3 challenges in data collection with AI and proxies, and ways to overcome them
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Think of the following fact. In 2023, a staggering 3.5 quintillion bytes of data for collection were generated daily. In 2015, it looked different. Back then, the level was 2.5 quintillion. How much data, in quintillions, will be there for obtaining, scrutinizing, leveraging in 2030?
What's for sure is that individuals and entities will be gathering and assessing those ever-expanding datasets. As a site focused on proxies for data collection, with offerings to buy residential and mobile proxies, Dexodata cannot help keeping an eye on this race.
Who will win, growing volumes of data for collection or the expanding power of AI? Let’s explore what we know.
Data collection difficulty paradox
Intelligent computing capacities are skyrocketing. Recent research shows:
- From 2010 onwards, volumes of computational resources dedicated to ML models have increased by a staggering factor of 10 billion;
- Per Time, AI is already as effective as humans or exceeds humans in handling handwriting, spoken language, images, texts, even common sense-based contexts.
A logical question arises: if AI turns out to be so advanced in data interpretation, what could be wrong with data collection based on AI models? Data collection must be simpler than data assessment. Paradoxically, there exist three prominent obstacles, making applicable dilemmas more nuanced.
AI data collection challenge # 1. Bias
Data is generated by people, reflecting human nature. As social units, we carry biases. Consequently, data might contain biases, too. Gathering such data, with inherent bias, brings about skewed erroneous conclusions. No matter how smartly AI models function, in case they accumulate and interpret wrong details, they compile defective datasets and give misleading ideas. That is a common data collection challenge. As recent research shows, organizations report costly repercussions stemming from AI bias. A significant 36% stated their organization had experienced adverse effects due to incidents of AI bias in one or more algorithms.
As of 2024, it is not feasible to resolve problems without people. Your resulting datasets must be manually analyzed by context-aware diverse reviewers to identify bias after collection.
AI data collection challenge # 2. Quality
Per Gartner, data quality belongs to TOP-3 obstacles to AI activation and usage. Maintaining proper quality during data collection flows can be a formidable task, primarily because available information is often unstructured, necessitating extensive processing. Here, AI possesses greater potential, as it can be taught to execute certain manipulations, e.g. data cleaning, reduction, transcription, etc.
Make sure your AI tools entrusted with data collection are supported and tuned by highly-qualified people familiar with:
- Pinning down missing data;
- Performing data integrity checks;
- Recognizing data irrelevance instances;
- Understanding data redundancy issues.
AI data collection challenge # 3. Drifting data
Big data constantly flows from connected IoT devices, encompassing sensors, smart home appliances, portable gadgets. News sites, social media feeds, as well as user-generated content, aggravate overloads. Such speed and volumes may cause flawed analysis, limitless latency, and data remaining unused due to lacking AI capacity.
Three tier-two impediments are likely:
- Temporal developments
Real-world data is subject to ongoing changes occurring due to alternating environments, behavioral patterns, or tech breakthroughs. What is meaningful or accurate today may not hold true in the future.
- Sustainment of currently running models
As data undergoes temporal shifts, AI trained on obsolete data can lose accuracy or become thoroughly outdated. This property underscores the importance of continuous tracking of model performance and its systematic retraining.
- Absence of historical data
In certain situations, especially with ascending phenomena or swift contextual transformations at stake, there may be shortages of historical records that validly represent ongoing or upcoming conditions.
Tackling these data collection difficulties might take two directions:
- Manual ongoing updates that imply retraining models with fresh data and maintaining their relevance. Robotized pipelines can also be implemented for refreshing AI models at intervals.
- Flexible self-advancing algorithms that employ active learning methodologies, empowering models to adapt in real-time as data gets acquired. These strategies enable gradual adjustments to model parameters, enhancing their abilities concerning coping with shifting data distributions.
Dexodata’s platform with geo targeted proxies, both residential and mobile network ones, emphasizes: AI is no panacea. As you can see, human involvement can only solve existing shortcomings. Yes, AI could facilitate data collection, assisting staff members. Artificial intelligence is yet unable to replace them completely.
We are ever-ready to serve your team of humans, as a platform where data collection professionals are free to buy residential and mobile proxies. This will support their AI activities all across the globe, as proxies from 100+ countries are accessible, including America, Canada, major EU countries, Turkey, Russia, Kazakhstan, Ukraine, Chile.
Paid proxy free trial is available for newcomers with data collection plans.
