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A Step-by-Step Tutorial on Building a Custom Business Intelligence Dashboard

  |   Business Intelligence

A Step-by-Step Tutorial on Building a Custom Business Intelligence Dashboard

Harnessing the vast amount of data available has become paramount for informed decision-making and strategic planning in the current fast-paced landscape of modern business. Business Intelligence (BI) revolves around converting raw data into actionable insights; in essence, it empowers organizations to transform data into meaningful information, offering a comprehensive view of their operations. Today, as businesses navigate through the complexities of the digital age, the role of BI has evolved to be more crucial than ever. At the heart of this transformative journey lies the indispensable tool – the dashboard. These visually intuitive interfaces are the bridge between raw data and actionable data insights, providing decision-makers with key performance indicators.

In this blog post, we embark on a hands-on exploration of the step-by-step process of creating a custom Business Intelligence Dashboard. As we delve into the different parts of this tutorial, such as:

– Step 1: Define Objectives and Key Metrics

– Step 2: Choose a BI Tool

– Step 3: Data Collection and Integration

– Step 4: Designing the dashboard layout

– Step 5: Testing and Feedback

we will unravel the art of transforming raw data into digital insights, empowering you to craft a dashboard tailored to your business needs.

Building your custom Business Intelligence (BI) dashboard requires a solid foundation, and that begins with clearly defining its objectives. Take a moment to pinpoint the specific goals your organization aims to achieve through the dashboard. Whether it involves optimizing sales performance, enhancing customer satisfaction, or streamlining operational processes, having a clear vision will serve as a guiding force for the subsequent steps.

In our tutorial, we will focus on crafting a straightforward sales dashboard, delving into understanding the extent of our sales, the products involved, and the customers we are reaching. This tailored approach ensures that the BI dashboard precisely aligns with the specific needs of our Sales Team. In collaboration with the Sales Team, we have identified four main Key Performance Indicators (KPIs):

1

Net Sales Value

2

Number of Orders

3

Average Order Value

4

Number of Customers

Moreover, the Sales Team expresses the need to analyze sales proportions of products and the geographical distribution of our customers.

There is a myriad of powerful BI tools available in the market, each offering unique features to cater to diverse business needs. Notable options include Tableau, renowned for its robust visualization capabilities; Power BI, a Microsoft product excelling in data connectivity and sharing; and Looker, lauded for its intuitive interface and integration with Google Cloud services. It is essential to carefully weigh specific criteria when selecting the most suitable BI tool for your organization. Factors such as the complexity of your data, budget constraints, and technical requirements should be considered. Tailoring your BI tool choice to your organization’s unique context is crucial, ensuring that the selected tool not only meets your current needs but also scales with your evolving business requirements. 

In our tutorial, we’ve chosen Looker as the BI tool of preference, leveraging its seamless integration with the Google Cloud. This decision aligns with the company’s existing use of Google Cloud and its extensive suite of products, ensuring a cohesive and efficient workflow.

Another crucial aspect of Business Intelligence involves not only identifying KPIs and selecting suitable BI tools but also identifying and leveraging data from relevant sources within the organization. This step involves a comprehensive assessment of all potential data streams, including internal databases, cloud services, spreadsheets, and third-party applications. Understanding the diverse sources of data is essential for capturing a holistic view of business operations. Once identified, the next imperative task is data cleaning and transformation. Raw data often contains inconsistencies, errors, and redundancies that can compromise its integrity and usability. By implementing robust data cleaning and transformation processes, organizations can ensure that their data is accurate, consistent, and actionable. This involves tasks such as removing duplicates, standardizing formats, correcting errors, and reconciling discrepancies. The importance of these processes cannot be overstated; clean and well-structured data forms the foundation of reliable insights and informed decision-making. Without proper cleaning and transformation, data inaccuracies can lead to flawed analyses, misguided strategies, and missed opportunities. Furthermore, after having the data extracted and transformed, you will need a proper storage solution. The most common data storage solution for Business Intelligence projects is a Data Warehouse.

Data Warehouse

A Data Warehouse serves as a centralized repository for structured and organized data, optimized for analytical querying and reporting. It allows businesses to store large volumes of data from various sources in a standardized format, facilitating efficient data analysis and decision-making processes.

Many Business Intelligence (BI) tools offer predefined data connectors that allow users to extract data directly from various data sources, such as databases, APIs, and cloud services. These connectors streamline the data integration process and provide the flexibility to clean and transform the data within the BI tool itself, without the need for separate storage. However, whether to opt for a BI structure with a separate process for data extraction, transformation, and storage or to perform these tasks within the BI tool is indeed a business decision. Each approach has its own set of advantages and disadvantages.

In our tutorial, the company is using Big Query, a Google Cloud Data Warehouse solution, to store their data. The company’s data is structured in dimension tables and fact tables. We will be using a view which is build on top of the customer dimension table as well as the sales fact table.  

Dimension and Fact tables

Dimension tables contain descriptive attributes that provide context for the measures stored in fact tables. These attributes typically represent the who, what, where, when, and how aspects of business operations. For example, in a sales context, dimension tables might include attributes such as product, customer, time, location, and salesperson. Dimension tables are often relatively small compared to fact tables and may contain hierarchical relationships.

Fact tables store quantitative data, often referred to as measures or metrics, that represent the business events or transactions being analyzed. These measures are typically numeric and can be aggregated to provide insights into business performance. Fact tables also contain foreign keys that link to dimension tables, providing the context necessary to interpret the measures. Examples of measures in a sales fact table might include sales revenue, quantity sold, discount amount, and profit.

Once you have collected, cleaned, and prepared your data, you are ready to begin building the dashboard. User Interface Design is a critical aspect of creating an effective Business Intelligence (BI) dashboard. Designing an intuitive and user-friendly layout enhances user experience and facilitates easier data interpretation. Following dashboard design principles, such as simplicity, clarity, and consistency, is paramount. When designing the layout, it’s essential to prioritize the most important information and arrange widgets in a logical flow. Place key metrics and KPIs prominently at the top, ensuring they are easily visible at a glance. Group related widgets together to provide context and facilitate comparison. Additionally, consider the use of color, font size, and spacing to highlight important elements and guide user attention. For optimal impact, strategically place visualizations and widgets based on their importance and relevance to the user’s workflow. Centralize critical data in the main area of the dashboard, while secondary information can be positioned in peripheral areas. Additionally, incorporating interactive features is key to enhancing the usability and effectiveness of a Business Intelligence (BI) dashboard. Interactive elements such as filters and drill-downs empower users to explore data from multiple perspectives and extract deeper insights.

Step 1: Create a blank report

Step 2: Connect the data source – in our tutorial’s case it is a View in BigQuery

Now you are able to see a blank report canvas with your connected data source on the right hand side.

Step 3: Rename the report and start building the dashboard

At the very top of the dashboard, we will add the company’s logo and interactive elements such as a currency switcher and a date range control. You can find all interactive widgets under ‘Add a control’ in the top toolbar of the dashboard building interface.

Just under the logo and the interactive elements, we will place the KPIs. For the KPIs, we will be using simple scorecards. You can find scorecards and any other type of charts under ‘Add a chart’ in the top toolbar of the dashboard building interface.

In the middle of the dashboard, we will add some charts and graphs to provide more detailed insights about the sales. For example, we’ll include data on sales and orders per day, the proportion of products contributing to the sales, customer locations, and more.

Quality assurance is a crucial step in the development of a Business Intelligence (BI) dashboard, ensuring that it meets the highest standards of accuracy and functionality. Thorough testing of the dashboard is essential to identify and rectify any potential issues or discrepancies in data visualization, interaction, or performance. This process involves rigorous validation of data sources, verification of calculations, and examination of visualizations to confirm their accuracy and alignment with business objectives. Furthermore, gathering feedback from users and iterating on the dashboard design based on real-world usage is integral to its continuous improvement. Encouraging users to provide feedback enables you to gain valuable insights into usability issues, feature requests, and areas for enhancement. By iteratively incorporating user feedback, you can refine the dashboard to better meet user needs, enhance usability, and drive maximum value from their BI initiatives.

In conclusion, building a custom Business Intelligence (BI) dashboard involves a series of key steps that lead to a powerful tool for data-driven decision-making. Throughout this tutorial, we’ve explored the fundamental aspects of creating an effective BI dashboard, from defining objectives and selecting appropriate BI tools to identifying and gathering relevant data sources. We have emphasized the importance of thoughtful dashboard design, incorporating interactive features, and ensuring data accuracy and consistency. By following these steps, organizations can harness the full potential of their data to gain valuable insights and drive strategic initiatives. However, the journey doesn’t end with the creation of the dashboard; continuous improvement is essential. As businesses evolve, so too must their BI dashboards. Therefore, we encourage ongoing optimization and adaptation to evolving business needs. Regularly gather feedback from users, monitor dashboard performance, and iterate on design and functionality to ensure that the dashboard remains aligned with organizational objectives and delivers maximum value over time. By embracing a culture of continuous improvement, organizations can leverage their BI dashboards as dynamic tools for informed decision-making.

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