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In today’s data-driven world, collecting, cleaning, and analyzing data are critical processes that drive decision-making, strategy development, and innovation in both research and business. These steps ensure data quality and accuracy, leading to meaningful insights and informed decisions that can improve efficiency, increase innovation, and manage risks.
Why Acquire and Analyze Data? In today’s data-driven world, the importance of collecting, cleaning and analyzing data is becoming increasingly important. Data forms the backbone of decision-making, strategy development and innovation. This is true in business and in research.
Below I discuss a couple crucial elements of the data cycle and how they add value in both research and business.
Data collection is the first step in the data lifecycle. It involves gathering information from various sources, such as surveys, experiments, transactions, web traffic, social media, sensors, among other places. Effective data collection ensures that the data is relevant, accurate and comprehensive. Without ensuring that your data is up to the standard that is necessary to derive the insights that you’d prefer, at best you may deceive yourself into finding something meaningful that doesn’t exist and at worst you may label something as important that may negative impact your long-term outcomes.
Example in Research: In medical research, collecting data from clinical trials is essential to understand the effectiveness of a new drug. Researchers gather data on patient demographics, treatment methods, and health outcomes. This data is then used to determine the drug’s efficacy and safety, influencing healthcare policies and patient treatments. However, if you collect noisy data and utilize methods that incur biases, you may incorrectly interpret as a drug failing when it may in fact work in an important population.
Example in Business: Retail companies collect data on customer purchases, preferences, and feedback. This data helps businesses understand consumer behavior, forecast demand and optimize inventory management. However, if you ask the incorrect customers of your customers you may be leaving important information on the table. Missing a crucial opportunity to build a connection with your customers and increase your growth.
Once data is collected, it often contains typos, irregularities, or missing values, or requires necessary transformations. Data cleaning involves detecting and correcting these issues to ensure the data’s quality, reliability and scaled in a manner that is relevant to the question and units of interpretation. Clean data is essential for accurate analysis and meaningful inferences.
Example in Research: In neuroimaging research, magnetic resonance imaging may contain issues due to motion artifacts. For example, clinical populations often struggle keeping still and so this degrades the quality of the images acquired. By using preprocessing and data cleaning steps, researchers can remove problematic images and clean up residual noise to improve the signal that can be used for clinical reading and interpretations.
Example in Business: A marketing team might collect customer data from various sources such as online forms, social media and/or transaction history. This data might have duplicates or missing data. Cleaning this data ensures that customer profiles are accurate, missing data is treated according to the establish protocol and data is excluded when necessary to enable personalized marketing strategies and improving customer engagement.
In many ways, if you’re at the data analysis stage the majority of the hard work may already been done. Collecting, aggregating and cleaning/preprocessing data are often the most tedious and time consuming steps.
Data analysis is the process of examining, transforming, and modeling data to test your questions or discover useful insights. The more time you spent on the front-end on data collection and quality, the better your insights will be on the back-end. Data analysis is a crucial step in uncovering patterns, trends and associations between your variables/data points of interest.
Example in Research: In social science research, analyzing survey data can reveal insights into human behavior and societal trends. For example, analyzing data on education levels and income can help researchers understand the impact of education on economic outcomes, informing policies to reduce inequality. Since income can be measured in various ways, it is often important that the level in which income in measured is relevant to the question and the noisiness is limited.
Example in Business: E-commerce companies analyze web traffic and sales data to optimize their online platforms. By understanding which pages attract the most visitors and which products have the highest conversion rates, businesses can improve user experience and increase sales. However, sometimes pages may not be a perfect apples-to-apples comparison. If an e-commerce business did not do it’s due-diligence in maintaining error free webpages or boosting search engine optimization details, a business may erroneously conclude that webpage A is more valuable to invest in for sales than webpage B. However, if webpage B contained errors, investing in B may be more valuable than A.
Collecting, cleaning, and analyzing data are integral steps that unlock the true potential of data. A robust data pipeline can enable groundbreaking discoveries and advancements in knowledge drives strategic decisions, efficiency, and/or competitive advantage.
Don’t ignore your data, embrace it.
PhDs wear many hats.
The following project is about video games, but this workflow can also power many real world applications. You can use this same method to digitize multi-page reports, consolidate photographed receipts, or reconstruct contracts from a series of images. Basically, it’s a technique to convert fragmented sets of images into a single, usable piece of text.
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Using AI to refine structured descriptions of products in Shopify to maximize the customer experience.
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