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Ashok Manoharan, Founder/CTO FocusLabs . Businesses across a plethora of sectors—including healthcare, finances, e-commerce and even manufacturing—are rapidly embracing artificial intelligence (AI). AI has a knack for automating repetitious work, interpreting voluminous data and forecasting potential ramifications.
However, there's a catch. Before you rush into integrating AI into business practices, you must first ensure the data is accurate. AI systems are data-hungry.
They use what is referred to as "training data" because these datasets act as educational content for the AI model. If the data is inadequate, incomplete or incorrect, the developed AI will yield poor and/or erroneous outcomes. Having optimum performing AI models within your business all stems from having the correct and appropriate data—organized, clean and comprehensive.
Why Good Data Is So Important For AI 1. AI lacks the capability or willingness to set objectives. The tools encapsulated by AI processes are not natively innovative.
AI is a trained performance model that looks for guidance from the data to create objectives and maximize functionalities. For instance, before it can forecast customer actions or enhance a certain procedure, AI has to study historical data to obtain such frameworks. For AI to provide any valid results, it must draw on accurate and relevant data.
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A popular adage in the tech industry is "garbage in, garbage out." Bad results are the consequences of putting unnecessary or dirty data into an AI system. It doesn't matter how sophisticated your AI is, the bad data is going to let you down.
If your data is incomplete or has bias or errors, the AI output will mirror all of these issues. However, if you have clean data, AI can properly analyze that data and support the business strategy with proper insights—allowing you to trust the AI's advice to improve the business's operations, marketing and customer care. 3.
AI can't fix bad data—it makes it worse. Many people view AI as some magic wand that can instantly clean or fix data problems. However, AI can actually exacerbate the data issues if it is operating under flawed data.
For instance, if there are inaccuracies or biases within the data, AI will always depend on such flaws—thereby disrupting your operations. Having proper data organization can allow AI to accelerate the pace of business operations without fail, as well as increase overall productivity. Ensuring The Data Is Fit For AI Usage Properly investing in data quality early in the process can help support business strategy and finances.
Trying to remedy errant data at later stages can be costly. Correcting it at the beginning can prevent important mistakes from occurring in the first place. Here's how to get started.
• Tidy the data. Begin by identifying and correcting errors, eliminating duplication and verifying data accuracy. Normalize data that comes from various systems so the AI can work with it.
• Complex data integration. Bring data from different places (e.g.
, sales transactions, salaries both in U.S. dollars and other global currencies, or customer info) into one place to allow AI to have a more complete look at all needed features for better suggestions.
• Appropriate data management roles. Enforce data governance rules for clear ownership and responsibilities regarding security, privacy, compliance, etc. • Get rid of bias.
Examine data to identify gender biases, racial biases, etc., and fix them so the AI does not perpetuate or exaggerate unfair results. • Refresh data.
Keep your data freshly updated, as AI systems can generate relevant insights and output only when working with the latest information. Start With The Data AI can improve your organization and bring new ways of doing business. Regardless of your AI's sophistication level, however, it will not function properly in the absence of good data.
Before getting into AI, be sure to enter, clean, standardize and organize your data. This will enable you to possess a solid base for success and also ensure AI provides the useful insights and outcomes you need to achieve. If the data quality is addressed first, AI can be fully unleashed within the organization—signifying the long-term viability and profitability of the organization.
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