What do we mean by Normalization
Normalization in data analytics refers to the process of organizing data so it is consistent and reliable. This involves making sure data is in a similar order and format across different sets. Normalization can involve several steps, including removing duplicate data entries, standardizing data formats and values, converting data to a common language, and ensuring data fields match a uniform structure. By normalizing data, businesses can improve data integrity by ensuring the data is accurate and facilitate analysis by making the data easier to work with for analytics and visualization.
For instance, consider a company collecting customer feedback from different regional offices. Each office might use different survey formats and languages to ask about "customer satisfaction". Normalization makes sure all responses are understood and formatted the same way, regardless of these differences. This makes it easier to analyze the feedback and ensures the results are accurate.
Normalizing Data with LLMs
At Tracer, we harness the incredible power of Large Language Models (LLMs) to automate and enhance the process of normalizing data on an extensive scale. This ensures that even the most complex and varied datasets can be efficiently normalized, providing businesses with clean, structured data ready for insightful analysis. By integrating OpenAI's technology, we've created a specialized OpenAI assistant that excels in converting unstructured and inconsistent data into clean, structured datasets ready for visualization and analysis. Our cutting-edge approach employs LLMs to tackle complex data normalization and combines this in Tracer’s business intelligence platform to ensure that businesses are empowered to make well-informed decisions effortlessly.
Data Normalization Case Study
Imagine being handed 57 distinct datasets, each representing survey data for different websites from different countries for a large global online brand. These surveys, aimed at understanding website navigation ease, presented several challenges. Each dataset was in a different language, with questions and columns arranged in varying orders, and there were 17 different ways of asking about the ease of website use.
Traditionally, this would require manually translating each survey into one language (typically English), identifying common questions, and manually integrating them into one dataset—a time-consuming and error-prone task. With Tracer, this complex process can be completed in under an hour, compared to the 25+ hours required for the manual approach. This means not only significant time and cost savings but also more accurate and consistent data for better decision-making.
Tracer’s Role in the Data Normalization Process
Tracer’s data intelligence platform played a crucial role in the data normalization process. A Tracer AI assistant, created using OpenAI technology, was applied to each survey to identify which questions pertained to website ease of use. These identified questions were then translated allowing all the datasets to be normalized. The normalized data was subsequently pushed to Tracer, our data intelligence platform, for streamlined reporting and analysis. Our platform's flexibility and scalability further ensured that the data could be leveraged effectively across various business functions. This seamless integration facilitated more efficient and insightful data analysis. See the image below for more details on the workflow with Tracer.
The Future of Data Processing
The combined power of Tracer, enhanced by OpenAI technology, showcases its ability to transform how global businesses handle and process large-scale datasets. Tracer’s innovative approach empowers businesses to make smarter, data-driven decisions. By automating the tedious aspects of data normalization, Tracer helps companies prepare data for analysis and for developing their own AI solutions. This not only streamlines workflows but also allows businesses to focus on their core competencies and make significant strides in their respective fields.
For businesses focused on analytics, integrating numerous complex datasets to prepare for analysis and glean insights is a common challenge. Our use case demonstrates the immense value AI can provide in this context. With Tracer, firms can concentrate on interpreting results and generating actionable insights, enhancing operational efficiency and improving accuracy.
Embrace the future of data processing with Tracer and experience how AI can unlock unprecedented levels of efficiency and insight, empowering your business to achieve more.