How to Use AI to Automate Data Processing and Visualization

With the rise of automation and AI, it seems crazy not to use AI market research tools in your projects. Automation and AI simplify survey setup and data analysis, freeing up time for strategic planning and corporate action.

Researchers can use automated data analysis and AI techniques to make data-driven decisions without manual labor.

Utilizing AI for data analysis and visualization

AI solutions are gaining acceptance in all areas, including market research. This is interesting for researchers who want to do more with less (resources, time, and funding).

Automation and AI are improving market research and data analysis in several areas.

AI can assist with data collection by creating high-quality surveys with meaningful questions that lead to actionable findings. Artificial intelligence (AI) technologies can stimulate academics with question framing and input ideas they may not have considered.

Researchers can use AI in data analysis to uncover overlooked insights. In particular, AI co-pilots can evaluate data sources to generate chart headlines and key insights with a click. AI tools can predict analytics in your data, helping organizations foresee resources and act on new trends before it’s too late.

AI-enabled data reporting and visualization allows researchers to quickly extract key findings from reports, providing stakeholders with clear action plans and next steps. AI tools can swiftly recognize patterns, forecast trends, and analyze data, giving researchers intuitive, automated data visualizations for even the most complicated advanced research methodologies.

The value and challenges of AI data analysis

Working in market research and data analysis is exciting due to AI tool development. Researchers and data analysts should feel empowered to use AI to reduce laborious analytical activities and focus on the “so what” of the customer story. Data analysis with AI speeds up decision-making, improves accuracy, and scales.

Fortunately, for engaged market researchers, artificial intelligence and data analytics automation cannot fully replace insights specialists and researchers. Automated data analysis still requires human intervention. AI can spark ideas, but every big business decision should be based on conversation and data humanization. Qualified insight specialists must identify algorithm bias, even as AI can reduce researcher bias.

AI should inspire and guide data-driven decision-making, not replace a seasoned research team. AI technologies are like adding a great team member that helps your business perform more with less—the ultimate research fantasy.

Quantilope allows for AI-driven data analysis

Whatever your research experience, Quantilope’s Consumer Intelligence Platform makes data analytics as easy as a few mouse clicks. Machine-learning and AI-driven algorithms mean you no longer have to wait days (or weeks or months) for a dataset from a data processing or data science team.

Start analyzing your data when the first few survey respondents finish. Click between chart visualizations in seconds, toggle significance testing without tedious calculations, and cut data by any survey variable. Save your analysis charts and choose which ones to add to your final insights dashboard for stakeholders.

Assuming you lead the Insights team at a large CPG firm (congrats on the impressive gig), your CEO wants an update on your newly launched study on a new product release by the end of the day. Despite your ‘urgent’ email subject line, without an automated research tool, you may stress about how long it will take to acquire early data tabs from your data processing staff.

Fortunately, Quantilope’s technology lets you filter and cut data, make reports, and create a dashboard in real time. You can also use Quantilope’s AI co-pilot, Quinn, to write chart headlines and footer descriptions. Click the ‘share’ button at the top right of the dashboard to send it to your CEO in less than an hour. Modern business intelligence.

Without a platform like Quantilope to automate data analysis, researchers must manually check the data’s accuracy, input data metrics into charts (with the risk of human error), and create their own data visualizations from scratch. This analytics process no longer works for stakeholders that want to keep up with consumer decision-making.

 Read More: The Role of Data Integration in Contemporary Data Sourcing

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