Some time back we didn’t have Data visualization and we all had a MIS executive in our team whose only role is to churn out data and use Microsoft Excel to create graphs, charts, plots, and so on on a daily basis send this all the management team as the reports for the day/week/Month. In case we need some different analysis like how many people choose a particular product, how fast it is selling, and what regions, we had to request a team of people who will then Write a script, give it to us, and then we could do something about it. This whole process of requesting and getting a script took 5 days. Maybe after days as a business manager, I had some other priorities so that data took a back seat.

You need to look at data when you want to look at data, else that thought, the idea, the moment gets lost. As business managers, we are programmed to take action when we want to experiment, and that is too fast. There was a need to access to the data the way businesses wanted but we couldn’t do much about it since we were limited by technology.

Thanks to Data visualization tools, it’s integration with the back-end data lakes or Data warehouses, we can have a look at the data the way we want it and the moment we want it. Data visualization as the name suggests is data transformed into images, which are essentially graphs, plots, charts, etc.

Think of data visualization as the translator between your raw data and the insights you need to make informed decisions. It takes mountains of data and transforms them into easily digestible charts, graphs, and maps. Imagine trying to decipher patterns or trends from a 100-page spreadsheet—that’s not just time-consuming, it’s also prone to inaccuracies. Now, picture those same insights illustrated as a trend line on a graph, or as clusters on a scatter plot—suddenly, the fog lifts, the patterns emerge, and the actionable insights become clear.

However, achieving effective data visualization isn’t a walk in the park—it requires expertise. Earlier we had an MIS executive and now we have Data analysts who plot the data the way we need it. Your company’s business teams may be incredibly talented, but if you don’t have Data analysts, it might be time to consider bringing in the experts. You will thank me later.

Why? Consider this: according to BARC Research, businesses using visual data discovery are 28% more likely to find information in time for decision-making. By investing in expert data visualization services, you’re not just buying pretty graphs—you’re investing in quicker, better-informed decisions.

Take a look at industry giants like Amazon and Netflix. Amazon uses data visualization to personalize customer recommendations, directly driving sales growth. Netflix, on the other hand, leverages it to understand viewer preferences and tailor their content strategy accordingly. These aren’t just passive, aesthetic enhancements—these are strategic tools driving their business success.

You should know why data visualization is essential in modern business, how it can revolutionize decision-making, and why expertise in this area can be a game-changer for your company.

What is data visualization?

Data visualization as we discussed earlier, in the simplest of terms is data transformed into images, which are essentially graphs, plots, charts, etc. These visuals/images come in various forms—charts, graphs, maps, and more, each chosen based on the story the data needs to tell. The beauty of data visualization lies in its flexibility. You’re not limited to just bar graphs and pie charts; there’s a whole world of visuals out there.

You are studying the path of consumers on the app or your website and what good data would do in an Excel sheet. You will gain nothing. Enter Heat maps, which can illustrate data intensity in different areas, box-and-whisker plots can show data distribution and outliers, while scatter plots can highlight relationships between different variables. Now you know in one instant which button is working, which is eye sore, and why people are scanning and not reading. So how we do define Data Visualisation?

At its core, data visualization is Defined as a methodology that takes raw, often complex data and transforms it into a visually engaging and easily understandable format. Whether it’s numeric figures from your latest sales report or qualitative feedback from customer surveys, data visualization offers a visual interpretation that aids in detecting patterns, correlations, and trends that might otherwise go unnoticed.

Gartner defines data visualization as a way to represent information graphically, highlighting patterns and trends in data and helping the reader to achieve quick insights. Also known as “interactive visual exploration,” it enables the exploration of data via the manipulation of chart images, with the color, brightness, size, shape, and motion of visual objects representing aspects of the dataset being analyzed. It includes an array of visualization options that go beyond those of pie, bar, and line charts, including heat and tree maps, geographic maps, scatter plots, and other special-purpose visuals. These tools enable users to analyze the data by interacting directly with a visual representation of it.

Remember, the goal here is not just to create visually appealing graphics—it’s to make your data work for you. Consider IBM, which saved $3.8 million in just 3 years by using visual analytics for predictive maintenance. By investing in expert data visualization, they were able to predict equipment failures and proactively address them, leading to significant cost savings. Now you know why I suggested hiring a team of Data analysts in the first place. If you don’t have one outsource it to DAAS LABS, a company that offers various data services.

Benefits of Data Visualisation

Data visualization is not just a tool, but a game-changer that can democratize information, inspire innovation, and strengthen business decision-making processes. All this is simply because you have now access to data which you can see the way you can understand it. You can now experiment, take business decisions, inform management, and of course, look like a Savvy Business manager.

Below points highlight how data visualization’s real power lies in its capacity to significantly impact various facets of a business, beyond the conventional realms of data analysis and reporting.

1. Empowers Non-technical Stakeholders: Advanced data visualizations democratize data by converting complex information into clear, intuitive graphics. This empowers non-technical stakeholders like a manager in your team who is responsible for one of the small products in your huge product lineup, to participate in business decisions, take his own and experiment the way he likes.
2. Facilitates Proactive Decision-making: All business managers are thinking all the time about various aspects of business. We need to look at data the moment we need and want. Through real-time and interactive dashboards, data visualization allows businesses to identify anomalies, trends, and outliers on the fly. This helps any business team greatly.
3. Boosts Storytelling Impact: Impressing Department heads, stakeholders, and vendors, and making a huge impression, is part of the business manager’s job. You need approvals for tasks you are planning. While numbers and statistics can be persuasive, weaving them into a compelling narrative through visualization amplifies their impact. This aids in pitching ideas or solutions both internally and externally, effectively garnering buy-in and driving action.
4. Serves as a Catalyst for Innovation: Suddenly brainstorming is so much easy. Data visualization can be a catalyst for innovation, particularly through its use in idea generation. By visualizing a wide array of data, new connections, and insights can be discovered, leading to breakthrough ideas and solutions that might not have been uncovered through traditional data analysis methods.

Types of Data Visualisations

Data visualization is not a one-size-fits-all solution. There are many types of business situations and not everything can be plotted in a graph. sometimes we need to look at data in the way it makes sense to us. Fortunately, there is a multitude of visualization types, each designed to represent different types of data in the most efficient and understandable way possible. Some common types of data visualization which we can use:

• Charts: These are likely the most traditional form of data visualization. They can be bar charts, line charts, pie charts, or even more complex variations like scatter plots or area charts. Each type of chart has its own strengths in presenting specific types of data.
• Graphs: These can be used to display relationships between different data sets, showing correlations and patterns. This could be a simple line graph showing a trend over time, or a complex network graph displaying interconnected data points.
• Maps (Geospatial Visualization): Geographic data can be represented on maps, allowing for easy visualization of location-based patterns. From simple point maps that show the location of specific data points, to heat maps that indicate data intensity in different areas, geospatial visualization can add a powerful spatial context to your data.
• Infographics and Dashboards: These forms of visualization combine various visual elements to present a comprehensive view of data. Infographics typically use charts, diagrams, and text to tell a story with data, while dashboards collate different data visualizations into a single interface for easy monitoring and analysis.

Specific Data visualization examples which may help

1. Area Chart: An area chart displays quantitative data visually. It is based on the line chart, but the area between the x-axis and the line is filled in, providing a sense of volume.
2. Histogram: A histogram is a graphical representation that organizes a group of data points into a specified range, showing the frequency of the data points in each range.
3. Pie Chart: A pie chart represents data in a circular format with segments depicting each category’s percentage of the total.
4. Bar Chart: Bar charts represent numerical values compared to each other using bars of varying lengths.
5. Box-and-Whisker Plot (Box Plot): A box plot, or a whisker plot, displays a summary of the set of data values including minimum, first quartile, median, third quartile, and maximum.
6. Bullet Graph: A bullet graph displays performance against a set measure, such as a goal or a target.
7. Gantt Chart: Gantt charts are primarily used in project management to illustrate project schedules including start, finish, and duration of tasks.
8. Treemap: A treemap displays hierarchical data as a set of nested rectangles, where the area of each rectangle corresponds to its numerical value.
9. Line Graph: A line graph is a type of chart used to visualize the value of something over time.
10. Geospatial Map (Choropleth/Geographic Heat Map): This type of visualization uses geographic maps and colors to represent data associated with different regions or locations.
11. Bubble Chart: A bubble chart is a scatter plot in which a third dimension of the data is shown through the size of dots.
12. Waterfall Chart: A waterfall chart is a form of data visualization that helps in determining the cumulative effect of sequentially introduced positive or negative values.
13. Heat Map: A heat map uses color gradients to display the density of data. It’s a way of representing tabular data where the cells are colored depending on the contained value.
14. Highlight Table: A highlight table uses colors to draw attention to specific parts of the data. It’s a combination of a table and a heat map.
15. Scatter Plot: A scatter plot uses dots to represent values for two different numeric variables. The position of each dot on the horizontal and vertical axis indicates values for each data point.
16. Stacked Bar Chart: A stacked bar chart shows a part-to-whole relationship among data. It’s an extension of a bar chart where each bar is broken down into different categories.

Choosing the Right Data Visualization Tool for Your Business

As we discussed before, you need an expert to bring in the right visualization for your teams. Here are some of the tools these teams of data analysts bring to the table. There are more but I am listing only the top 3 data visualization tools.

Tableau: This is a highly interactive data visualization tool that has an intuitive interface. It supports a wide variety of charts, graphs, and maps and can connect directly to databases, cloud services, spreadsheets, or even big data processors.

Power BI: Microsoft’s Power BI is a business analytics tool that delivers insights throughout your organization. It can connect to a vast range of data sources, simplifies data prep, and drives ad hoc analysis. It also produces beautiful reports and publishes them for your organization to consume on the web and across mobile devices.

D3.js: This is a JavaScript library for creating dynamic and interactive data visualizations in web browsers. It’s very flexible but also quite complex, making it best suited for developers with strong programming skills.

The best tool for your business will depend on your specific needs, the skills of your team, and your budget. However, regardless of the tool you choose, the goal remains the same: to take business decisions faster and better, to experiment more and more.

Innovation and The future of data visualization

Imagine if you could test your business Hypothesis in real time, wouldn’t that be interesting. There has been a surge of creativity and innovation in the field of Data visualization. Increasing use of interactive visualizations that allow users to explore and manipulate data in real time.

There’s also been a rise in the use of data storytelling, where visualizations are used to weave a compelling narrative about what the data means. Simultaneously, advances in technologies like artificial intelligence (AI) and machine learning are bringing about a paradigm shift in how we visualize and interpret data. Looking ahead, developments such as predictive analytics and real-time data visualization promise to further revolutionize the field. let’s delve into these interesting topics together without further delay.

1. Interactive Visualizations: This technique allows users to engage with data directly. They can zoom in/out, hover to get detailed insights, filter data, or even manipulate data in real-time. This interactive approach makes data exploration intuitive and user-friendly.
2. Data Storytelling: Here, visualizations are used to narrate a coherent story. It’s not just about presenting data, but about providing context, drawing connections, and explaining the implications of the data.
3. Immersive Visualizations: With VR and AR, data visualization can be truly immersive. Users can explore data in 3D, allowing for a more holistic understanding.
4. Geospatial Visualizations: Techniques like heat maps, choropleth maps, and GIS-enabled visualization tools are now commonly used to display geographical trends in data.
5. Automated Visualization: AI can examine the data and automatically suggest the most effective visualizations, taking into consideration the type of data, the correlations present, and the user’s needs.
6. Highlighting Key Insights: AI and machine learning algorithms can sift through vast amounts of data to highlight key insights and patterns, which can then be visualized for the user.
7. Conversational Analytics: AI is being used to enable users to query their data using natural language, with the answers provided in the form of relevant visualizations.

As we move forward, we can expect several emerging trends to shape the future of data visualization. Predictive analytics, for example, will become increasingly mainstream. Rather than just showing what has happened, visualizations will use statistical models to forecast what is likely to happen in the future. Real-time data visualization is another exciting area.

As more and more systems operate in real-time, there’s a growing need for visualizations that can keep up – showing what’s happening as it happens. Just like sports analytics, it predicts the game as it proceeds

• Predictive Analytics: The focus of data visualization is shifting from what has happened to what could happen in the future. Predictive models are used to forecast trends, which are then visualized to aid in decision-making.
• Real-Time Data Visualization: As more systems generate real-time data, the need for real-time visualizations is growing. Such visualizations update dynamically, providing a live view of the situation.
• Integration with AI and Machine Learning: Expect to see smarter visualizations that can understand context, learn from user behavior, and adapt to provide the most relevant insights. AI can help to highlight the key patterns in data, while machine learning can help to create models that can be visualized to predict future trends.

People also confuse Data visualizations and Visual Analytics. Know the Difference. Read what is visual Analytics.

Revolutionizing Business Intelligence: How SCIKIQ’s Innovative Approach Simplifies Data Visualization

Business Intelligence (BI) systems can be a tangled web of different tools and platforms. This can lead to scattered, hard-to-manage data. SCIKIQ is stepping up to simplify this landscape. we’re introducing new solutions that bring these varied tools together, making data analysis and reporting much more efficient. This work has not gone unnoticed; Forrester recently highlighted SCIKIQ’s innovative approach to BI platform rationalization. Check the Best Practice report by Boris Evelson and his team. This also makes one of the feature of our Data lineage product.

SCIKIQ’s approach starts by creating a common semantic layer for multiple BI platforms. This semantic layer separates BI developers and users from the complexities of underlying physical database structures. By implementing a common semantic layer, each platform uses the same business glossary and metrics, leading to a single version of the truth and single trusted source of data.

In essence, SCIKIQ’s advancements in the BI space are redefining how businesses handle data, offering solutions that cut through the complexity to offer clarity, consistency, and efficiency. With these innovations, SCIKIQ is leading the charge in transforming the world of enterprise BI. Know more about this here at the SCIKIQ Consume page for Data Visualisations.

The Future of Data visualization as a practice

As we look toward the future, data visualization stands at the precipice of even greater innovation, spurred by rapid advancements in technology. It’s not far-fetched to envision data visualization centers of excellence (COEs) emerging within organizations, underscoring its strategic significance. Companies like DAAS LABS who are specialised service companies in the Data space are bringing up centers of excellence in Data visualization for other organizations since it is now shaping as specialized practice.

The innovative approaches of companies like SCIKIQ demonstrate the power of data visualization, moving beyond traditional methods to create systems that not only bring clarity but also increase efficiency and productivity. It’s clear that the path forward in business intelligence lies in the effective visualization of data, an arena where expertise and innovation can truly set a company apart.

As we continue to generate and collect more data, the importance of robust data visualization techniques will only increase. Businesses looking to stay ahead must recognize the transformative power of effective data visualization. It’s more than just nice to have; it’s an essential tool in any data-driven organization’s arsenal.

We hope this blog has illuminated the intricacies of data visualization and its critical role in modern business. Remember, the power of data lies not just in its collection, but in its interpretation and understanding. Visualization is the bridge that connects raw data to insightful action. So, whether you’re a billion-dollar company or a small start-up, harnessing the power of data visualization could be your next step toward achieving your business goals.