The potential of AI to drive business value relies on access to quality data. However, effectively collecting, preparing, and managing training data remains a significant obstacle for many organizations looking to operationalize AI.
Recent surveys reveal declining AI adoption over the past year, with data collection challenges as a primary reason. According to Statista, the global adoption rate of AI technology decreased from 25% in 2020 to 20% in 20241.
This article will dive into the top 6 challenges in sourcing, processing, and maintaining AI training data with potential solutions to smooth your organization‘s path to AI success.
1. Limited Dataset Availability
The bedrock of any AI system is its training data. But collecting or accessing datasets broad and relevant enough to handle real-world complexity is hugely challenging.
One key problem is dataset narrowness – when the scope of the data fails to fully cover the intended use cases. Studies show a majority of datasets used in AI research are recycled from prior projects or limited public repositories2. This over-reliance on existing data often misaligns with new applications.
For example, an image classifier trained only on photos of animals may fail to identify other objects like food items or clothing. Speech recognition models perform poorly when trained solely on formal conversational data rather than real-world noisy speech.
Without access to broad training data spanning all facets of the problem space, AI systems struggle to deliver robust performance in complex real-world environments.
Solutions
Strategies to mitigate limited data availability include:
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Assign responsible data teams: Dedicate cross-functional data teams focusing solely on strategic dataset acquisition for each project. With deeper knowledge of the models‘ objectives and technical nuances, they can better judge fitness of datasets.
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Expand collection methods: Look beyond recycling existing datasets. Leverage other techniques like web scraping, sensors, surveys, crowdsourcing etc. to generate customized data tailored to the problem.
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Generate new data via crowdsourcing: Crowdsourcing platforms like Amazon Mechanical Turk allow creation of new annotated datasets specific to your needs.
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Combine datasets: Merge multiple niche datasets to incrementally expand coverage of concepts and scenarios.
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Simulate data: Generate synthetic simulated data to fill gaps and cover fringe cases lacking real-world examples.
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Consult data partners: Experienced data vendors can provide strategic guidance on specialized datasets to solve specific AI challenges.
Expert Tip
Based on my experience building customized e-commerce product datasets, I‘ve found that starting early is crucial. Data collection planning should begin at the start of the ML lifecycle – not as an afterthought once models are ready to be trained. Cross-functional collaboration between data teams, ML engineers and product managers ensures alignment between data collection and product goals.
2. Data Bias
Biased or unrepresentative data poisons AI systems and leads to discriminatory and unethical results when operationalized. Due to historical biases in society, real-world data often exhibits prejudices against marginalized communities. Subtle sampling issues during collection can further compound these problems.
For example, facial recognition systems trained on datasets biased towards lighter skin tones have much higher error rates in identifying darker skinned faces3. Similarly, resume screening algorithms trained only on profiles of majority demographics often exhibit gender, race and age bias.
Omitting diversity in training data denies AI models exposure to the full spectrum of populations they will serve in the real world. Without proactive mitigation, historical and sampling biases propagate into AI systems leading to unfair and harmful outcomes.
Solutions
Strategies to reduce data bias include:
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Analyze dataset composition: Audit datasets to ensure adequate demographic diversity across gender, age, ethnicity, income levels etc. Proactively review for skews rather than assuming unbiased data.
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Diversify data collection: Involve team members from minority backgrounds in data collection and annotation to reduce individual biases.
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Leverage crowdsourcing: Crowdsourcing provides broader access to global demographic diversity compared to in-house collection.
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Debias via data augmentation: Use techniques like GANs to generate additional synthetic training examples that counteract dataset imbalances.
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Implement bias testing: Both before and after model training, test AI systems for biases using simulated biased data.
Expert Tip
Based on my experience, blind spots often lead to bias issues going undetected until after models are deployed. Consider diversity not just in data collection but also in review. In one project, we had native Arabic speakers vet our ML system for cultural biases that non-natives missed.
3. Data Quality Issues
Real-world data is often messy, requiring substantial processing before usable for model training. But cleaning and preparing massive datasets demands extensive human effort and technical tooling.
Raw data inevitably contains quality issues like missing values, outliers, errors, duplication, irrelevant features etc. According to Gartner, poor data quality and tedious data prep are top hurdles to operationalizing AI, with data teams spending upwards of 80% of time just on cleanup4.
| Data Quality Issue | Example |
|---|---|
| Missing values | Empty cells, null entries |
| Outliers | Abnormal or extreme values |
| Errors | Typos, formatting problems |
| Duplicates | Repeated entries |
| Irrelevant features | Columns unrelated to target variable |
This massive overhead slows the pace of development and delays training of production-ready performant models.
Solutions
Some ways organizations can address data quality challenges:
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Leverage data preprocessing tools: Equip data teams with software like Pandas, NumPy, SQL etc. to automate aspects of cleaning and preparation.
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Outsource data processing: Offload the heavy lifting to external data service partners rather than solely relying on internal resources.
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Implement QA practices: Actively manually inspect samples from datasets before and after processing to catch lingering issues.
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Monitor model performance: Decaying model accuracy indicates deteriorating data quality due to drift etc.
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Retrain models often: Use fresh clean data in regular intervals to retrain models and prevent accuracy degradation over time.
Expert Tip
In my experience, don‘t underestimate the effort involved in quality data preparation – it can easily take 80%+ of a project timeline without proper planning! Get clear on responsibilities between data teams, ML engineers and providers. Also budget for data processing tools and services to optimize efficiency.
4. Data Protection and Legal Risks
With growing public awareness of technology risks, governments are enacting stricter regulations on data privacy and usage. Organizations now face more legal and ethical risks than ever in collecting data for AI systems.
While anonymizing personal information can help, prominent examples like the Facebook-Cambridge Analytica scandal highlight how even innocuous data can compromise privacy at scale5. There are also stringent laws like GDPR and CCPA restricting how personal data of EU and California residents can be used.
A 2022 Linux Foundation survey of technology leaders identified data privacy as the biggest ethical challenge in AI adoption6. Any perception of dubious data practices can severely damage brand reputation. Organizations must address compliance, security and transparency challenges in data sourcing.
Solutions
Some ways organizations can mitigate data privacy risks:
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Anonymize data: Scrub personally identifiable information like names, account numbers, IDs etc. from training data. Use techniques like differential privacy.
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Get informed consent: Communicate clearly how data will be used and collect only from willing participants with opt-in policies.
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Assess regulations: Understand your jurisdiction‘s laws regarding data residency, transfer, retention periods etc. and ensure AI data practices comply.
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Secure storage: Prevent data breaches by restricting access to encrypted repositories. Use trusted cloud providers.
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Reduce risks via federated learning: Train models directly on decentralized data sources without central aggregation.
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Conduct impact assessments: Audit datasets for hidden insights that could compromise privacy or lead to other issues when revealed through the AI system.
Expert Tip
I helped a health-tech client navigate HIPAA regulations to securely collect medical imaging datasets from partner hospitals for an AI diagnostic assistance system. We maintained patient consent documentation, anonymized identifiers, implemented access controls, and followed strict transfer protocols.
5. High Data Collection Costs
For enterprise-scale AI projects, assembling massive datasets gets extremely expensive. Just the overhead of hiring, training and managing large internal data teams can become cost-prohibitive.
Building sophisticated natural language or computer vision capabilities requires diverse data spanning millions of examples. For instance, training conversational chatbots demands text and voice data encompassing countless potential user interactions and scenarios7.
Many companies find it untenable to exclusively build such immense datasets completely in-house. With limited budgets, the costs of large-scale data sourcing for industry-grade AI can torpedo projects before they begin.
Solutions
Some tips for controlling data collection costs:
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Estimate costs upfront: Factor in data needs during planning phases and get buy-in on budgets before kicking off AI development.
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Prioritize essential data: Don‘t over-engineer datasets. Start with readily available data and collect custom data only for critical gaps.
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Outsource data tasks: Third-party data partners offer cost-efficiencies compared to large in-house collection and processing teams.
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Leverage internal data: Tap into existing enterprise data like customer support logs before looking externally.
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Explore free public datasets: Reuse open-source datasets where possible to avoid costs of fresh collection.
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Start small, then scale up: Iterate models initially on smaller datasets, then scale up data as project matures.
Expert Tip
I helped an e-commerce client boot strap their visual product search engine starting with just 10,000 initial product images scraped from their site. We steadily expanded the dataset to over 500,000 images by tapping into marketplace seller feeds once the MVP showed promise – keeping data costs aligned to value.
6. Data Drift
In the real world, data is not static. It gradually shifts over time – a phenomenon known as concept drift. Customer preferences, language, social trends – everything evolves.
If not detected and mitigated, data drift quickly causes AI model performance to degrade. Systems trained on stale historical data lose touch with reality.
For example, consider an ML model predicting retail customer conversion based on past purchase behavior. As new products emerge or consumer habits change, historical patterns become outdated, causing conversion rate predictions to suffer.
Continuously monitoring datasets and retraining models to counteract drift imposes overhead on data teams. But neglecting its impact causes ever-compounding technical debt.
Solutions
Some strategies to combat data drift:
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Monitor for drift: Use statistical tests like KL divergence to detect significant shifts in data distribution over time, signaling drift.
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Retrain regularly: Set up pipelines to feed models with latest data in periodic intervals to maintain performance.
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Use online learning: Models continuously adapt parameters in real-time rather than retraining from scratch. Requires less data.
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Work with data partners: On-demand fresh datasets tailored to evolving model needs through crowdsourcing, web scraping etc.
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Employ techniques like active learning: Models identify gaps in their knowledge and request most informative samples for retraining.
Expert Tip
For a financial client, we detected data drift by tracking sliding accuracy and loss metrics – sudden changes indicated their fraud prediction models needed retraining. We then used active learning to selectively source additional informative data from crowdworkers that improved performance with minimal overhead.
Key Takeaways
To quickly recap, the 6 main data challenges organizations face in AI adoption are:
- Limited availability of broad, relevant datasets
- Biases entering due to unrepresentative data
- Extensive effort required for data cleaning and preparation
- Privacy, security and legal risks
- High costs of large-scale data sourcing
- Data drift causing model decay over time
By understanding these pitfalls and applying the right solutions, organizations can streamline data practices for AI success. Investing in the right data collection infrastructure and workflows will pay dividends through highly accurate models that drive business impact.
For more best practices on AI data collection, see our detailed guide:
[Link to downloadable data collection guide]References
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Statista. (2022). Artificial Intelligence – Statistics & Facts. https://www.statista.com/topics/3127/artificial-intelligence-ai/
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Koch, B. et al. (2021). Reduced, reused and recycled: The life of a dataset in machine learning research. https://arxiv.org/abs/2112.01716
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Raji, I.D. and Buolamwini, J. (2019) Actionable Auditing: Investigating the Impact of Publicly Naming Biased Performance Results of Commercial AI Products. AIES 2019 – Proceedings of the 2019 AAAI/ACM Conference on AI, Ethics, and Society.
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Goasduff, L. (2019). 3 Barriers to AI Adoption. Gartner Research. https://www.gartner.com/smarterwithgartner/3-barriers-to-ai-adoption
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Lapowsky, I. (2018). How Cambridge Analytica Sparked the Great Privacy Awakening. Wired. https://www.wired.com/story/cambridge-analytica-facebook-privacy-awakening/
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The Linux Foundation. (2022). Artificial Intelligence and Data in Open Source Report. https://www.linuxfoundation.org/tools/ai-data-open-source-report/
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Metz, C. (2020). To Build Robust AI, OpenAI Trains Bot to Bot. Wired. https://www.wired.com/story/openai-trains-bot-bot-dactyl/