based on predefined analysis methods, they may struggle to manage more complex and unstructured data, or to address novel analysis questions that require more advanced analytical techniques.

    Limitations of customization: The tools may not be able to address all the unique needs and requirements of individual users or departments within an organization, which could limit their usefulness in certain contexts which can be especially problematic for organizations that rely heavily on data to inform their decision-making, as it may lead to missed opportunities or incorrect conclusions.

    Data Quality Issues: Data quality problems, such as incomplete or inaccurate data, can compromise the accuracy and reliability of guided analytics insights and can lead to wrong or misleading conclusions. The tools may not always be able to detect or correct data quality issues, which could further exacerbate the problem and may lead to poor decision-making or missed opportunities.

    In summary, while guided analytics has many benefits such as reduced complexity and support for data governance but, organizations should also be aware of the potential disadvantages, including limited flexibility, bias and prejudice, over-reliance on tools and difficulty in integration, etc.

    But how can we mitigate these disadvantages while working with it? Is there an approach that reduces these disadvantages and still provides the same results?

    Yes, there is, Self-service BI. It is a method of delivering data analysis and insights to business users who are not experts in data science or IT and allows users to create reports, and dashboards, and build custom solutions. Self-service BI tools often have user-friendly interfaces and drag-and-drop capabilities, making it easy for non-technical users to create meaningful and interactive reports. This approach saves time and money for the organization while increasing user engagement and promoting data-driven decision-making across the organization. So now let us look at the differences between self-service and guided analytics through Qlik’s Qlik Sense and Qlik view.

    Guided Analytics vs Self-Service BI

    Guided and self-service BI are two approaches to business intelligence that differ in how users interact with data and analytics tools. Guided analytics refers to a more structured approach to data analysis, where users are provided with a pre-defined set of visualizations, reports, and dashboards to guide their analysis. In contrast, self-service BI refers to a more flexible approach where users can explore and analyse data on their own, without relying on pre-defined structures or guided workflows.

    Let us look at some key differences between Guided Analytics (QlikView) and (Qlik Sense):

    User interface: QlikView has a more traditional user interface, with a fixed layout and predefined objects such as charts and tables. Qlik Sense, on the other hand, has a more modern and flexible user interface, allowing users to create their own visualizations and dashboards.

    Data modelling: QlikView has a more complex data modelling process, requiring users to define data relationships upfront. Qlik Sense, on the other hand, uses a more flexible, on-the-fly data association approach, allowing users to connect data sources on the fly.

    Visualization: Qlik Sense has a more modern and intuitive interface, with drag-and-drop capabilities and a more visually appealing design. QlikView, on the other hand, has a more traditional interface that may require more technical knowledge to navigate.

    Collaboration: Qlik Sense allows for better collaboration among team members, with features such as shared bookmarks, commenting, and real-time co-authoring. QlikView, on the other hand, has limited collaboration features, which may make it more difficult for teams to work together on projects.

    How Can We Implement Guided Analysis?

    Guided analytics is a journey, a journey that explores different problems uniquely to reach to its solution with the help of some determined steps along the way. These steps include:

    Identifying the problem and defining the scope: Foremost step to solve any problem it is vital identify the key issues, understand the objectives of the analysis and the key questions that are needed to be answered. This revolves around gathering requirements from stakeholders, conducting research, and identifying any constraints or limitations The scope may involve identifying the relevant data sources, such as databases, APIs, or spreadsheets, and understanding the data structure and format.

    Establishing success criteria: Establishing success criteria involves defining the metrics that will be used to measure the effectiveness of the analysis. Some of the parameters that come under it are accuracy, completeness, timeliness, and relevance. It is important to ensure that the success criteria are aligned with the objectives of the analysis and that they are measurable. For example, if the objective is to improve sales forecasting accuracy, success criteria may include measuring the mean absolute percentage error (MAPE) or the root mean squared error (RMSE) of the forecasts.

    Using visualization and providing context for your analysis: Visualizations are a powerful tool for exploring and understanding data. They can help you identify patterns which others can understand the significance of your findings that might be difficult to see in a table or spreadsheet. One of example of visualization tool is Analytics dashboards are often used in guided analytics to provide users with a real-time view of their data, allowing them to identify trends, patterns, and anomalies that might otherwise go unnoticed. The dashboard provides data visualization through a range of charts, graphs, and tables, making it easy for users to interpret data at a glance and help identify areas for improvement.

    Using iterative analysis and collaborating with others: Data analysis is rarely a one-time process which requires constant collaboration with others which can help you to identify new insights and approaches that you might not have considered on your own. It often requires data quality checks, establishing data ownership and stewardship, and enforcing data privacy and security policies and ensure that your analysis is accurate and dependable.

    Provide apt training and accessible support: Any tool and resource are only useful if users know how to use them effectively. To ensure the full productivity, it is important to provide adequate training and support resources. Having online tutorials, user manuals, training sessions, and help desk support can help users learn how to use the tools and resources effectively and get the most value out of them This can help to maintain user engagement and satisfaction with the tools and resources.

    Industry Use Cases: Guided Analytics

    Guided analytics is widely used in different industries to improve decision-making, gain insights, and optimize business processes. Here are some detailed examples of how it is used in different industries:


    • Clinical trials: Used for ensuring that the trials are conducted efficiently and effectively and to gain guidance on how to design the trial, which variables to measure, and how to analyse the results.
    • Patient monitoring: Guided analytics can be used to monitor patients’ health and progress during treatment and tells when to take action.
    • Supply. chain management: It manages the supply chain for pharmaceutical products, ensuring that the right products are available at the right time and in the right quantities, and mitigating risks.


    • Quality Control: Manufacturing companies can use guided analytics to track defects by product, time period, or location, enabling them to identify patterns that may indicate issues with production processes or materials.
    • Supply Chain Management: It can help with optimizing their supply chain by providing real-time data on inventory levels, delivery times, and supplier performance which will improve efficiency and reduce costs.
    • Energy Management: By monitoring energy consumption and identifying opportunities for optimization, manufacturers can use guided analytics to track energy usage by production.


    • Inventory Management: For making data-driven decisions on what products to stock, how much to stock, and where to stock them.
    • Personalized Product Recommendations: It provides personalized product recommendations based on their purchase history and browsing behaviour which can increase customer satisfaction and drive sales.
    • Store Layout Optimization: To help retailers optimize their store layout for better customer flow and increased sales.

    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.

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