How KPIs Add Value to Big Data Visualization
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Big data has created unrivaled opportunities for businesses. It aids them in achieving faster and deeper insights that can strengthen the decision-making process and improve customer experience. It can also accelerate the speed of innovation to give companies a competitive advantage. Given that we live in a time of data and information overload, we really need to have better mechanisms to make sense of it all. Enter data visualization, which supports the transfer of information to knowledge by illustrating hidden issues and opportunities in big data sets.
A significant amount of the human brain is dedicated to visual processing. This results in our sight having a sharpness of perception far surpassing our other senses. Effective data visualization shifts the balance between perception and cognition. The visual cortex is used at a higher capacity, and the viewer understands the presented information much quicker to make better decisions based on the findings.
Businesses are increasingly turning to data visualization to organize the overwhelming amount and variety of data cascading into their operations and to eliminate the struggle of just storing the data, shifting instead to analyzing, interpreting, and presenting it in a meaningful way. The trend toward data visualization is worth delving into by any business seeking to derive more value from big data.
Tackling big data usually involves the four Vs: volume, velocity, variety, and veracity. However, we must emphasize a fifth V that requires attention, namely visualization. Even with the use of business intelligence (BI) tools and the exponential increase in computing power, the need to consume information in a meaningful manner exceeds the ability to process it.
The Role of Big Data Visualization
Visualization plays a key role starting from the raw input of big data, where structures and underlying patterns that may be held within the data can be observed or formed, resulting in a visual representation that presents key insights in an efficient and digestible way.
Crafting data visualization is more than simply translating information in a visual display. It ought to communicate information effectively, with the prime purpose of presenting data in a quick, accurate, powerful, and long-lasting manner.
The main problem with big data involves complexity. Data is growing exponentially with time, as an increasing amount of it is made available on the internet. Furthermore, the number of insights, opportunities or hypotheses hidden in a dataset is exponential to the size of the datasets.
Key performance indicators (KPIs) can be used to achieve efficiency and ensure the comprehensibility of visual representations resulting from big data, attain the goal of graphical excellence, and add value to the result. Big data visualization requires skills and principles that must be carefully learned. Each big data visualization created should follow a clear path to success: attain, define, structure, extract, load, display, refine the data, and interact with it.
KPIs add value to the entire process by ensuring clarity in developing the strategy of the project, focus on what matters and requires attention, as well as improvement by monitoring the progress towards the desired state.
Read More >> KPI Data Visualization: Key Benefits, Popular Formats, and Design Principles
How to Manage a Big Data Visualization Project
When developing a big data visualization project, the process should follow a cursive and pre-defined flow in accordance with the project needs and the requirements of the end-user. These recommended stages are:
Acquiring the Data
This is usually how the process starts, unrelated to the platform that provides the data. In the process of big data collection, there is also the issue of data selection. Instead of just throwing it all in, one should focus on selecting high-quality data that is relevant to the project’s objective and does not add noise to the end result.
The noisier the data is, the more difficult it will be to see the important trends. It is suggested to have a clear strategy for the data sources required, as well as for the subsets of data relevant to the questions the project wants to answer.
Structuring the Data
The next phase in the process is structuring the acquired data. This includes the process of organizing the data to align it to one standard. The data store might be comprised of a combination of structured, semi-structured, and unstructured data.
At this stage, it is easier to identify the common aspects in each set of data and to find relationships between the data at hand. This includes translating system-specific data coding to meaningful and usable data (the platform where the data will be aggregated does not know that the set of data labeled “Customer No.” is the same as “# Customer” or “ID-Customer”).
Loading and Visual Mode Selection
After cleaning the data, filtering through enormous amounts of data, and replicating the application logic to make the data self-describing, the process continues with loading the data in the preferred platform and choosing the visual mode of representation.
In this stage, we can determine if the background data is very noisy, as the emerging visual representation will be hard to read or irrelevant to the strategic objective of the project.
By implementing KPIs along the project and linking them to the project objectives, the increased value will be added in the form of:
- Better quality of the visual representations
- Fewer project delays
- Less rework along the way
- Improved productivity
- Greater contribution to the visuals’ value
- Enhanced growth and innovation of the visual representation
- Easier project assessment.
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Editor’s Note: This article was originally published on October 17, 2017. It has been updated as of March 7, 2025.