Data science is all about extracting the information from the data gathered. As data has become one of the essential entities in today's world, it drives substantial business profits. It also inspires many to launch their startup based on the assessments they perform of the user interests. While it seems a simple option to get started with, converting the data into something useful is not that simple.
Based on the past data you have gathered and the information you currently have on hand, you can essentially build a foundation for your next business strategy to promote the selling and increase revenues. However, data visualization plays a vital role when it comes to making use of the existing data on hand.
Data visualization helps in plotting the data on graphs and use charts to generate insights from it that turn out to be really useful. However, based on the nature of data and the information available at hand, the best method is chosen, which can provide valuable insights and save the time of businesses and data analysts.
But before you start with plotting the data and extracting facts, it is necessary to keep the best practices and expert tips in mind to save time and provide more useful insights than ever.
Here are the best practices of data visualization that helps deliver excellent services within a limited time. No matter the size of data available at hand, these techniques help you plot it efficiently and generate useful business insights.
When plotting your collected data on graphs, ensure to pay complete attention to the metrics you find most helpful. The data must be divided between two crucial metrics that can help you divide it between parts further. For example, when you have such data on hand, you can use the scatter chart to distribute data in metrics and generate insights in accordance with it.
Scatter plots divide the matrics in a quadruple setting and help generate useful insights based on the two significant metrics we have chosen. Hence, it is one of the best and expert-recommended ways of plotting out the data and generating insights efficiently.
However, non-technical people may find this slightly difficult to read out but with the time and practices of big data analytics, you can easily interpret data irrespective of the technical knowledge you possess on this subject.
To start plotting, all you have to do is find two metrics that will become the dimensions of the plot. At least one of the dimensions must be an actionable figure that you can use, e.g., rate, state, employees, or any other. Later, start plotting and deprive the insights of the results you generate.
The most crucial part of data insight generation is that your client should be able to relate and find insights by themselves. By managing the clarity of the project you have on hand, you can ensure they get answers to the questions. The data sets you will use, metrics you finalize on, the calculation you perform, everything should have approval from your clients.
This sounds much simpler that it actually is. Many times, as the project keeps expanding, the actual purpose, goals, and the insights to be generated are forgotten, and the path is changed in-between. This leads to unsatisfied customers and does not leave a good impression of your analytics practices. Therefore, what to do? Read further.
Your clients want to get their data analyzed in a way that can help them understand and generate data insights quickly from the results you produce. Therefore, it is advisable to keep the project clarity in mind when you deliver the insights to your clients through plots/graphs.
If you are still confused about what your client wants, you should imagine yourself standing in their position and then ask the questions as if you were the client. It will help you cover up many of the issues and perform visualization that can help in understanding what your clients want and maintain project clarity.
It is better to check the results as many times as you can. It helps in verifying the data visualization you have done and recheck the consequences before you submit it to clients. Not only this, it sometimes gives away a better way to generate insights and can help in producing efficient results.
Mostly, data analysts tend to use pie charts when it comes to comparing the results on various basis. Bar graphs are always a great way to compare results, but when the comparison is required to be done on multiple aspects, and the number of comparisons is higher, pie charts are the best way to find out the right answer.
For example, a business has handed over their work to analyze customer behavior based on their requested services. Now you can use this data collected through their gojek clone app and plot it on various pie charts based on the dimensions you select from the provided data. It helps you find out the most accurate insights from the list of all available. This not only helps satisfy your clients but also tends to deliver excellent services and follow the best data visualization practices.
No matter how efficient and excellent data visualization you have performed, the representation of the same makes a huge difference. The clients judge your excellence based on the design and formatting you use, and if you are not paying enough attention to it, you might bail out in advance.
Data visualization is all about providing useful insights and engaging the clients in the entire process by generating the best results and aligning them in the right design. The way you represent the generated data makes a big difference, and often, an excellent work represented with poor design fails to impress the clients.
Therefore, the design constraints and formatting must be chosen, keeping the best interest in mind. By understanding the customer needs, you can decide which design and format will get appreciated and should follow the same to generate the best results.
Data visualization is indeed a crucial task to execute for generating better insights. However, the approach you use and the design and formatting you perform creates a significant difference.
While data analytics is set to become a vital technological concept in the future world, an expert data analyst is appreciated by businesses around the world. And to prove your expertise in data analytics, it is required to have complete knowledge of the entire process and deliver excellent results.