Business Analytics and Visualization

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It has been a while since I posted a blog article.  Things have been busy and unfortunately my blog has suffered.  Not only have I been busy working in the Microsoft Dynamics 365 and Power Platform arena, but I have also been teaching at a local university.  This past semester I taught Business Analytics and Visualization.  It was a fun course to teach and I was able to introduce business students to the power of analytics using both Power BI and Tableau.  The text for the course was Introduction to Business Analytics, 1st Edition by Vernon Richardson & Marci Watson.  The text did a great job of covering the types of analytics and the mindset and approach you should follow as a business analyst.  I recently used some of this material in a presentation at an FPnA conference, and it was well received, so I thought I would put the thoughts together here for everyone to read.

In today's data-driven world, the ability to transform raw data into meaningful insights is crucial for businesses.  This blog post summarizes the key takeaways from both teaching at the university level as well as working with many different clients.

In the ever-evolving landscape of business, the ability to transform raw data into actionable insights is paramount. Business analytics and data visualization are two critical components that enable organizations to make informed decisions, optimize performance, and drive growth. This blog explores the significance of these elements and provides insights into how businesses can leverage them effectively.

The Importance of Business Analytics

Business analytics involves the systematic analysis of data to uncover patterns, correlations, and trends that can inform strategic decisions. It encompasses various types of analytics, including descriptive, diagnostic, predictive, prescriptive, and adaptive analytics. Each type serves a unique purpose:



  • Descriptive Analytics: This type answers the question, "What happened?" by summarizing historical data to identify trends and patterns.
  • Diagnostic Analytics: This type delves into the reasons behind past outcomes, answering the question, "Why did it happen?"
  • Predictive Analytics: This type forecasts future outcomes based on historical data, addressing the question, "What is likely to happen?"
  • Prescriptive Analytics: This type provides recommendations for actions to achieve desired outcomes, answering the question, "What should we do?"
  • Adaptive Analytics: This type uses artificial intelligence to continuously learn and adapt to changes, addressing the question, "How can we continuously improve?"

The Role of Data Visualization

Data visualization is the graphical representation of data and information. It plays a crucial role in making complex data more accessible, understandable, and actionable. Effective data visualization can transform raw data into meaningful insights by highlighting key trends, patterns, and outliers. Here are some key principles and techniques for creating impactful visualizations:

  • Understand Your Audience: Tailor your visualizations to the needs and preferences of your audience. Consider their level of expertise, the context in which they will use the data, and the specific insights they seek.
  • Choose the Right Visualization Type: Different types of data and analysis require different visualization techniques. For example, bar charts are ideal for comparisons, while line charts are effective for showing trends over time.
  • Design for Clarity: Use clear and concise labels, logical ordering, and appropriate color contrasts to ensure your visualizations are easy to interpret. Avoid clutter and focus on the most important information.
  • Tell a Story: Data visualization is not just about presenting numbers; it's about telling a story. Use your visualizations to guide your audience through the data, highlighting key insights and providing context.

The SOAR Analytics Model

The SOAR analytics model is a structured approach to business analytics that helps organizations systematically address their business questions. SOAR stands for Specify the Question, Obtain the Data, Analyze the Data, and Report the Results. Let's delve into each step:


  • Specify the Question: This step involves clearly defining the business question that needs to be answered. Different questions lead to different types of analytics. For example, descriptive analytics answers "What happened?", while predictive analytics addresses "What is likely to happen in the future?"  Specifying the question ensures that the analysis is focused and relevant.
  • Obtain the Data: Once the question is specified, the next step is to gather the relevant data. This involves identifying which data is available, which data needs to be collected, and ensuring the data is clean and reliable. The quality of the data is crucial for accurate analysis.
  • Analyze the Data: This step involves applying appropriate data analytic techniques to interpret the data and uncover insights. Different types of analytics are used depending on the question. For example, diagnostic analytics might be used to understand why a particular event occurred, while prescriptive analytics might provide recommendations for future actions.
  • Report the Results: The final step is to communicate the findings in a clear and actionable manner. This involves creating static or dynamic visualizations, reports, graphs, and dashboards that effectively convey the insightsgo. The goal is to ensure that the results are easily understood and can inform decision-making.

Techniques for Exploratory and Explanatory Analysis

Data visualization techniques can be broadly categorized into exploratory and explanatory analysis:

  • Exploratory Analysis: This involves using visualizations to explore and understand the data. Techniques such as scatter plots, heat maps, and histograms help identify relationships, distributions, and patterns within the data.
  • Explanatory Analysis: This involves using visualizations to communicate specific insights and findings. Techniques such as bar charts, line charts, and pie charts are used to present data in a clear and compelling way, often with the goal of influencing decision-making.

Best Practices for Data Visualization

To create effective data visualizations, consider the following best practices:

  • Apply Fundamental Design Principles: Use design principles such as alignment, contrast, and balance to create visually appealing and easy-to-understand visualizations.
  • Use Typography and Iconography: Choose professional fonts and use icons to enhance the readability and visual appeal of your visualizations.
  • Leverage Technology: Utilize tools like Power BI and Tableau to create dynamic and interactive visualizations that can handle large datasets and provide real-time insights.

Conclusion

Business analytics and data visualization are powerful tools that can transform the way organizations operate. By leveraging these techniques, businesses can gain deeper insights into their data, make more informed decisions, and drive strategic growth. Whether you are a seasoned data analyst or just starting out, understanding and applying the principles of business analytics and data visualization will help you unlock the full potential of your data and communicate it in a way that resonates with your audience.

 



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