Data visualization
Konstantin RnD
???? IT Lead Product manager | B2B | B2C | Digital | Mobile and Web Apps | R&D |
Magical guide about?#productmanagement?. Part 7 article #52
They say it’s better to see once than hear a hundred times. Today, in the era of big data, when companies are drowning in information from a variety of local and cloud sources, this saying is more relevant than ever.
Data visualization?is the process of using visual elements such as charts, graphs, or maps to represent data. It translates complex, large-scale or numeric data into a visual representation that is easier to process. Data visualization tools enhance and automate the process of visual data transmission to ensure accuracy and detail. You can use visual representations to extract practical conclusions from the raw data.
Every day, your organization accumulates more and more data on revenue, marketing effectiveness, customer interaction, inventory levels, production indicators, headcount, costs and other key performance indicators. But due to the need to process a large amount of data, it is difficult for your employees to see the big picture.
Data visualization helps to turn all this detailed data into clear, visually appealing and useful business information. Using external data sources, modern data visualization tools allow not only to more clearly represent key performance indicators: they combine data and help to see the relationship between KPIs, the market and the world of AI analytics.
Data visualization helps to achieve results
Data visualization is an important function of business analysis tools and the key to advanced analytics. Visualization helps to evaluate the value of information or data being created today. Data visualization refers to the representation of information in a graphical form, for example, in the form of a pie chart, graph, or other type of visual representation.
Why Visual Analytics is of great Importance
High-quality data visualization is critical for data analysis and decision-making based on it. Visualization allows you to quickly and easily notice and interpret connections and relationships, as well as identify emerging trends that would not attract attention in the form of raw data. In most cases, no special training is required to interpret graphical representations, which reduces the likelihood of misunderstandings.
A well-thought-out graphical representation not only contains information, but also increases the efficiency of its perception due to visibility, attracting attention and retaining interest, unlike tables and documents.
How Data Visualization works
Most data visualization tools can connect to local or cloud data sources, such as relational databases. In this way, the data is extracted for analysis. Users can choose the most appropriate way to present data from several options. Some tools automatically output recommendations on the use of views depending on the analyzed data.
Choosing the most appropriate visualization tool
The choice of graphical representation is carried out taking into account the type of data and their purpose. Some views are better suited for a certain type of data than others, such as a histogram or pie chart. However, most tools offer a wide range of data display options, from conventional line graphs and bar charts to time scales, maps, dependencies, histograms and customizable views.
Why is data visualization important?
Modern enterprises usually process large amounts of data from various data sources, such as:
But raw data can be difficult to interpret and use. Therefore, data scientists process and present data in the right context: they give it a visual form so that decision makers can identify the relationships between the data and discover hidden patterns or trends. Data visualization creates stories that advance business intelligence and support data-driven decision making and strategic planning.
What are the benefits of using data visualization?
Here are some of them:
Strategic decision-making
Key stakeholders and senior management use data visualization to interpret data in a meaningful way. They save time thanks to faster data analysis and the ability to visualize the big picture. For example, they can identify patterns, detect trends and get a complete picture of events in order to bypass competitors.
Increase employee engagement
Data visualization techniques are useful for communicating the results of data analysis to a large team. The whole group can jointly visualize data to develop common goals and plans, as well as assess progress in work and improve team motivation. For example, the sales department works together to increase sales in one quarter.
What components does data visualization consist of?
Data specialists distinguish three main components:
History
History is the purpose of data visualization. A data scientist communicates with several stakeholders to understand what they want to achieve by analyzing the data. For example, they may want to measure key performance indicators or predict sales volumes. Data scientists and business users jointly determine what kind of history they want to learn using data.
Data
Data analysts identify appropriate data sets that will help them lay out the history of the data. They modify existing data formats, clean up data, remove outliers, and perform further analysis. After preparing the data, they plan various methods of visual research.
Visual effects
Then data scientists choose the visualization methods that are most suitable for sharing new information. They create charts and graphs, highlighting key data points and simplifying complex datasets, and also think about effective ways to systematically present data for business analysis.
What problems arise when visualizing data?
Data visualization involves some difficulties that can lead to distortion of information or exaggeration of certain facts.
Oversimplification of data
Data scientists must find a balance between interpreting data and communicating. Oversimplifying data can lead to the loss of key information. For example, consider a scientific report on academic performance. The report presents a histogram showing that academic performance has declined, while the use of video games by students has increased over the past decade. The report concludes that the use of video games has a negative impact on learning. However, data visualization is overly simplified — it does not take into account demographic indicators and a number of other factors that also affect academic performance.
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Exaggeration
You can visualize unrelated data to create non-existent correlations. Attackers can use such inaccurate data visualization to justify harmful behavior or making wrong decisions. For example, a team overspends on production equipment to support a relative supplier. Then they justify the purchase using data visualization reports showing how the safety of workers has improved after the installation of new equipment. However, the safety of workers was affected by several factors unrelated to the new equipment.
Human prejudice
Human prejudice negatively affects data visualization. The team creating the reports may distort the results by pre-selecting the data according to their personal interests. Although data visualization tools are more accurate, the team working with them may unwittingly introduce bias by unfairly selecting and cleaning data. Therefore, it is important that you work with different teams during data visualization and listen to different opinions.
1. Plotly
Allows you to build very detailed graphs.
This application creates charts, presentations and dashboards. You can perform the analysis using JavaScript, Python, R, Matlab, Jupiter or Excel. There are also several options for importing data. A visualization library and an online chart creation tool allow you to create truly beautiful graphs.
Panel Data:
2. DataHero
It is well suited to collect information from a variety of services into a single system.
You can integrate data from cloud services into DataHero and create charts and dashboards. It does not require any special technical knowledge, so it is a great tool that the whole team can use.
3. Tableau
Creates datasets that can be shared in real time.
Tableau Public is an almost free visualization tool with graphs, charts, maps and much more. You can easily upload information to the system, and then watch how everything is updated. To speed up the process, you can work simultaneously with other project participants.
Tableau?helps people and organizations be more data-driven. As the market-leading choice for modern business intelligence, our analytics platform makes it easier for people to explore and manage data, and faster to discover and share insights that can change businesses and the world.
This is my most favorite and understandable BI tool??
4. Power BI
Microsoft Power BI is a collection of software services, applications, and connectors that interact with each other to turn disparate data sources into coherent, visually immersive, and interactive analytics
There are a lot of data visualization tools out there. I decided to show the most popular tools on the roar. I think this information was useful to you.
Thank you for your attention ??