In today’s digital realm where technology can match human intelligence, the one asset that can make or break any business is data. When used meticulously, it can provide the needed competitive edge for surviving in hypercompetitive markets. But when overshadowed, it can push a business to the backseat, giving a severe death blow. This is why analysts ensure to make the best out of this asset when and as needed through different forms of AI models and algorithms. Integrating AI in any project requires a thorough cycle of data preparation. When not done cautiously, it can lead to the generation of inaccurate and meaningless reports and information.
Although AI data preparation might sound easier, the entire cycle is segregated into seven steps. On top of this, many are still in the dark regarding the importance of this task prior to the use and implementation of the evaluation algorithm. This is why most often the results generated through AI models are inaccurate and lead to a dead end. That being said, we have compiled a detailed guide on data preparation techniques and steps involved for artificial intelligence projects, along with explaining its benefits for the analysis.
Significance of data preparation for AI models
Ensures optimal data quality
The sourced data might not have the optimal quality needed for the AI algorithm to perform efficiently. Most often, the datasets contain meaningless information, corrupted records, null values, and even multiple versions. Performing analysis using them will result in inaccurate results, thereby washing down the efforts of the analysts down the drain. That’s why preparing the datasets appropriately is crucial as it ensures optimal quality and elimination of redundancies.
Helps in accurate model training
The major benefit of data preparation in machine learning is increased accuracy in training. With proper datasets and eliminated discrepancies, you won’t have to worry about the algorithms functioning anomalously or failing to provide the expected results. For example, when you want to analyze customer actions based on historical data, preparing and filtering the input information can help you train the AI and ML models for appropriate results.
Custom feature selection
With machine learning data preparation, professionals will have the leverage to select specific data features for further analysis. For example, while analyzing customer data, one can specify the characteristics to be evaluated through the ML model using custom feature selection techniques. This will not only reduce the time consumed for data analysis but also yield precise and accurate results.
Lowered costs and time-saving
Data preparation can also help in reducing the capital and operational expenses associated with large-scale AI projects by eliminating the need for repeated analysis. Furthermore, you will need less human support for conducting the process, ensuring the budget doesn’t go overboard than the forecasted numbers.
7 steps of data preparation for AI projects
Despite acknowledging the importance of appropriate data preparation for artificial intelligence models, incorrect implementation can render all the efforts useless. Owing to this, we have defined the seven major steps involved in preparing accurate, concise, and efficient datasets for further evaluation through AI and ML algorithms.
Data Collection
At the very beginning of the cycle is the data collection step. As the name implies, it involves defining the sources from where data will be scouted through scraping techniques. Usually, multiple sources are needed to ensure the data volume is as large as possible for accurate pattern and trend analysis. For example, when you want to improve customer experience for your retail business, the best sources for data scraping and collection will be POS systems, online reviews and testimonials, customer feedback, and surveys.
Data preprocessing and profiling
Once the data is collected from the determined sources, it’s time to implement strategies for pre-processing and profiling. It will involve running a thorough analysis of the collected information and detecting anomalies in the records. For example, you should implement strategies for finding duplicate records, missing values, and other forms of discrepancies in the datasets. By doing so, you can significantly reduce the chances of AI model failures and generate optimal results.
Data Cleansing
After the issues are identified and listed, you should work on cleaning the datasets and remove the anomalies without fail. For example, if there are duplicated records for a single identifier, you should correct the versioning and remove all the redundant ones. Similarly, cleansing should involve granular elimination of missing and null values to avoid any exception or model failure.
Data Classification
Classification of datasets is crucial for implementing the right security and governance measures. Only then will you be able to remain compliant and avoid any legal issues later on. Usually, datasets are categorized based on their relevance and sensitivity. For example, public data can be accessible by anyone and from any place while internal data requires more security and can be accessed based on specific allowable roles.
Data transformation with feature engineering
The datasets that you have in hand post-categorization might not be compatible with your pre-defined AI and ML algorithms. So, transforming them to the most feasible format is necessary before the actual evaluation. When you prepare data for machine learning, feature engineering will be a crucial step. It will allow you to select specific features of the input datasets that can be evaluated by the AI algorithms with ease.
Data validation
Once cleansing, categorization, and transformation are completed, you should run a second inspection of the datasets at a much more granular level. It will help you identify any hidden issue that wasn’t addressed during the first round and immediately resolve the same. Furthermore, it will also help in making the datasets more consistent from top to bottom.
Data Correlation
Lastly, when multiple datasets are involved, identifying the overlapped areas of common keys is crucial. It will help you to find the correlation between the records and gain a more detailed structure. Furthermore, it will also make it easier for the AI model to evaluate the correlated datasets and generate precise results.
Hurdles of AI model data preparation to be addressed
Understanding the major hurdles with data preparation for machine learning and artificial intelligence models will help in prompt and efficient resolution. Below we have listed the five major anomalies that can render the collected datasets useless for your project.
- Data inconsistencies: When different datasets are merged, there can be significant differences in formats, characteristics, and other forms of inconsistencies. If not addressed at the beginning, the AI model will have a hard time finding the correlation between them.
- Duplicated records: With duplicated records, the AI models won’t be able to generate unbiased results. It can also cause specific information to repeat itself and compromise the accuracy of the results.
- Lack of scalability: If the involved technology stack cannot be scaled on the go based on the increasing data volume, it will be difficult for prompt analysis.
Conclusion
Here we have described the seven steps involved with preparing data for machine learning and artificial intelligence projects. Based on the illustration, it’s evident that professionals must pay undivided attention to this task before using the algorithms for evaluation and report generation. You can also outsource data preparation projects to reputed and skilled AI consulting services so as to reduce the discrepancies and risks.
