With so much data being acquired through data analysis in today's corporate environment, we need a means to visualise that data so we can understand it.
By placing data in a visual context, such as maps or graphs, data visualisation helps us understand what it means. This makes the data more natural to understand for the human mind, making it simpler to see trends, patterns, and outliers in huge data sets.
What is data visualisation?
The graphical depiction of information and data is known as data visualisation. Data visualisation tools make it easy to view and comprehend trends, outliers, and patterns in data by utilising visual components like charts, graphs, and maps.
It provides insights on one or more pages or screens to assist you keep track of events or activities at a glance. Unlike an infographic, which displays a static graphical representation, a dashboard displays real-time data by extracting complicated data points from massive data sets.
An interactive dashboard allows you to quickly sort, filter, and dive into many sorts of data. Data science approaches may be used to quickly understand what is occurring, why it is occurring, and what will occur next.
(Must read- top 10 data visualization techniques)
Different applications of data visualisation
A dashboard that visualises a patient's history might aid a current or new doctor in comprehending a patient's health. It might give faster care facilities based on illness in the event of an emergency. Instead than sifting through hundreds of pages of information, data visualisation may assist in finding trends.
Health care is a time-consuming procedure, and the majority of it is spent evaluating prior reports. By boosting response time, data visualisation provides a superior selling point. It gives matrices that make analysis easier, resulting in a faster reaction time.(From)
When compared to local options, cloud connection can provide the cost-effective “heavy lifting” of processor-intensive analytics, allowing users to see bigger volumes of data from numerous sources to help speed up decision-making.
Because such systems can be diverse, comprised of multiple components, and may use their own data storage and interfaces for access to stored data, additional integrated tools, such as those geared toward business intelligence (BI), help provide a cohesive view of an organization's entire data system (e.g., web services, databases, historians, etc.).
Multiple datasets can be correlated using analytics/BI tools, which allow for searches using a common set of filters and/or parameters. The acquired data may then be displayed in a standardised manner using these technologies, giving logical "shaping" and better comparison grounds for end users.
(Related blog: Business Intelligence vs Data Analytics)
Applications of data visualisation
It's a matter of life and death for the military; having clarity of actionable data is critical, and taking the appropriate action requires having clarity of data to pull out actionable insights.
The adversary is present in the field today, as well as posing a danger through digital warfare and cybersecurity. It is critical to collect data from a variety of sources, both organised and unstructured. The volume of data is enormous, and data visualisation technologies are essential for rapid delivery of accurate information in the most condensed form feasible. A greater grasp of past data allows for more accurate forecasting.
Dynamic Data Visualization aids in a better knowledge of geography and climate, resulting in a more effective approach. The cost of military equipment and tools is extremely significant; with bar and pie charts, analysing current inventories and making purchases as needed is simple.
(Recommended blog: Common types of data visualization)
For exploring/explaining data of linked customers, understanding consumer behaviour, having a clear flow of information, the efficiency of decision making, and so on, data visualisation tools are becoming a requirement for financial sectors.
For associated organisations and businesses, data visualisation aids in the creation of patterns, which aids in better investment strategy. For improved business prospects, data visualisation emphasises the most recent trends.
Data scientists generally create visualisations for their personal use or to communicate information to a small group of people. Visualization libraries for the specified programming languages and tools are used to create the visual representations.
Open source programming languages, such as Python, and proprietary tools built for complicated data analysis are commonly used by data scientists and academics. These data scientists and researchers use data visualisation to better comprehend data sets and spot patterns and trends that might otherwise go undiscovered.
In marketing analytics, data visualisation is a boon. We may use visuals and reports to analyse various patterns and trends analysis, such as sales analysis, market research analysis, customer analysis, defect analysis, cost analysis, and forecasting. These studies serve as a foundation for marketing and sales.
Visual aids can assist your audience grasp your main message by visually engaging them and visually engaging them. The major advantage of visualising data is that it can communicate a point faster than a boring spreadsheet.
In b2b firms, data-driven yearly reports and presentations don't fulfil the needs of people who are seeing the information. They are unable to grasp the art of engaging with their audience in a meaningful or memorable manner. Your audience will be more interested in your facts if you present them as visual statistics, and you will be more inclined to act on your discoveries.
(Must read: Data visualization in marketing)
Food delivery apps
When you place an order for food on your phone, it is given to the nearest delivery person. There is a lot of math involved here, such as the distance between the delivery executive's present position and the restaurant, as well as the time it takes to get to the customer's location.
Customer orders, delivery location, GPS service, tweets, social media messages, verbal comments, pictures, videos, reviews, comparative analyses, blogs, and updates have all become common ways of data transmission.
Users may obtain data on average wait times, delivery experiences, other records, customer service, meal taste, menu options, loyalty and reward point programmes, and product stock and inventory data with the help of the data.
Real estate business
Brokers and agents seldom have the time to undertake in-depth research and analysis on their own. Showing a buyer or seller comparable home prices in their neighbourhood on a map, illustrating average time on the market, creating a sense of urgency among prospective buyers and managing sellers' expectations, and attracting viewers to your social media sites are all examples of common data visualisation applications.
If a chart is difficult to understand, it is likely to be misinterpreted or disregarded. It is also seen to be critical to offer data that is as current as feasible. The market may not alter overnight, but if the data is too old, seasonal swings and other trends may be overlooked.
Clients will be pulled to the graphics and to you as a broker or agent if they perceive that you know the market. If you display data in a compelling and straightforward fashion, they will be drawn to the graphics and to you as a broker or agent.
(Also read: AI in real estate)
Users may visually engage with data, answer questions quickly, make more accurate, data-informed decisions, and share their results with others using intuitive, interactive dashboards.
The ability to monitor students' progress throughout the semester, allowing advisers to act quickly with outreach to failing students. When they provide end users access to interactive, self-service analytic visualisations as well as ad hoc visual data discovery and exploration, they make quick insights accessible to everyone – even those with little prior experience with analytics.
In e-commerce, any chance to improve the customer experience should be taken. The key to running a successful internet business is getting rapid insights. This is feasible with data visualisation because crossing data shows features that would otherwise be hidden.
Your marketing team may use data visualisation to produce excellent content for your audience that is rich in unique information. Data may be utilised to produce attractive narrative through the use of infographics, which can easily and quickly communicate findings.
Patterns may be seen all throughout the data. You can immediately and readily detect them if you make them visible. These behaviours indicate a variety of consumer trends, providing you with knowledge to help you attract new clients and close sales.(From)
When data is visualised, it is processed more quickly. Data visualisation organises all of the information in a way that the traditional technique would miss.
(Read also: How is Power BI and Tableau used for Data Visualization and Business Intelligence?)
We can observe ten data visualisation applications in many sectors, and as these industries continue to expand, the usage of data visualisations will proliferate and evolve into key assets for these organisations.