Getting Your Data AI Ready

In the age of artificial intelligence (AI), data is becoming new gold. But raw data alone is insufficient to power the AI systems. How you prepare your data for AI deployment is a vital and often understated step. In this article, we are having a look at the key strategies and best practices in order to prepare your data for AI—ensuring that it’s clean, relevant, and ready for deployment.

The Delicate Process of Data Collection

Data is the lifeblood of AI. However, a dataset that is not sufficiently broad and robust will render even the most sophisticated AI algorithms unable to operate as designed. High-quality data contains the most accurate and relevant dataset possible without compromising the soundness of values within their intended domains.

Reducing data bias is another critical aspect of the collection process. It’s crucial to gather data from multiple resources, though it’s also important to avoid redundancies and offer qualitative, non-noisy data for the AI algorithm processing your request. Moreover, you need to standardise data collection protocols that will enable you to collect, analyse and validate for optimal functionality of AI algorithms.

Data Governance, Privacy & Security

In continuation of the collection process, we move towards data governance and protection, which is another crucial aspect of getting your data ready for AI.?

Victoria Bond , who was speaking at a recent event held by Fruition IT, alluded to the importance of data governance when using artificial intelligence, particularly with respect to GDPR, saying, “You’re putting in data that other people can use.”?

Bond emphasised, highlighting the need for stringent data governance and protection measures. Effective data governance involves implementing policies and procedures to control access to your data while maintaining the credibility, security and availability of your dataset.

Along with governance, you also need to implement policies to ensure data protection to deal with the continuously evolving issue of data breaches and cyber threats. This involves implementing security protocols such as end-to-end encryption, access control and regular audits to find vulnerabilities.

Data Transformation

Data transformation enables raw data to be converted into a format that can be used in artificial intelligence applications. In other words, transformation is a process of cleaning/preprocessing and transforming the raw data into meaningful information for AI algorithms. This ensures that AI models can learn effectively and produce accurate results without any abnormalities. Tools available for data transformation include:

• Use either Python libraries like Pandas, NumPy, Matplotlib, or R for data statistical analysis to clean the noisy data you have collected.
• Use libraries like Scikit-learn to perform preprocessing functions on the data, such as scaling and encoding or normalisation, while the TensorFlow Data API is great for building data pipelines.
• For supervised learning model development, you would require annotated data. Labelbox, Dataloop, or custom Python libraries are just some examples of data labelling programmes that can generate labelled datasets very effectively.
• Use libraries like Albumentations for image data augmentation or NLP augmentation tools for text data to increase model performance.
• Keep track of when the data changes or is updated. Use repositories such as DVC (Data Version Control) or Git LFS turnover to manage version control and ensure reproducibility.

Insuring Data Quality

Data quality is one of the main factors that contribute to the successful deployment of AI. The better the data, the more efficient the models are to produce accurate and reliable responses. Ensuring data quality involves rigorous validation, consistent updating and thorough vetting of data sources to eliminate errors and biases.?

Poor-quality data can lead to inaccurate models and misguided outcomes, undermining the potential benefits of AI. Therefore, it’s crucial to implement data quality management measures to use AI systems more efficiently and securely.

Finding Places to Use AI in Your Business

The sound of artificial intelligence alone is pushing businesses towards rapid and sometimes even unnecessary incorporation of AI. Overoptimistic about the possibilities offered by AI, businesses are rushing into investing time and money in how artificial intelligence can fit into their operations, though it’s a compulsory tick-box. Another aspect of the rapid deployment of AI is the fear of being left behind.

However, it’s crucial that you take a pause and figure out if you really need AI-powered modules in your organisation and, if yes, where exactly in the organisation it can bring value without going overboard with possibilities.?

Determining which AI business solutions you can implement to improve operations, make better decisions, or innovate would dictate the kind and amount that will help your business thrive. Going slow when adopting AI will help you align AI software with your business goals and deliver actual, tangible benefits instead of being added overhead or even a liability.