Most businesses have data they’d like to exploit to make mission-critical decisions, and perform analytic functions. When dealing with large amounts of information, achieving data-driven outcomes with repeatability and at scale can be exceedingly challenging.
The process becomes incredibly computationally intensive and time-consuming. What if there was a better way? Well, this is where visual analytics comes into play.
What is visual analytics?
Visual analytics is the science of analytical reasoning facilitated by interactive visual interfaces. It uses data analytics and interactive visual representations of the data and dashboarding to enable users to interpret large volumes of data.
Visual analytics vs data visualisation
Even though visual analytics and data visualisation are commonly compared to each other, they have distinct functions and potentials. Both offer a specific range of data that can be used to answer particular inquiries and provide a visual method of presenting data— allowing for easier communication of findings and for narrating stories with data.
Additionally, both provide data points and help to emphasise issues and primary indicators. However, that is where their similarities end.
While data visualisation simply offers a graphical representation of the data, visual analytics gets into the analyzing part to make informed decisions.
Visual analytics allows you to explore your data in a visible way— without having a preconceived notion of what you are looking for. This allows for the discovery of unexpected findings and insights that may have been previously unknown or overlooked.
Overall, data visualisation helps you answer the “what”, while visual analytics dig deeper to help you answer the “why”.
Visual analytics process
Data collection is an essential first step in the visual analytics process. It involves gathering data from various sources, such as databases, spreadsheets, and external sources like the internet or sensors.
In practice, the collated data should be relevant to the problem or decision that needs to be made and should be in a format suitable for analysis. Overall, the cardinal goal of this step is to have a comprehensive and accurate dataset that can be used to support decision-making through visual analytics.
Data preparation is the next step in the visual analytics process. It involves cleaning, transforming, and preparing the data for analysis. This is important because it helps to ensure that the data used for analysis is accurate, consistent, and relevant to the problem or decision that needs to be made.
Data preparation activities may include:
- Removing missing or duplicate values.
- Correcting inconsistent or incorrect values.
- Handling outliers.
- Combining data from multiple sources.
- Transforming data into a suitable format for analysis, such as converting data into a numerical format.
- Normalising data to ensure that it is on a common scale.
- Removing irrelevant or redundant data.
Generally, the core goal of this step is to deliver a well-prepared dataset that can be used to support decision-making through visual analytics.
The visual analytics process involves presenting data in a graphic form, like charts, diagrams, or grpahs, to aid people in finding trends, patterns, and correlations in the data.
Visualisation of the data makes it simpler for users to recognise trends that may not be obvious in raw data, and it can assist them in spotting correlations and interconnections between variables.
Fundamentally, the chief aim of this step is to craft a visual representation that enables users to understand the data more easily.
Data interpretation involves ‘making sense’ of the data and drawing insights from it through visual analysis.
This step is where the user can employ their expertise and knowledge to understand the data and ‘intepret’ the patterns and relationships that are revealed through the visual representation.
Action planning is the final step in the visual analytics process. It involves using the insights gained from the data interpretation step to support decision-making and problem-solving.
This step entails taking the insights and turning them into actionable steps that can be taken to solve a problem or make a decision. Action planning activities may include:
- Developing recommendations based on the insights gained from visual analysis.
- Identifying the next steps and tasks to be carried out.
- Creating a plan of action to address the problem or make the decision.
- Assessing the feasibility of the plan and any potential risks or challenges.
- Implementing the plan and monitoring its progress.
Overall, the main goal of this step is to turn the insights gained from visual analysis into concrete actions that can be taken to address a problem or make a decision.
Visual analytics techniques
Visual analytics techniques are methods used to represent and analyse data visually in order to gain actionable insights and support decision-making. Some of the common ones include:
- Data Visualisation: This entails representing data in a visual format, such as graphs, pie charts, and maps, to help users understand patterns, trends, and relationships in the data.
- Statistical Visualisation: This technique involves representing statistical data using techniques like histograms, scatter plots and box plots to understand the distribution and relationships between variables.
- Information Visualisation: This technique helps in representing complex information, such as networks and hierarchies, using techniques like node-link diagrams and tree maps to reveal relationships and patterns.
- Geospatial Visualisation: It revolves around representing data with a geographic component, like heat maps, choropleth maps, and flow maps, to understand spatial patterns and relationships.
- Temporal Visualisation: This technique focuses on representing data over time via line charts and time series plots, to understand trends and patterns over time.
- Multivariate Visualisation: This approach represents multiple variables in a single visual representation, like parallel coordinates and scatterplot matrices, to understand relationships and patterns between variables.
For the most part, these techniques can be combined and applied in various ways to support visual analytics and decision-making. Generally, the central goal should be to use these techniques to communicate information effectively and gain insights from data.
Benefits of visual analytics for businesses
1. A better understanding of data
Visual analytics provides an intuitive and interactive way for users to explore and understand large and complex data sets. This can, otherwise, be difficult to comprehend using traditional analysis methods.
2. Discovering errors
In traditional data analysis methods, errors in data can go unnoticed and lead to incorrect insights and decisions. However, visual analytics provides an intuitive and interactive way for users to spot errors and inconsistencies in data, which can then be corrected.
3. Improved decision making
By providing an intuitive and interactive way to explore and understand data, visual analytics supports informed decision-making and leads to improved outcomes and results for businesses.
4. Quick business insights
Visual analytics enables users to intuitively and expeditiously uncover insights into data that would be difficult to find using traditional analysis methods. This can lead to a better understanding of data and support informed decision-making. Some of the ways that visual analytics can provide quick business insights include:
- Quick identification of patterns and trends.
- Easy comparison and contrast of data sets.
- Spotting hidden correlations and relationships.
- Visual representation of complex relationships.
5. Understanding the latest trends
With visual analytics, businesses can quickly and easily explore and analyse large amounts of data to uncover the latest trends. This can help businesses stay ahead of the curve and make informed decisions.
6. Enhanced communication with clients and within the organisation
Visual analytics provides a clear and intuitive way to communicate data and insights, which can improve communication and collaboration within organisations and with clients.
7. Customer behaviour analysis
With visual analytics, businesses can quickly and easily analyse large amounts of customer data to uncover patterns, trends and insights into customer behaviour. This can help businesses make informed decisions about new product launches or marketing strategies. Additionally, they can use it for improving customer engagement and customer retention.
8. Improve sales and marketing strategies
Some of the ways that visual analytics can support the improvement of sales and marketing strategies include:
- Targeted marketing: Visual analytics supports targeted marketing by providing insights into customer behaviour and preferences, which can help businesses identify target segments and craft effective marketing campaigns.
- Sales performance analysis: Visual analytics can be used to analyse sales data, which can help businesses understand what is driving sales and create better strategies for future.
- Understanding marketing ROI: Visual analytics can help businesses understand marketing campaigns’ return on investment (ROI), which can inform marketing strategies and budget allocation.
- Predictive analytics: Visual analytics supports predictive analytics, which can help businesses understand future customer behaviour and make informed decisions about sales and marketing strategies.
9. Better supply chain management
With visual analytics, businesses can analyse supply chain data through real-time monitoring, inventory optimisation, supply chain visibility, root cause analysis and collaborative decision-making. This can help to identify areas for improvement and make informed decisions about supply chain management.
Visual analytics tools
Visual analytics tools are software applications that support the process of visualising and analysing data. Some of the most popular visual analytics tools include:
- Tableau: This is a popular data visualisation tool that allows users to create interactive dashboards, reports, and charts.
- QlikView: This is a business intelligence tool that provides self-service data discovery and powerful interactive analysis.
- Power BI: Power Bi is an industry-leading data visualisation tool from Microsoft that allows users to create interactive reports and dashboards.
- TIBCO Spotfire: This advanced data visualisation and analytics tool flexibly supports advanced data discovery and analysis.
- SAP Lumira: This self-service data visualisation tool from SAP supports data discovery and analysis, and can be used to create interactive dashboards and reports.
- IBM Cognos Analytics: This unique business intelligence tool provides data visualisation and analytics capabilities.
- Oracle Business Intelligence: This business intelligence tool from Oracle provides data visualisation and analysis capabilities.
- MicroStrategy: This enterprise business intelligence tool provides advanced data visualisation and analysis capabilities.
These are just some examples of the many visual analytics tools available and prevalent in the market. The best tool for a particular business will depend on several factors, including the size of the organisation, the type of data being analysed, and the specific requirements and needs of the business.
We are entering an exciting time for visual analytics, particularly when it comes to the business value that can be derived from this methodology. As enterprises continue to demand more from the data collected by their organisations, visual analytics will become increasingly valuable.
And if you’re a decision-maker looking to make sense of big data, it’s critical that you know your toolkit.
Reach us at Accord for expert training on how to use visual analytics to drive business decisions.
Areas of expertise: Training and consulting in technology, strategy, analytics, business management, and learning and development.
Awards: ‘Innovation for Impact Award’ 2016-17 | ‘Associate Excellence Award’ 2018-19 | ‘Innovation for Impact Award’ 2020-21 by CSC.
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