Large Language Models (LLM) have become the new driving force of modern enterprises according to the Forbes.com article Demystifying Data Preparation for LLM – A Strategic Guide for Leaders, LLM like GPT-4 can increase annual global corporate profits by up to $4.4 trillion. Likewise, Goldman Sachs also predicts that generative technology can add almost $7 trillion to the global economy and lift productivity growth by 1.5 percentage points in the next decade.
Like all things AI, language models also need clean, high-quality data to do their best.
AI: The Next Big Thing
CIOs, CTOs, and COOs from Fortune 500 companies are the visionary leaders shaping the future of business. With the pandemic now a distant memory, their focus has shifted to the next big thing: artificial intelligence (AI), the “shiny new toy” that’s set to revolutionize industries and redefine success.
Three main messages are conveyed:
- AI is here. Leverage it for growth
- You can’t leverage AI without clean data
- Garbage-in, garbage-out
Your Data is a key business growth enabler: clean it up now before you go down the road to AI.
Automated Intelligence is the Key to Unlocking Your Data
Strive for data cleanliness
You don’t have to take a leap of faith. True believers already know all about that old saying “garbage-in, garbage-out.” Your business data may not be garbage, but it could be the litter that is hampering your business growth. Stale data could be clinging to your system and cluttering your ability to plan, innovate, and be ready for all the good things artificial intelligence can do for you.
What are those “good things”?
AI, when powered by clean data, can significantly transform your business operations. It enforces self-sustaining data cleansing and governance rules, ensuring the integrity of your data. This, in turn, promotes better decision-making across every aspect of your organization. By driving business process efficiencies, AI can automate repetitive tasks, optimize supply chain management, enhance customer service, improve decision-making, and boost product development. Additionally, AI serves as a critical resource in mitigating risks, avoiding fraud, and ensuring compliance. Furthermore, AI-powered predictive data analytics can enable strategic growth by forecasting customer demand, analyzing customer behavior, and tracking market trends.
Data Cleansing Must Come First for Good Things to Happen
As AI-based solutions emerge and accelerate in industries such as healthcare and finance, they increasingly rely on sanitized data to make accurate predictions and decisions. For instance, in medical diagnostics, AI algorithms analyze vast datasets of patient records, imaging scans, and lab results. The accuracy of these AI-driven diagnoses hinges on the correctness and cleanliness of the data. Any errors or biases in the data could lead to incorrect conclusions, impacting patient outcomes. Similarly, in finance, AI models that predict market trends or assess credit risks depend on large volumes of historical financial data. If this data contains inaccuracies, it could result in flawed financial advice or erroneous risk assessments, potentially leading to significant economic losses.
Data strategy for cleansing is the crucial step that:
Data cleansing is a critical process that ensures the integrity and accuracy of your data by scrubbing out irrelevant information, locating and isolating outliers and duplicates, and filling in missing data attributes. By systematically addressing these issues, data cleansing enhances the quality of your data, providing a solid foundation for reliable analysis and decision-making in your organization.
Even better, data cleansing can sometimes remove biases from raw data, making it usable. Data biases result from incomplete or inaccurate data that fails to include a cross-section of the overall data universe. One case in point was the discovery that Amazon’s automated hiring tool discriminated against women. The data Amazon used was from an overwhelmingly male population.
How to Do Your Best to Get Ready to Rise with the AI Tide?
To get your data ready to rise with AI, you should follow a systematic process that ensures your data is clean, well-organized, and fit for AI applications.
Here are the main steps to getting your data ready for AI:
- Define your Data Needs – Clearly define what you want to achieve with AI. Whether it’s improving customer insights, automating processes, or predicting trends, your data strategy should align with these goals. Understand the types of data required.
- Data Collection and Integration – Collect data from various internal and external sources that are relevant to your AI objectives. Combine data from different sources into a unified dataset, ensuring that it is comprehensive and relevant for your AI use case.
- Data Cleansing – Remove duplicates, correct errors, handle missing values, and eliminate any irrelevant or redundant information. Standardize formats, normalize data, and perform feature engineering to transform raw data into a format suitable for AI models.
- Data Validation and Testing – Check the data for consistency, completeness, and accuracy. Split the data into training, validation, and test sets to ensure that it is ready for model training and evaluation, addressing any biases or anomalies.
The goal is to ensure your data is well-prepared to support robust, accurate, and effective AI-driven insights and solutions.
It is important to remember that the best data cleaning helps with accuracy, but it does not ensure compliance with data privacy regulations such as HIPAA, ITAR, PCI, etc. An example of why compliance with data privacy regulations is crucial in the AI industry. In 2022, the tech startup DataCraft cut corners by collecting vast amounts of personal data without proper consent, relying on questionable third-party sources, and failing to anonymize the data used in its AI models. DataCraft was found in violation of GDPR and CCPA, resulting in massive fines and a loss of clients. The scandal led to a sharp decline in the company’s valuation.
How Liberty Advisor Group can Help?
In summary, the real value in data is in gaining insights that ultimately gain a competitive edge or sell more products. Looking for more ideas and insights? We’ll show you how to align your data priorities with business goals and leverage your clean data into actionable results. Contact us. Let us be your partner in the future growth of your business.
About Liberty Advisor Group
Liberty Advisor Group is a goal-oriented, client-focused, and results-driven consulting firm. We are a lean, handpicked team of strategists, technologists, and entrepreneurs – battle-tested experts with a steadfast, start-up attitude. We collaborate, integrate, and ideate in real-time with our clients to deliver situation-specific solutions that work. Liberty Advisor Group has the experience to realize our clients’ highest ambitions. Learn more on LinkedIn and Twitter.
