To effectively extract valuable insights from vast amounts of data, Artificial Intelligence (AI) helps process this data quickly and accurately. To save time and make it easier, I’ve prepared practical tips and hacks in this article.
Practical Tips for Using Ready-Made AI for Big Data Analysis
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Use the Right Queries for Specific Tasks
To get an accurate and useful response from AI, it’s important to formulate your queries in accordance with the task. For example, when forecasting sales or searching for trends, you need to clearly specify what exactly needs to be analyzed.
✅ Correct example: “Using sales data from the past year, predict how demand for the product will change next month.”
❌ Incorrect example: “What sales data is important?” — this query is too vague and won’t give a specific answer.
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Clean Your Data Before Queries
Before sending a query to AI, make sure that the data you’re working with is free from errors or gaps. AI cannot work with “dirty” data, so preprocessing is extremely important.
✅ Correct example: “Here are my sales data, without gaps or errors — can you analyze it and find patterns?”
❌ Incorrect example: “Here are my sales data, including rows with empty values — find trends based on this.”
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Consider AI Capabilities
Ready-made AI systems can analyze data quickly and efficiently, but they cannot handle overly complex or large datasets all at once. If the data is too much, split it into parts and send queries step by step.
✅ Correct example: “I have a sales table for the year — can you help identify seasonal fluctuations?”
❌ Incorrect example: “Analyze all my sales data from the past five years, including every transaction.”
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Update Data for Accurate Forecasts
AI will work better if you provide up-to-date data. For example, when analyzing customer purchases, it’s important to consider the latest trends and behavioral changes.
✅ Correct example: “Predict how demand for the product will change if we add data on purchases from the last month.”
❌ Incorrect example: “Predict product demand using data from last year.”
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Ask Clarifying Questions
If the AI’s answer is unclear or seems incomplete, don’t hesitate to ask additional questions to understand how it reached that conclusion.
✅ Correct example: “Why do you think the demand for the product will increase next month? What factors influenced this?”
❌ Incorrect example: “Okay, you predicted demand growth, but I don’t understand why.”
Advantages of Using AI for Big Data Analysis
➕ Speed of Data Processing
AI can process vast amounts of information in a short time, making it an ideal tool for analyzing big data.
➕ Forecast Accuracy
Using machine learning algorithms and statistics, AI can accurately predict trends, helping to make informed decisions.
➕ Accessibility and Ease of Use
Ready-made AI systems, such as ChatGPT, Google Gemini, and Jadve, easily integrate into workflows and require minimal technical knowledge to use. This makes them accessible even to users without deep expertise in data analysis.
➕ Automation of Analysis
AI allows you to automate routine data analysis tasks, such as pattern recognition, anomaly detection, and report generation, saving time and resources.
➕ Powerful Data Processing
Ready-made AI systems can handle large amounts of data in real-time, which is useful for analyzing dynamic and rapidly changing datasets (e.g., social media monitoring or transaction tracking).
Disadvantages of Using AI for Big Data Analysis
➖ Dependence on Data Quality
If the original data contains errors, gaps, or noise, AI may produce inaccurate or even incorrect results. Therefore, data preprocessing is crucial.
➖ Limited Interpretability
Some AI models, especially neural networks, can be complex to understand. Sometimes it’s difficult to comprehend why AI reached a particular conclusion, which complicates using these conclusions in business.
➖ Need for Regular Data Updates
AI systems perform better with up-to-date data, so it’s important to regularly update the information. Without updates, forecasts and analyses may become inaccurate.
➖ Power and Resources
While ready-made AI systems are easy to use, processing very large datasets may require significant computational resources, which can increase costs.
➖ Risk of Incorrect Conclusions
AI can make conclusions based on patterns found in the data that may not always be logically correct. For example, the system may suggest a solution that works well with historical data but fails to account for future market changes.
Top 5 Tools for Integrating AI into Big Data Analysis
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A versatile tool for processing text data, creating reports, and analyzing textual information. It is excellent for generating summaries and automatically responding to queries.
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A powerful tool from Google for data processing, forecasting, and trend analysis. It is particularly effective in processing large volumes of data and in real-time.
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Specializes in analyzing large datasets using machine learning. Ideal for tasks related to forecasting and identifying patterns in data.
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An AI platform from IBM that helps analyze data and make decisions based on deep knowledge and machine learning algorithms. Suitable for more complex and large-scale analyses.
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A platform from Microsoft for creating and deploying machine learning models and AI. It is suitable for processing large datasets and integrating into cloud solutions.
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