How to Use AI in Data Visualization?
by Ramesh Panuganty, Founder & CEO
How to Use AI in Data Visualization?
by Ramesh Panuganty, Founder & CEO
Humans have always found learning and sharing insights through visualizations helpful – be it the early human cave paintings depicting constellations, Florence Nightingale’s rose diagram on causes of mortality in wars, Dr. John Snow’s map on tracking cholera in London, Charles Minard’s cartographic flow chart of Napoleon’s army’s losses, William Playfair’s time series area chart on England’s imports and exports, or the recent Covid-19 visualizations that helped us track the world-wide pandemic.
Data visualizations help to draw relationships between various data points, identify clusters of similar characteristics, and spot emerging patterns and trends. Data visualizations include line, bar, and area charts, geographical maps, scatter plots, heat maps, tree maps, infographics, dashboards, and audio-visuals. When applied to enterprise data and business use cases, data visualizations can explain the insights extracted from data in graphical and creative formats that bring clarity and aid understanding.
What is the need for AI in data visualization?
The information age boosted the creation of data visualization, thanks to computers and spreadsheet applications such as Microsoft Excel. Data could be digitized, categorized, and presented visually in the form of graphs, charts, and pie diagrams easily. With the advent of Business Intelligence (BI) tools, data visualizations become refined and easy to share using dashboards. But as data continues to increase, traditional visualization tools struggle to process massive datasets, present a comprehensive picture, and offer granular insights.
Creating engaging and aesthetically pleasing visualizations takes a lot of human effort and time. Further, personalizing them for a specific department or region, a business conference or annual review meetings, or an important stakeholder requires additional efforts. This delay can cause the insights to become stale by the time they reach a decision maker.
The delays do not stop at creation, but increase multifold when it comes to updating the visualizations. As business situations keep changing and new information pours in, the data visualizations need to be constantly refreshed and updated. Static dashboards lose their value and daily chart requests keep stacking up, clogging the information flow and burdening IT teams and analysts with mundane tasks. This repetitive process consumes precious resources in terms of money and time that can be better invested in innovations and actions.
The need for AI in data visualization arises to simplify and expedite the process of converting insights into visualizations. AI-powered data visualizations can present insights faster in engaging formats, provide best-fit visualizations based on the insight type, and make visualizations interactive to drill-down and access granular insights faster. AI automates data visualization, and eliminates human efforts and time delays involved in creating and updating visualizations.
How to use AI in data visualization?
AI can implemented and used in data visualization in the following ways:
When visualizations are created using a manual process, there is a time lag between when an anomaly is found and when its visualization is created and provided to the relevant stakeholders. With AI-powered visualizations, real-time monitoring of systems, transactions, and activities is possible and potential threats can be flagged early.
The type of visualization used to depict an insight greatly impacts its interpretation and understanding by the users. For example, a cluster is better represented in a scatter plot than a bar chart. Similarly, an answer which results in a single numeric value such as “what are the sales for Q2” does not need an elaborate visualization. AI can interpret quickly the best-fit visualizations for a particular insight and generate them instantly. Refreshing the insights in visualizations based on latest data is also done in an automated manner, saving time and effort.
AI can not only interpret what happened in the past but also predict what will happen in future based on its advanced algorithms and machine learning models. By establishing correlations and identifying patterns in data, AI can visualize how the trends will proceed in future. AI-based visualizations can improve the predictions and help users clearly see the future course of events.
Data is not limited to just numbers and facts. It also involves a lot of qualitative aspects such as reviews, feedback, staff interactions, service experience, telephonic conversations, feelings associated with a brand, and customer buying behavior. AI is efficient in analyzing such sentiments and explaining negative, positive, or neutral associations with the product or service through data visualizations.
Customized and Granular Insights
AI-powered visualizations can be tailored to suit the specific needs of a decision maker or a business function. The scope of information, type of visualization, and frequency of creation and updates can be managed efficiently with AI for data visualizations. The ability to drill-down visualizations and access granular insights without initiating new searches makes data exploration very easy for decision makers.
Use cases of AI in data visualization
Here are some industry-wise use case where Ai can be seen in action in data visualizations:
Healthcare and Medical Research
AI-powered visualizations can help medical researchers analyze patient data, disease symptoms, and effectiveness of medicines. It can help them measure outcomes of clinical trials, identify patterns in spreading of infections, and compare different courses of actions to arrive at the most impactful solutions.
Banking and Finance
Visual analysis of financial transactions can help financial institutions detect irregular patterns, potential financial frauds, and avoid losses. AI-powered data visualizations can also bring useful insights into cross-selling financial products and offer recommendations on optimizing investments.
Retail and E-commerce
By learning about customer habits and preferences, AI can provide product recommendations, engage in targeted conversations, and customize offers. Retail managers can use AI-powered data visualizations not only to understand behavior and preferences, but also to forecast demand, create strategies, and track inventory.
AI-powered data visualizations can help marketing teams quickly visualize market trends, changing preferences, and effectiveness of various marketing channels. These visualizations can help them create and present marketing strategies to stakeholders in an engaging way.
Benefits of AI in data visualization
AI-powered data visualizations provide the following benefits:
Make complex concepts easy to understand
IT is very difficult to decode insights by reading lengthy reports or looking at static dashboards with myriad tiles. Various researches have proved that people learn better through visuals and graphical representations. The use of AI in data visualizations help explain and present complex insights in a simple and understandable manner. Along with visualizations, AI can also generate text summaries that explain the visualization and help users focus on finer details.
Expedite access to insights
The creation of AI-powered data visualizations can be automated and expedited. Charts, maps, and audio-visual narratives can be generated on-the-fly as soon as a search question is posted. This eliminates the time delays required in processing the request, deciding which visualization type can best present the insight, and actually creating the visualization.
Enhance insights through storytelling
Visual storytelling is a powerful medium of explaining relationships between various data points and revealing how they impact the outcomes. Using AI generated audio-visual data visualizations are instrumental in conveying insights in the form of storytelling. They not only explain what happened but also why it happened and how certain factors proved to be influential.
Encourage deeper data exploration
AI in data visualization brings insights to life by making them interactive and dynamic. Unlike static visualizations, AI-powered data visualizations enable users to change point of views, drill-down details, focus on specific elements, expand scope of query, compare distinct attributes, and examine granular aspects. These abilities help uncover patterns, reveal correlations, and spot trends easily.
Provide customizations for personalizing insight consumption
AI-powered data visualizations can offer delightful data experiences to decision makers by providing customized insights. Based on their interests, work, geography, or access privileges, data visualizations can provide focused insights on what’s actually important and relevant to each decision maker. Bite-sized visualizations on automated business headlines ensure that decision makers are always aware of their business events.
Make smarter decisions with MachEye’s AI-powered data visualizations
With its AI-powered Intelligent Search, MachEye provides an intuitive interface to search and consume insights. The insights are presented in best-fit visualizations, along with various chart options, so that users can explore and visualize data from different perspectives. MachEye offers various controls using which organizations can apply their branding to visualizations, change colors, drill-down on various attributes, and focus on specific values. This interactivity enables users to explore freely and access related information, without initiating new searches.
Audio-Visual Data Stories
MachEye presents actionable insights in the form of not just charts but also audio-visual data stories. When users enter a search phrase, MachEye not only finds the answer but also finds related insights such as anomalies, analogies, clusters, trends, business drivers, and root causes. All these advanced insights are compiled in an audio-visual data story that is created on-the-fly using AI-powered data visualizations. Organizations can also add their custom visuals and configure carousels to display insights visually.
The insights extracted by MachEye can be organized in dashboards as vignettes using various layouts. Once added, these insights do not remain static. To view the latest insights in a dashboard, users can refresh either individual vignettes or refresh the entire dashboard in a single click. Refreshing of dashboards can also be scheduled in advance. This is useful especially for data that needs to be tracked on a periodic basis such as monthly sales dashboards, quarterly financial positions, weekly targets, and so on.
Easily Shareable Insights and Presentations
MachEye’s AI-powered data visualizations also foster collaboration and teamwork. The visualizations can be easily shared with specific individuals or user groups. Data access privileges can also be applied to shared visualizations so that receiving users can see only the data that is within their defined access. When it comes to presenting insights to stakeholders, dashboards can be converted into presentation slides in one click. This saves users time and efforts required for creating presentations separately to share their compiled insights.
The visualizations generated by MachEye can be embedded easily into existing business systems and applications. This provides business users access to visual insights at the point of decision making, thus promoting a data-driven decision making culture within the organization. MachEye’s AI-powered data visualizations offer users a seamless experience of consuming insights in their familiar work interfaces.
MachEye integrates the powers of Generative AI, Natural Language Processing (NLP), and Machine Learning (ML) technologies to understand simple language search queries, identify user’s intent behind a query, and provide instant contextual answers with actionable insights. With its intelligent search, interactive audio-visuals, and actionable insights, MachEye offers a modern analytics experience that simplifies analytics, gives more control to the users, reduces time-to-insights, promotes a data-driven decision making culture, and integrates seamlessly in existing applications.Start Your Free Trial