How Your Enterprise Can Overcome AI Data Challenges
Uncover how your data creates roadblocks to effectively implementing AI. Find common solutions to AI data challenges to get your AI models to perform at their best.
You need to apply AI to your business — is your data helping or hindering?
Data-related issues are one of the top AI implementation challenges: without data integrity, no AI model can perform well or be used at scale.
This article covers the main data challenges facing businesses and how to get around them, ensuring that your AI implementation is a smashing success.
What AI Data Challenges Can Compromise Development?
As your data volume increases, so do potential data-related obstacles. Inaccurate implementations can undermine the model, its performance, and your ROI in AI. These possible inconsistencies with data depend on what data you collect, how you store it, and whether you ‘clean’ data.
Structural Errors Influence Model Accuracy
If your data contains structural errors such as typos, incorrect spelling, and inconsistent formatting, the AI data integrity of your algorithm can be jeopardized.
Say your dataset has a column for “age,” but some entries are recorded as days or years in that column. This error can mislead AI models when making predictions or analyzing trends related to age.
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Have someone run through the data to spot inconsistencies or errors and remove data samples that can tamper with the accuracy of your AI algorithm. You can also use simple or more advanced algorithms and write scripts to automate the process.
Duplicate Data Clutters Training Datasets
The same data collected multiple times can also put your model accuracy at risk. For example, you have identical information collected from one or multiple channels. Or you may be using two tools that record the same data, leading to unwanted clutter. Consider the data format, use, and quality level to surmount AI data challenges and keep the dataset ‘clean’.
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You can perform an exact match and de-duplicate using algorithmic methods. When the case is not so clean-cut, you can use a fuzzy match or even train special AI models to resolve the duplicates.
Data Silos Can’t Be Used Separately
If you store data in multiple locations, you can end up with data silos. Data silos often mirror your company structure: there are as many data silos as there are departments in your organization. Each department uses data differently. Marketing relies on a content management system (CMS). Analytics resides in a data warehouse. Client communication happens through a mailing system or client relationship management (CMS) system.
When you’re gathering data to train your model, finding the ‘true’ data among those silos can challenge your team. The more scattered your data is, the harder it is to join the dots and feed the right dataset to your AI application.
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Create a data platform on top of all your data silos to source the necessary data and feed it to the AI model you’re training. This helps to ensure efficient data management.
Outdated Data Downgrades Relevance
Data has an expiration date. Using outdated data sets can affect your AI data quality and make it irrelevant.
Think of a businessman who searched for specific information about biotech innovations. This individual found relevant answers months ago and is probably searching for something else. Your data, however, still shows his search results from months ago. Whatever model you’re trying to make based on the months-old data, makes your AI algorithm irrelevant to this user.
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Have regular data cleaning and update sessions to ensure your data is intact and current. Use data versioning to automatically track changes in your data through a software program running on top of your data.
Ungoverned Data Makes You Lose Money and Reputation
Knowing the nature of your data and how to keep it safe can spare you regulatory violations, data breaches, and data leaks.
Data Types Falling Under Protection
PII – Personally Identifiable Information
PHI – Protected Health Information
PCI – Payment Card Information
NPI – Nonpublic Information
Sensitive Data Examples By Category
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Housing and acting off of non-compliant data can get a company into hot water. Take control of what data you’re feeding the model rather than just telling the model to consume your entire storage array. If this sensitive customer data is fed into an AI model, it may result in severe privacy breaches, lawsuits, or data leaks.
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Data governance helps you make sense of your data and regulate its ethical and legal use. It also helps you process, store, and transmit data securely.
Poor Data Accessibility Jeopardizes AI Projects
You may be housing data with differing security clearance levels. Due to regulations and legal procedures, using this data to train an AI model may become nearly impossible.
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If the existing data is insufficient, you can increase it with AI data augmentation.
More on AI Data Augmentation
AI data augmentation is used in machine learning and deep learning to artificially increase the size of a dataset. The augmentation happens when you apply various transformations to the existing data samples. Some common AI data augmentation techniques include:
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Image Data Augmentation
For tasks involving image data, augmentation techniques may include random rotations, flips, translations, scaling, cropping, changes in brightness and contrast, and adding noise.
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Text Data Augmentation
For text data, augmentation techniques may involve synonym replacement, word deletion, word reordering, adding or replacing words with similar ones, and paraphrasing sentences.
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Audio Data Augmentation
For audio data, augmentation techniques may include adding background noise, changing the pitch or speed, time shifting, and applying various types of filters.
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Other Data Types
Data augmentation techniques can also be applied to other types of data, such as time series data or tabular data, depending on the specific task.
As you apply AI data augmentation techniques, balance the amount and type of augmentation to prevent overfitting or introducing unrealistic variations, which can degrade the overall AI model performance.
Lack of AI Infrastructure Undermines Systems and Strategies
Your infrastructure already handles existing software implementations, whereas AI implementation will require a new infrastructure for training, deployment, monitoring, and scaling capabilities. Correct ML operational practices help you overcome major AI data challenges.
Key infrastructure components include:
- Flexibility and scalability of your systems
- Computing resources
- Network performance
- Integration of existing systems with the AI core
- End-to-end data protection roadmap
- Datasets and systems assessments
- Optimization prospects once the AI project scales
Building an AI model can go smoothly once you correctly tune these components.
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Data experts can meticulously analyze your infrastructure. Experts use an MLOps approach — real-world practices for building machine learning systems — to build the entire infrastructure that automates the creation of AI models.
How Does AI Data Integrity Drive Development?
Data has to meet the right standard to pass an AI readiness assessment. It’s very uncommon for an organization to have perfect datasets ready to go, and most that we’ve worked with need quite a bit of help. We usually start with the questions below to perform a quick AI data integrity check.
AI Data Integrity Checklist
These AI data integrity questions help you assess your position. If you’re uncertain or your answers lack clarity and transparency, data experts can help you analyze and prepare concrete steps to address any AI data challenges.
Additionally, the model you’re going to train dictates the data required to build an AI solution. Say you’re building a large language model (LLM). It could take up to 15 trillion data tokens to make something similar to Llama 3. Conversely, the data quantity will decrease if you’re developing a machine learning algorithm to fulfill a specific operation. A single-purpose algorithm may only require a few thousand data samples.
The variables can also change whenever the problem you want to solve — the idea that stands behind your solutions — dictates the data necessary for training.
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To create an algorithm behind your AI solution, ensure you distribute the data accordingly. 80% of your data can be used for training. The other 20% can test the AI model’s accuracy. The 20% you set aside ensures unbiased model verification. Plus, you validate your model with a unique dataset relevant to your company’s experience.
What Do Data Storage Spaces Accomplish in AI Projects?
Data needs a home before you feed it to an AI model. As a large business, you store vast amounts of data in a designated repository filled with unstructured and/or already processed data. That storage space determines AI data quality and its initial state. Before any development begins, analyze what you have and whether you need a supporting mechanism to source data.
Data Storage Type Can Propel an AI Initiative
Data lakes serve as repositories for diverse raw, unfiltered data collected from line-of-business applications, mobile apps, social media, IoT devices, and the like. The data in a data lake remains in its original format until a specific analysis is needed. Data lakes can support multiple, if not all, strategic and immediate decisions executives make to grow their ROI and reach. In an enterprise context, data lakes consist of centralized repositories designed to store, process, and secure large amounts of structured, semi-structured, and unstructured data.
Data warehouses contain curated, contextualized, and transformed data to assist in analysis and derive actionable insights. Since the data is labeled and structured well, you can maneuver categories and spot patterns through graphs, charts, or any other visual aid generated by the data warehouse tools.
A data mart is created for particular departments or business units, like marketing, sales, finance, or HR.
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AI data pipelines can assist you in moving data from disparate sources to a data warehouse or another target repository. They combine sequences of actions, tools, and processes or a series of data processing steps.
AI Data Challenges Are Surmountable
When you have data — it’s great. And if you have vast amounts of data — it’s even better.
Nonetheless, the above AI data challenges may seem daunting.
To ensure your AI data integrity, consider starting with an AI PoC to bulletproof a concept for an AI model. An AI PoC takes just a slice of your data, preps it, and shows how plausible an AI model is in your business context, laying the groundwork for a fail-proof AI implementation strategy.
If the PoC results are promising, you can build the actual AI model and impact your business in more ways than you initially intended. You can establish connections between your departments, make data work for your bottom line, and justify further investments.
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