Without data, even the most advanced AI model can only be as smart as a rock. For models to become adept at their intended use-cases, they require data for model accuracy, low costs, and effective value delivery.
Since the release of ChatGPT in 2022, the world has become obsessed with the new AI world that’s being built around us. While there is a lot to be excited about, it is important for organizations to be measured in their approach, and to be good stewards of their data to make sure they are fully taking advantage of it.
Everyone is working to improve AI model architectures right now, and no one knows where it’s going. What we do know, however, is that data is the lifeblood which drives the development of AI and underpins its future. At StandardData, we see data as being the limiting factor in the market penetration of AI over the long-term, and especially over the next several years.
This blog explores the benefits of well-architected data infrastructure and data governance, and why it is important for your organization to start now and realize the competitive advantages that come with preparing your data for AI, and only then, how AI modeling companies, such as KUNGFU.AI, can match the high quality data with high quality model architectures to deliver the most competitive AI technologies.
In this blog:
- The Data Scarcity Challenge
- Data as a Competitive Advantage
- First the Data, Then the Model
The Data Scarcity Challenge
Data is the foundation upon which AI models are built. Without clean, accurate, and relevant data, even the most sophisticated algorithms can falter. High-quality data ensures that AI models can learn effectively, make accurate predictions, and deliver reliable insights.
Despite the abundance of data we have today, according to the Wall Street Journal (WSJ), there is a 50% chance that demand for high quality data will exceed supply by Mid-2024, and a 90% chance it will happen by the year 2026. Why is this so? There is a subset of an organization’s data that is likely not digitized yet (on paper), and even though a large portion may already be in the cloud, it’s unlikely the data can be utilized. This is the difference between quality data that is leverageable, and data that is meaningless and waste of cost to store.
Data will be scarce for many years to come, but it doesn’t have to be that way for your organization. You likely have already generated tremendous amounts of data including image and video data, contracts, proposals, invoices, call recordings, and so on. This is what constitutes the “quality” data the WSJ mentions, as it’s data that isn’t available on the public internet and is unique to your organization. The good news is, even if AI is something later down the road for your organization, you can take full advantage of a modernized data infrastructure now, and there are many benefits to doing so beyond preparation for AI.
Data as a Competitive Advantage
With the understanding that a lack of quality data is going to be an impediment to AI model development as a whole over the next several years, organizations must take advantage of this by treating their data as the precious material it is, or more specifically: Data as their competitive advantage. Not only is this data most likely valuable to others in industry that are driving generative AI efforts, but also to an organization that is preparing for its future.
Rob Zelinka, CIO of Jack Henry & Associates
We’ve seen many cases where an organization is held back by its data infrastructure: it’s slow, expensive, and very disparate. Companies often struggle with consolidation of their data stores since they are actively acquiring such great volumes of data, often in the petabyte scale. Data, like any other asset, derives a large part of its immediate value from just how liquid it is. If it’s locked away in paper archives, for example, it can’t at all be leveraged in the cloud to derive AI insights, train AI models, or contribute to an organization’s operational intelligence.
Regardless of the end-services that wind up consuming the data, whether they be Large Language Models (LLMs) for training, Machine Learning (ML) models for inference, AI-as-a-Service APIs (e.g. ChatGPT), it is important that an organization focuses on utilizing the data it already has rather than investing in new developments. We recognize these two benefits as most significant:
- Leveraging Proprietary Data: The focus should be on utilizing proprietary data effectively with advanced AI models provided by the market unless the use-case specifically calls for it.
- Unlocking New Efficiencies and Insights: With high-quality data, companies can unlock new operational efficiencies and gain deeper insights, which are crucial for maintaining a competitive edge in the market.
We’ve seen a variety of different industries benefit from preparing their data for AI, even if they aren’t necessarily using AI yet.
For the Automotive Space, this means Responsiveness
We worked with an automotive telematics supplier that stored petabytes of sensor log data in Azure cloud storage and struggled to identify safety faults in their sea of disparate data which encompassed 250,000 CSV files of different schemas, with over 30 trillion rows. We modernized their infrastructure and allowed them to return results to their client in a more time-efficient and resource-frugal manner. In fact, it was taking them weeks to load data into MySQL and query for VINs that exhibited safety faults. We built a data pipeline around their data lake and sped up the processing dramatically, allowing IOT device data to be sent straight to cloud storage where our processing pipeline could parse the data and return fault-affected VINs in a matter of minutes.
We optimized their data lake, reducing the processing time from weeks to minutes, and shaved 50% off the cost of their data infrastructure, putting the company in an ideal spot for development of predictive maintenance artificial intelligence models and providing higher levels of responsiveness.
Read more about this case study.
For the Public Sector, this means Accessibility
Document extraction, specifically Optical Character Recognition (OCR), has tremendous implications for manufacturing. As an example of what OCR can achieve, for one of our clients, we took a database of historical archive images sitting in cloud storage and ran them through a document extraction engine to build a structured dataset with quality OCR to enhance the searchability of the database for end users.
To improve processing efficiency, we migrated their system to Amazon Web Services (AWS), utilizing a serverless architecture. This allowed us to distribute processing across hundreds, or even thousands of virtual machines, working in parallel.
The results were outstanding. The client experienced a 10X improvement in OCR quality, successfully converted previously unreadable pages into fully legible text. Their technology costs decreased 94%, reducing from $300 per batch to $20 per batch. In addition, implementation speed improved 99%, from three weeks to one hour.
The new accessibility of the troves of records are likely to be incredibly valuable for the historian AI models of the future.
For Financial Sector, this means Efficiency
The financial sector has long been built on systems. Banking systems, trading systems, software systems, etc. The efficiency of a financial organization largely boils down to how responsive and insightful its system is. Imagine a financial institution with petabytes of data that must continuously comply with ever-changing regulatory requirements, customer needs, and competitive weaknesses. A financial organization is largely dependent on its ability to manage its data well so that it doesn’t, for example, buy too many government-backed securities only to have the FED raise interest rates at unprecedented scale and cause total collapse (e.g., SVB).
The benefit of a modernized data infrastructure is a bank that can see itself for what it is, unbiased and as a whole. According to a Deloitte study, generative AI has the potential to significantly enhance the end-to-end risk management lifecycle—from risk assessment through fieldwork and reporting—resulting in broader assurance coverage and timelier insights and optimizing collective resources.
Key benefits include:
- Internal audit and compliance: Continuously monitoring large volumes of data to assess adherence to regulatory requirements, policies, and internal controls.
- Tax Sensitivity: Generative AI solutions could help extend the lives of enterprise systems that are not fully tax sensitized through anomaly detection, data enrichment, and analysis of historical patterns.
- Investor relations and reporting: Generative AI models trained on company-specific data could generate draft stakeholder materials, regulatory filings, and other communications, allowing more time to focus on strategy.
As we move further into the AI-driven future, the organizations that prioritize effective data management and governance today will be the leaders of tomorrow. Investing in data infrastructure is not just a technical necessity but a strategic imperative. Ensuring your data is well-organized, accessible, and high-quality will allow you to fully leverage AI technologies to drive your business forward.
First the Data, Then the Model
As you can see there are many scenarios in which preparing data is critical to unlocking and harnessing its potential. It’s only after data is prepared that companies can take the next step to furthering optimization through AI modeling. As mentioned, companies such as KUNGFU.AI, a trusted StandardData partner, can then utilize that data to leverage the full potential of AI across strategy, engineering, and operations. AI modeling companies design, architect, build, and deploy end-to-end AI solutions ranging from innovative proofs-of-concept to fully managed production AI models.
Looking Ahead
In summary, there are two principal components to the best AI models: Quality Data and Quality AI Model Architectures. We have been honing our data services to make sure that the AI industry is supplied with ample quality data to move forward as fast as it can.
Along the way, as outlined in the use-cases above, we expect to help our clients with more fundamental data issues surrounding storage cost optimization, processing, and machine learning preparation. After these building blocks are in place, organizations can move forward, when and if they are ready, with the quality model architecture piece, with companies like KUNGFU.AI, to link our high-quality datasets with high quality models.
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