Data Visualization: The Lynchpin of AI/ML and Business Analytics

The human brain values visuals over any other type of information. MIT neuroscientists have confirmed that our brain can process an image in just 13 milliseconds60,000 times faster than the speed at which it consumes text. [1]

Data visualization has thus emerged as key to integrated data analytics and gaining valuable business intelligence. Data visualization is no less than art supporting data science, where tabular or non-tabular data, binary codes, and text-heavy data get collected and transformed into simplified graphical representations. The graphs, maps, and charts that are visually appealing make the information easier to understand, analyze, and derive business intelligence. Data visualization can help reveal patterns and acquire insights that would not have been easily detectable in text format.

Data Visualization Techniques:

  • Charts (Line/Pie/Bar)
  • Plots
  • Maps
  • Diagrams and Matrices

There are many different techniques for data visualization. Some of the more popular types are:

The different data visualization techniques are used interchangeably to represent information and reveal valuable hidden patterns and insights. Since each has its strengths and shortfalls, choosing a data visualization technique depends on the data and the goal of the analysis. For example, a business analyst may use a simple bar chart to compare the results of two different Machine Learning models and their performance. However, a more granular visualization technique might help a production planner drill down into the process parameter details to discover anomalies in the production.

The Power of LIVE Data Visualization with AI/ML:

Data visualization is like the lynchpin between big data analytics and machine learning or AI apps. It helps production planners and domain experts interpret and understand what our Bodhee AI Apps have uncovered from the complex production data. When our Digital Twin creates a LIVE simulation of the shop floor realities, vivid data visualization provides 360-degree visibility of processes. That empowers the decision-makers to develop more accurate and efficient production models.

If our machine learning algorithms are fast at detecting outliers, accurate data visualization expedites the communication of actionable insights. Identifying patterns and correlations between process parameters and gaining insights into shop-floor realities would have been impossible without the simplified data visualization methods. Our AI apps also utilize well-articulated data visualization for real-time communication of results reflected in production throughput, batch quality, yield, etc.

When all production data gets integrated and visualized, organizations can also clearly communicate the findings to stakeholders. Additionally, data visualization can help present complex ideas to non-experts who need to understand the use, impact, and efficacy of AI apps for manufacturing optimization.

Drive optimized production performance with strong data visualization and AI for advanced business analytics!


[1] Trafton, A. (2014, January 16). In the blink of an eye. MIT News.

[2] Peterman, M. (n.d.). 15 Statistics That Prove the Power of Data Visualization.

Data Experts Can Help Improve Manufacturing; Here’s How…

The way we manufacture anything today is drastically different than how our grandfathers did. Machines get connected to the web and are ripe for data analytics. The SMART machines are data-driven, enabling newer endless possibilities for manufacturing. In fact, realized intelligent factories depend on data science methodologies and collaborative manufacturing systems to make operations more flexible, adaptable and optimized. 

Data Science methodology is a set of techniques and processes used when solving manufacturing problems by utilizing data. This methodology has a significant impact on the practical application of data-driven products. Critical business data is analyzed with the objective of aiding enterprises better understand their business. The focus gets expanded from ‘what happened?’, ‘how often?’ and ‘where?’ to answer ‘why?’, ‘what if this trend continues?’, ‘what will happen in the future?’, and ‘what is the ideal scenario?’. Using subject-specific jargon, these questions correspond to analytical tasks widely known as statistical analysis, forecasting, predictive modeling, and optimization. [1]  

In other words, Data Analytics is about making the most of all data using a set of tools and technologies to deliver business value. The process begins with acquiring data from multiple sources (preferably clean and possibly in large amounts) in layman’s terms, the information you can leverage. Even the smallest of data can get integrated and analyzed to reveal whether or not a manufacturer is making profits, saving money, or over-spending to improve performance. Let experienced professionals or the right data analytics tools handle the data to get real value from your production processes.  

Aside from data analysts and statisticians, you can make the most out of available data by harnessing advanced AI apps, leading you to customer-centric goals and process efficiency. The AI apps work with the collected business data, bringing the numbers in the statistics to life, diagnosing, and correcting the manufacturing through informed decisions that drastically improve the production processes. 

“The goal is to turn data into information, and information into insight.” 

– Carly Fiorina, former executive, president, and chair of Hewlett-Packard Co. 

A smart factory monitors the entire production process by linking physical and digital worlds. From building tools to individual operators in the workshop are rich data sources. It is an end-to-end connected and flexible production system, which uses a constant stream of data to provide learning from the machines. Based on the integrated data analytics, AI Apps provide real-time insights to adjust the process workflows, adapt to new needs, and optimize production. Intelligent factories thus achieve machine maintenance in advance, inventory management, and efficient production processes throughout the manufacturing network. 

Data can take many forms and fulfill many purposes in an intelligent factory environment. Data makes discrete information on manufacturing environmental conditions such as humidity, temperature, and pollutants accessible. It also helps self-optimize devices, addresses numerous manufacturing problems, improves process control, and guides while responding to new requirements. 

According to a recent study conducted by the World Economic Forum, nearly 75% of surveyed manufacturing executives consider advanced analytics to be critical for success. However, only a few companies capture the full value that data and analytics can unlock to help address the manufacturers’ most pressing challenges. [2] Establishing a strong technological backbone is a prerequisite to effectively scale data and analytics applications for manufacturing success in the post-pandemic world.  

Industry 4.0 is data-driven and when its technologies are growing exponentially, the frequency of data collection will also increase. Those organizations that are under-using data or are still not AI-ready run the risk of remaining on a lower rung of the business ladder.  

On a lighter note…. 

Although the D word is of BIG importance in the manufacturing world, we stand divided on the pronunciation. There is a valid reason behind the ‘proper’ pronunciation of the word Data. Ideally, it should be pronounced: day-taa, (not daa-taa) as data is the plural of datum (day-tum). 


[1] Omar, Y. M., Minoufekr, M., & Plapper, P. (2019). Business analytics in manufacturing: Current trends, challenges and pathway to market leadership. Operations Research Perspectives, 6, 100127.

[2] Weber, A. (2021, August 24). The Big Data Dilemma.