In the past few decades, data has become remarkably beautiful to look at. Every dataset in our surroundings is visualized for our clear understanding. From the weather morning report at the start of our day to fitness tracking apps; data has transcended 0’s and 1’s and now takes the form of beautiful visualizations. A great example is the system used by many public transportation systems that use data visualization in the form of maps, routes, stops, and connections.
But how did our society get to this point?
Data visualization is the graphical representation of data that helps us understand complex information more easily. It benefits us by enabling quicker identification of patterns, trends, and relationships within data, facilitating better decision-making. It also enhances communication by making data more accessible and engaging to a wide range of audiences, promoting effective collaboration and knowledge sharing.
Now, that we know why data visualization is crucial – how do we get started?
Kanerika’s Three-Step Journey To Data Visualization
Data visualization is a journey. In today’s technologically advanced world, companies actively collect an immense amount of data that not only poses challenges in mapping but also necessitates cleaning before starting out.
Over years of observation, we have summed up the data visualization process into a funnel – beginning with data cleaning, moving on to the next step of data transformation, and finally arriving at data mapping and visualization.
Let’s understand each of the steps in brief.
Data cleaning plays a vital role in data visualization as it eliminates errors, inconsistencies, and duplicates within the dataset. Addressing formatting issues and filling in missing data, ensures the accuracy and reliability of visual representations. A thorough data-cleaning process enhances the clarity of insights and prevents misleading interpretations.
Data transformation is crucial for data visualization because it enables the conversion of raw data into a suitable format for visualization tools, ensuring accuracy and consistency. Transformation allows for data aggregation, filtering, and normalization, enhancing the effectiveness and interpretability of visual representations.
Now that we have already explored two out of three steps – the third step must seem obvious to you. Data Visualization – the process that simplifies data insights for users. A process that extracts the most important information from raw data/big data and allows business users to derive key business insights.
However, Data Visualization can be notoriously misleading to the untrained eye. Moreover, it is heavily dependent on good-quality data for meaningful insights. Incorrect data, or bad data, can generate misleading data visualization reports that project incorrect information. This can lead to businesses losing out on revenue and making incorrect decisions – a trend that has increased over the past decade due to a large influx of big data for companies.
Recognizing this trend, our team at Kanerika has created the perfect tool to suit the need of every business across industries. A tool that simplifies data integration and transformation. One that ensures your data visualization tool has the correct data to generate reports from. Introducing FLIP!
The AI-Powered FLIP That Visualizes Data For You
FLIP is an AI-powered data operations platform that optimizes data visualization. It provides businesses with the necessary capabilities to transform and process their data effectively.
And the best part? We have designed FLIP to be used as a zero-code tool that can directly plug into your data lifecycles. Users can utilize it without relying on direct assistance from data engineers or developers. FLIP’s dashboard allows users to import and configure data and segregate them into programs while assigning due dates, partners, and team members responsible for the program.
But we didn’t stop there. We took it a step further to ensure process configuration and process trends can be viewed directly via the dashboard. FLIP’s integration with visualization tools further facilitates the creation of charts, graphs, dashboards, and other visual representations of data, making it easier for users to interpret and communicate information effectively.
Making Data Visualization Easy with Effortless Data Transformation
FLIP allows users to organize their data into folders of their choice. Users can employ it across various industries including healthcare, fintech, banking, finance, insurance, logistics, HR, and more!
Furthermore, FLIP’s drag-and-drop interface and user-friendly features simplify connecting data sources, defining transformations, and configuring visualization outputs.
While all the imported data is available to the user in designated folders, FLIP enables users to effortlessly map it into fields. Fields such as string, numeric, date, validation, and variable. This enables users to customize data fields, resulting in highly effective data visualization graphs that are segregated based on custom variables.
For instance, for a healthcare professional, having all the insurance data tagged and configured in one place is challenging. However, FLIP solves that problem. An insurance program can be spread across multiple variables. These include Record Type, Record ID, Program Name, Policy Number, Patient Name, Patient Address, Entry Date, Coverage Limit, Coverage Expiration Date, and Transactions. This allows for a far greater scope of data visualization. Therefore, enables businesses to view individual data variables through the visualization tool of their choice.
The Future Of Data Visualization With FLIP
By seamlessly preparing data for the visualization process, FLIP enables businesses to maximize the potential of their data visualization tools. Additionally, we have designed it to create data transformation pipelines on the go. You can now automate tasks such as data cleaning, aggregation, and formatting.
Experience the ease of FLIP’s automated processes, and leverage data visualization as a powerful tool for analysis and decision-making.