The Top 5 Most Important Data Collection Use Cases to Know in 2024
In today‘s data-driven world, organizations rely on collecting quality data for a variety of critical purposes. Based on comprehensive research into industry trends and practices, I‘ve identified the top 5 most impactful data collection use cases that technology leaders should understand going into 2023:
- Training AI and machine learning models
- Deploying and monitoring AI/ML models
- Improving and updating existing AI/ML models
- Conducting market research
- Optimizing search engine optimization
In this article, I‘ll provide an in-depth look at each use case, complete with examples, supporting data, and best practices you can apply to get the most value out of data collection. By the end, you‘ll have an expert overview of how leading organizations leverage data collection to power key initiatives and drive competitive advantage. Let‘s dive in!
1. Training More Accurate AI and Machine Learning Models
One of the most common and critical reasons companies invest in data collection is to train AI and machine learning models. In 2022, global spending on AI exceeded $62 billion as businesses rely on AI to unlock insights, automate processes, and enhance products.
But for machine learning algorithms to work effectively, they require massive training datasets relevant to their intended task. The more quality data you feed these models during training, the more accurate they‘ll become at making predictions and decisions.
According to an NVIDIA study, model accuracy consistently improves as the volume of training data increases, regardless of model type. For example, error rates for image classification models dropped from over 30% to under 5% as the dataset expanded from 1,000 to 1 million images.
Source: NVIDIA
This means organizations need access to large, well-labeled datasets to train computer vision and natural language processing systems. Self-driving car companies might use billions of images showing pedestrians, traffic signs, and other objects to develop perception models.
Meanwhile, banks need vast transaction histories to identify fraud, and retailers require years of sales data to forecast demand. But collecting training data at the scale and quality needed remains a top challenge for many businesses starting AI initiatives.
That‘s why leading organizations partner with data annotation specialists who can quickly label thousands of hours of video, audio, and text data to enable AI training. For instance, companies like Appen and Scale AI offer data labeling services covering over 100 languages tailored to verticals ranging from automotive to healthcare.
By leveraging qualified data annotation partners, you can rapidly build the datasets required to train innovative AI systems that give your organization a competitive edge.
2. Deploying and Monitoring AI Models in the Real World
Data collection doesn‘t stop once you‘ve trained an AI model. You also need fresh datasets to responsibly deploy and monitor models in production environments.
Of a standard machine learning dataset, around 60% goes towards training and 20% for validation during development. But the remaining 20% should be reserved for model testing in the real world.
New test data evaluates your model‘s performance on current, real-world examples of the problem it aims to solve. This guards against overfitting on the training data which may not fully generalize.
For instance, say you built an AI model to detect credit card fraud. If you only test it on past data used in training, your model could perform poorly on new types of fraud. But testing on fresh transactions provides a true assessment before deployment.
Ongoing data collection after release enables continuous monitoring as well. In practice, model performance tends to degrade over time due to "concept drift" – when the distribution of real-world data changes relative to the training data.
Your fraud detector may decline in accuracy as new fraud patterns emerge. Or a retailer‘s demand forecasts could suffer if customer purchasing behavior shifts. With continuous data feeds, you can evaluate if model accuracy begins drifting below acceptable thresholds.
This signals when it‘s time to retrain your model on new data or rebuild it altogether to adapt to changing real-world conditions. Disciplined data collection protocols make deploying and managing AI in production smoother.
3. Improving and Updating AI Models Over Time
Speaking of concept drift, a major application of data collection is improving and updating AI systems after deployment. Retraining on fresh datasets allows you to maintain – or even boost – your model‘s accuracy over time.
As mentioned above, model performance tends to degrade as real-world data changes. Say you developed an ML model to assess loan default risk two years ago. The data patterns in your training set likely look very different today.
Default risk may have increased across demographics due to macroeconomic impacts of the pandemic. By regularly collecting new credit and loan data, you can retrain your model to account for these emergent trends.
With frequent retraining, you can nip performance declines in the bud before they significantly impact business outcomes. Or better yet, use new data to enhance accuracy beyond original levels.
For instance, Google leverages user feedback to continually train its speech recognition models. In 2021, Google reduced its speech transcription error rate from over 8% in 2017 to just 2.6% – a remarkable achievement. Ongoing data collection fueled these gains.
You don‘t need Google-level scale to benefit here. Set your models up for continuous improvement by establishing data collection pipelines that bring in fresh, high-quality data over time. Partner with labeling experts if needed to annotate retraining datasets cost-effectively.
4. Conducting Quantitative and Qualitative Market Research
Beyond AI applications, data collection powers market research efforts that fuel strategic decisions. 85% of companies rely on market research to understand target buyers, gauge demand, set pricing, and validate concepts.
Both primary research (collecting new data directly) and secondary research (using existing data) require access to relevant, accurate datasets. For primary research, you need mechanisms to gather first-hand data at scale.
Online surveys are a popular approach – if designed well, they can provide a wealth of quantitative data on customer preferences, pricing thresholds, feature needs, and more. You may collect hundreds or thousands of survey responses to spot trends.
But quantitative data only tells one side of the story. Qualitative insights from focus groups, interviews, and observational studies are crucial for putting data in human context. These efforts demand careful participant recruiting and screening.
Partnering with specialized research panels and communities allows you to gather quality insights from target demographics like IT decision makers or healthcare workers. The right partners minimize time spent finding participants.
Meanwhile, tools like sentiment analysis help parse unstructured feedback data from sources like social media, reviews, and call center logs to identify trends. With the insights gleaned from disciplined data collection, you can strategize confidently.
5. Optimizing Search Engine Optimization
If you operate an online business, data collection is indispensable for optimizing search engine optimization (SEO) and maximizing website traffic.
Unique, high-quality website content ranks better in search results. But creating fresh content at scale takes extensive data gathering and analysis. Product descriptions, localized translations, blogs, and other website copy require relevant, up-to-date data.
Ecommerce players may need to write thousands of product descriptions across a vast inventory. Rather than manually compile this data, leading retailers automatically pull in structured data like price, attributes, and imagery to build descriptions.
Meanwhile, SEO-focused content benefits from researching relevant keywords and analyzing competitor sites to identify gaps. With the right data piped into workflows, you can scale optimized content creation to boost organic search performance.
Tools like Searchmetrics and Ahrefs help collect website traffic data, analyze competitor domains, and determine optimal keywords. Combining content optimization with keyword research unlocks major SEO wins.
Key Takeaways on Leveraging Data Collection
To recap, these five use cases represent prime opportunities to derive tremendous value from comprehensive data collection:
- Training accurate AI models
- Deploying and monitoring robust AI systems
- Continuously improving AI accuracy over time
- Conducting insightful market research
- Optimizing content and assets for search engines
But focusing on use cases is only half the equation. You also need to collect data ethically, control quality, and maintain security throughout the process. Set clear data governance policies and assess options like anonymization.
With an intentional strategy grounded in specific use cases, you can build a data asset that fuels next-level analytics, sharpens your competitive edge, and opens up new opportunities. The possibilities are endless when you tap into the true power of data.
I hope this overview of the top data collection use cases provides a launchpad to drive your organization‘s data maturity to the next level. Please reach out if you need any help bringing these use cases to life – I‘m always happy to chat through your data initiatives in more detail.
