Imagine enjoying a movie by just reading the script, without seeing any action or emotion. Would it be half as exciting? Would you feel that same connection with the characters?
Well, the same is true with data.
Human brain is driven by visuals. Much like a script without visuals, a surplus of data, while valuable, can be overwhelming. It’s akin to an avalanche of reports, figures, and projections inundating your team. Without a streamlined approach, this flood of information can erode productivity and, subsequently, your bottom line.
That’s where the power of effective data visualization steps in. It’s like bringing a movie to life on the screen, transforming words into action, and allowing you to truly connect with the story. In the realm of data, visualization is the key to turning raw numbers into actionable insights, ensuring that you’re not just drowning in data, but making the most of it.
Table of Contents
- What is data visualization in the context of business analytics?
- Different Types of Data Visualization
- Top 3 use-cases of data visualization in business analytics
- Sales Analytics
- Customer Segmentation
- Supply Chain Optimization
- Where do I get started with data visualization?
- Do business analysts do data visualization?
- Is business analytics the same as data analytics in the USA?
Data visualization in business analytics presents complex information in visual formats like charts, graphs, and dashboards. It transforms raw data into clear, intuitive visuals, making it easier to understand, analyze, and derive insights.
When done right, data visualization helps business people make decisions faster and find connections in data that might be hard to see in just words or tables. Therefore, finding a technology solution embedded with robust data visualization capabilities is not just an advantage—it’s a vital necessity, especially for enterprises heavily reliant on business intelligence.
Bar Charts: Ideal for comparing categories or displaying trends over time, bar charts use rectangular bars to represent data values.
Histograms: Histograms display the frequency distribution of a continuous dataset. They group data into intervals (bins) and represent the frequency of occurrences in each interval as bars. This provides insights into the shape and spread of the data.
Pie Charts: Always fascinating in appeal, pie charts display data as slices of a circle, emphasizing proportions.
Line Graphs: Line graphs use lines to connect data points over a continuous scale. They are primarily used to illustrate trends and patterns.
Box-and-whisker plots: Also known as box plots, display the distribution and spread of a dataset. They show the median, quartiles, and potential outliers, concisely summarizing the data’s central tendency and variability.
Infographics: Infographics combine visuals, such as charts, graphs, and images, with concise text to convey information in a visually engaging and easily digestible format.
“Data Visualisation in business analytics plays a vital role in steering a company towards sustained growth and profitability.”
Scatter Plots: These graphs display relationships between two variables, with points representing data points on a Cartesian plane.
Waterfall charts: A waterfall chart visually represents how an initial value is affected by positive and negative values. It’s used to track the cumulative effect of different values on an initial quantity, making it easy to see how it changes over time or stages.
Heat Maps: Using color intensity to represent values, heat maps visually summarize complex data sets, highlighting trends and patterns.
Fun Fact? Team Kanerika will analyze the performance of this blog using Heat maps tool. You can make it a winning blog by reading it till the end. 🙂
Maps: Maps visualize spatial data, representing geographic features, locations, or statistical information using symbols, colors, or patterns. They provide a powerful way to understand and analyze data within a geographical context.
Sales performance analysis involves the examination of data related to a company’s sales activities. Through visualizations, businesses can gain valuable insights into their revenue generation processes, product demand, and forecast for future trends.
In most cases, the sales dashboard may reveal that a particular product category, for instance, electronics, consistently outperforms others. Additionally, it might highlight a surge in sales during certain seasons or in specific regions. The analysis might also uncover underperforming product lines or regions that require attention. This prompts the company to reevaluate its marketing strategies, potentially leading to a product redesign or a targeted promotional campaign.
Armed with these insights, the company can strategically allocate resources, plan marketing campaigns, and negotiate supplier contracts to further capitalize on the success of electronics.
Customer segmentation is categorizing a company’s customer base into groups defined by common traits like demographics, behaviors, or preferences. Visualizing this data empowers businesses to gain deeper insights into their diverse customer base, enabling them to craft targeted marketing strategies.
In today’s economy, social media platforms and e-commerce stores are prime examples of enterprises harnessing the potential of customer data. Customer-centric companies can generate graphical representations of their customer base by employing data visualization tools. These visuals may encompass charts and graphs illustrating key demographic factors such as age, location, and spending patterns.
In many supply chains, especially those complex or involving multiple partners, visibility can be a significant challenge. This lack of visibility can lead to delays, inefficiencies, and increased costs. Data visualization tools provide a clear and comprehensive view of the entire supply chain, allowing businesses to track the movement of goods, monitor inventory levels, and identify any bottlenecks or delays in real time.
Through these visualization tools, stakeholders gain access to user-friendly dashboards showcasing vital metrics and performance indicators at different stages of the supply chain. This newfound transparency empowers them to make well-informed decisions, respond swiftly to disruptions, and fine-tune processes for enhanced efficiency.
Kanerika’s Supply Chain Collaboration Platform (SCCP) stands out as an exceptional innovation for organizations seeking heightened visibility among stakeholders. Deployment of this tech stack at one of our esteemed clients, a Global Consumer Goods Company, resulted in notable reductions in stockouts and lead times, alongside improved supplier performance.
The AI-powered solution also fostered effective collaboration among supply chain partners. This wealth of data in various visual formats facilitates real-time information sharing and insights, nurturing a more synchronized and responsive supply chain network.
Getting started with data visualization is a crucial step in leveraging your business data effectively. However, your business must have a streamlined data integration and transformation process to ensure an efficient data visualization process. This is because visual representations won’t yield the desired insights without effective data collection and distribution.
For data visualization in business analytics, FLIP acts as a catalyst, streamlining data processes and facilitating effective data transformation before it’s fed into a data visualization tool.
Additionally, we have designed it to create data transformation pipelines on the go. You can now automate tasks like data cleaning, aggregation, and formatting.
Yes, business analysts often engage in data visualization. It’s a crucial aspect of their role, as it helps them translate complex data into clear, actionable insights for informed decision-making.
Business analytics and data analytics are related in the USA but not entirely synonymous. Business analytics encompasses a broader scope involving data to drive business decision-making, including aspects beyond data analytics, like statistical analysis, predictive modeling, and more. Data analytics, on the other hand, specifically focuses on analyzing and interpreting data for insights.