Driving Sales through Multidimensional Data Analysis in Dashboards

Steering Sales through Multidimensional Data Analysis in Dashboards

With the advancement of technology, the challenge to store the ever-growing amount of data has become a concern of less interest than simply how to analyze this data effectively. In fact, 30% of decision-making employees claim that they have trouble identifying useful data.

Dashboards definitely provide real-time visibility into business performance, however, they still provide a limited set of answers to stakeholders.

The Solution?

Using multidimensional data analysis on the dashboard provides an interactive platform for decision-makers to further explore and analyze data in the business context, thereby providing them deeper insights into business performance and trends.

What is Multidimensional Data and its Analysis

Multidimensional data analysis gathers data and converts it into interactive, explorable structures, often called cubes. These structures pave the way for a multidimensional view of the data on dashboards, thereby providing quick answers to commonly asked business questions.

For instance, you can get to know not just the best product sold, also what sold best in a specific region, during a specific time period, through a specific sales channel.

This multidimensional data view provides great data insights into the business, thereby allowing the decision-makers to make more informed decisions.

Multidimensional Data View

Key benefits of Multidimensional Data Analysis in Dashboards

Multidimensional data in dashboards help organizations boost their performance by:

  • Showcasing complex data in easy to understand the business context
  • Enabling decision-makers to stay ahead of changing business conditions: market shifts, mergers, and acquisitions, and providing trending analysis
  • Reducing IT workload by providing self-service access to corporate information

In a nutshell, if multidimensional information is extracted and converted into easy-to-understand visuals on dashboards, then stakeholders can easily conduct their own analysis, thereby taking proactive and apt business decisions.

The 3 Crucial Pillars of Multidimensional Data Analysis

A Multidimensional data model provides interactive and intuitive data analytics using multidimensional structures in the form of Dimension, Category, and Measure. These three basic objects combine to help decision-makers to discover changes, patterns, measure success and find reasons for any gaps in performances via The Analytic Dashboard.

1. Dimension

‘Dimension’ is the first vital concept in multidimensional data analysis that helps stakeholders to measure and compare results in an organization. For example, you are dealing with the ‘Sales’ cube with the following dimensions:


Visualization of this dimension on the dashboard will help enterprises to answer the following questions:

  • How did we perform this quarter vs the previous quarter?
  • How did we perform in this quarter versus the same quarter last year?
  • This year versus last year?

Sales Cube in multidimensional data


With the visualization of this dimension on dashboards, stakeholders will get acquainted with the following data:

  • The percentage of overall revenue that comes from a specific product line
  • The change of revenue mix between various product lines
  • The most and the least profitable product


Usually, enterprises have their branches spread across various regions around the world. With so much data coming from all parts of their respective regions, they will get to know the following with ‘location’ dimension on the dashboard:

  • Sales growth comparison across regions
  • Top regions in terms of revenue generation
  • Regions with a higher pipeline and a higher number of salespeople to pursue all the leads

Sales growth comparison across regions using multidimensional data analysis

2. Category

The second pillar of multidimensional data analysis is ‘Category.’ These are the individual data points within dimensions. It also details the parent/child relationship between the dimension keys.

For example:

  • The time dimension could be “2018” or “2019.”
  • The location dimension might include categories like “New York,” “Melbourne,” or “Beijing.” However, some categories can be subsets of others— “Melbourne” could be a subset of “Australia.”

This hierarchical organization makes it possible for decision-makers to define a dimension that they wish to use for ‘Quick Explore’ on the dashboard. It means that a stakeholder can also analyze a particular chart segment (column, point, region) just by a single click.

Quick Explore on the multidimensional dashboard

3. Measure

‘Measure’ is the third key concept used in multidimensional data analysis, which plays a vital role in building reports and performs analysis. It provides a consistent view of information for all stakeholders. Since ‘Measure’ is a subset of ‘Dimension’, various stakeholders can slice a cube according to the point of analytic interest to get a personalized view of the measure(s).

For example:

  • Financial Analysts will study data on the current and previous time period for all markets and all products.
  • Brand Managers will focus on groups of products across many time periods.
  • Strategic Executives may concentrate on a subset of the corporation’s data.
  • Sales Reps would focus on all time periods and all products across selected markets.

Sets and Subsets of Sales Cube


Multidimensional data analysis on dashboards helps in collecting and presenting useful enterprise information drawn from volumes of heterogeneous data, in a meaningful way, offering a great opportunity to improve and coordinate decision-making of your enterprise.

Thus, choosing an effective analytics dashboard with multidimensional data analysis is a wise investment for your enterprise, providing you with an option to quickly act upon the relevant information, enabling you with faster decision-making.

Contact Net Solutions to build Analytics Dashboards

Arshpreet Kaur

About the Author

Arshpreet is currently working with Net Solutions as a Lead Business Analyst. She plays an indispensable role in providing top-notch analytical and resource management advice to companies of all sizes. Her well-rounded knowledge in engineering concepts and a certification as a Scrum Product Owner helps her to handle multiple projects effectively, defining the product vision, acting as primary liaison with stakeholders and delivering value to customers.

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