How Can DataViz Help Cross-disciplinary Workforce Collaboration
Today, the boundaries between departments and product development teams are vanishing as cross-disciplinary workforces arise as the new norm. Collaboration in these employee groups is not limited to the sitting spaces, discussions, or let alone internal reviews. The industry requires employees to proactively work on interwoven domains to create a single product. But, how do these people from various educational backgrounds and functional areas come together for sharing information? This is a major challenge for organizations across the globe. In this article, we will learn about DataViz, one of the most powerful solutions to address this challenge.
DataViz is the graphical representation of data to simplify it for analytical purposes. Dive in deeper to explore more about using it for workforce collaboration.
Why we need it for workforce integration
A study from the World Economic Forum finds that 2.5 quintillion bytes of data are generated daily. Moreover, 90% of the data we have today is created in the last two years. Every business now interacts with market, processes, and human resources in a much more profound manner. Gone are the days when one could make a business decision without any prior considerations. This is because the products in the older days were developed by technicians/experts from a single domain. A car or motorcycle needed only mechanics with little electrical engineering knowledge. Today, one needs to understand that engines, control systems, wiring, chassis-body, liasioning, and R&D are all separate entities within an organization.
When people from different fields of expertise come together, sharing knowledge and aiding each other is but natural. Communicating the process parameters that divulge different fields turns complex if proper methods aren’t followed. For a product manager, it becomes difficult to get briefings from all the sources daily. Even if they are given in the form of central tendencies of behaviour, the concerned person cannot make complete sense out of it at a glance. Correspondence with each team member is also far from the feasible option.
Fact Time: The brains receives 90% of the total messages in the form of visuals. The human brain processes an image 60,000 times faster than text. (source: Visual Teaching Alliance)
Most professionals face this problem throughout their careers. Cross-disciplinary teams are prone to such shortcomings as collaboration beyond domains is an everyday reality. Owing to the workload and required precision, we cannot trust descriptive details for taking quick actions. Highlighting the relevant developments within short attention spans is more important in businesses. Therefore, a medium was sought to bridge the gap between the abundance of available data and the need for useful chunks. Visualization emerged as a solution in the 1800s and laid the foundation of modern BI tools.
Challenges in cross-disciplinary workforce collaboration
Different work profiles require different skill sets and approaches to solve problems. The people working on technical sides have a methodical approach while the ones leaning towards artistic work are flamboyant. They give inputs based on their domain knowledge. On a retrospective note, both of them face severe challenges while expressing their work to each other. However, understanding each other’s opinions is imperative to reach an optimal level of functional ease.
Sharing process parameters in the form of information to peers becomes problematic as not everyone understands all concepts instantly. Regardless of the situation, they don’t need to know everything. The staff members need to communicate the effects of their work on the entire product/project and the processes in real-time. To process and handle large datasets demands a distinct medium for summarizing key details on the go is needed.
How DataViz works with respect to collaborative exchange of knowledge
The human mind is more receptive to colours, patterns, and symbols in general. This behavioural aspect is linked to the two major types of information processing as studied by Nobel Laureate Daniel Kahn. He briefly divides information processing into two categories: System-1 and System-2. System-1 focuses on the natural, automatic, and basic processing methods that are deemed as natural responses. While the latter one is more logical, sophisticated, and driven by conscious efforts. It includes concepts like statistics that aren’t compatible with the normal cognitive functioning of humans.
Comparison between System-2 and System-1 data to describe Arnold Schwarzenegger:
Example for System-2: The person considered as the father of bodybuilding (8x Mr. Olympia) who popularized the sport in the ‘70s. Known for his work in Hollywood as an actor, filmmaker and also as the former governor of California.
Example for System-1:
DataViz sublimates the large datasets to segregate relevant information and represents it in the graphical formats. Most of the modern BI tools automatically scale the particulars to make the end results visually appealing. In the workplace, this transforms into a faster sharing of inputs and enhances data-driven decision making. In collaborative environments, sharing summaries becomes faster as the user can elucidate information promptly. Let us take a real-life example by considering the image given below:
(Image source: Ansys)
If a mechanical engineer from the R & D department finds a problem in an existing design, he can use the above image to show the problematic area. Everyone including employees from design, production, assembly, post-sales service, and even a technician can identify where the problem lies. As a result, the team members from each vertical can give suggestions faster by instantly identifying red-coloured regions. Therefore, converting System-2 particulars into a piece of System-1 friendly information reduces overall complexity and triggers innate responses. As a result, DataViz makes information more understandable and helps employees identify any requirements swiftly.
DataViz uses different types of graphs, representation techniques, word clouds, and network diagrams to simplify interpretation.
The applications of using DataViz include:
- Tracking Deviations And Trends
- Developing Management Information Systems (MIS)
- Scheduling And Allocating Resources
- Risk Management And Valuation
- Determining Project Status
- Mathematical Modelling
- Highlighting System Sensitivity
All of these concepts have importance for different disciplines and one cannot understand plain statistics of other fields. For instance, a Boeing 787 generates a half-terabyte of data on each flight. It is obtained from a network of sensors and results apply to both aviation and chemical engineers. Heat maps can help them in designing the aircraft fuel tanks without having to exchange calculations continuously. In such cases, saving a few days can result in handsome profits and trailing behind costs in millions.
Summing Up
Data interoperability across the organization helps improve processes, aligns teams, and recalibrates operations efficiently. Getting improvised insights from different departments improves the process of collective decision making. The number of decisions taken by a person any odd day is 35000. For C-suite managers, every decision translates to money and affects business at large.
DataViz simplifies the cognitive aspects of interpretation, uses images to leverage association, and directs attention swiftly. Its purpose is to pacify complexity by adopting an alternate method to display information. All cross-disciplinary teams can save multiple iterations of their personal work by using DataViz for exchanging and interpreting information while collaborating scalably.
- How Can DataViz Help Cross-disciplinary Workforce Collaboration - January 22, 2020
