AI-ready data: overcoming barriers and shaping AI’s future

What is AI-ready data?

Data readiness for AI refers to the state in which data is maximized and suitable for use in AI and machine learning models. AI data readiness involves data cleansing, structuring, integration, and ensuring high-quality data standards. When data is properly prepared and meets consistency standards, it can effectively train AI models and produce accurate results.

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For instance, AI-ready data does not have inconsistencies, duplicates, or missing values. Structured numerical data or annotated text are suitable formats to organize data that are easy to interpret. Companies with effective data readiness plans can get more reliable AI results.

What does it mean for data to be AI-ready?

Data are AI-ready when they meet specific quality criteria to assist AI tasks. If you want to train complex AI systems like generative AI models, you will need accessible and complete data.

AI data readiness involves several features, such as:

• Quality. High-quality data is without errors, duplicates, and irrelevant information. Low-quality data can lead to biased or inaccurate AI results.
• Structure. Data should be well-organized, no matter its nature. Structured data is easier to process and interpret for AI.
• Accessibility. Data should be readily obtainable across platforms. If you do not want to slow down the data process, do not put them in silos.

Companies who want to take advantage of AI models must understand the importance of AI readiness. Also, having strong data governance and proper assessment tools to help manage quality and accessibility is a key step to enhancing data maturity.

According to a recent report from Harvard Business Review on “Data Readiness for the AI Revolution”, 59% of respondents indicate “establishing data governance policies, standards, and frameworks as the main area of improvement for the companies to adopt AI.

How to make your data AI-ready?

Is your data ready for AI? Don’t worry about it. You can create your AI-ready data in 5 simple steps. Just follow the following list and start to turn your raw data into AI-compatible datasets:

1. Data collection and cleaning. First, collect data from trustworthy sources and tidy it up by correcting errors, addressing any missing values, and standardizing the formatting. You can use data cleaning tools to automate this process, ensuring that data remains consistent and valuable.
2. Data structuring and annotation. Having structured and annotated data makes it easier for AI models to comprehend and learn from. For instance, add labels or annotations to your text data, while for image data you can add objects or features within the pictures.
3. Integration and transformation. Build your datasets from as many solid sources as possible you can, namely if you are training a generative AI model.
4. Quality assurance and assessment. Some companies usually employ an AI readiness assessment tool to appraise the performance of their data quality. This also helps determine the gaps and what they can improve. Subsequently, it enhances data readiness for the company.
5. Ensuring security and compliance. Implement data governance practices to handle data securely and comply with industry regulations. Namely, if your company deals with sensitive user data.

Discover the 3 types of data used in AI

In AI, you can divide data into three main categories:

• Structured data. Highly classified data, such as numbers or strings, in a tabular scheme. Some examples are monetary exchanges or consumer profiles.
• Unstructured data. Information without a predefined style, such as text, images, and videos. AI applications like natural language processing (NLP) and computer vision often bank on unstructured data.
• Semi-structured data. Data that is not fully sorted but contains tags or markers, like XML files or JSON formats, making it partially systematized.

Gathering and merging these data sets is fundamental in creating AI-ready data. Each set needs unique management and processing to guarantee compatibility with AI models.

Reveal the AI-ready data products (e.g., AI-ready AWS)

Different tools and platforms assist companies in making AI-ready data products. Here is a list of the top ones:

• Amazon Web Services (AWS). AI-ready AWS solutions furnish data storage, transformation, and machine learning instruments to support data readiness for AI. For example, AWS SageMaker provides, tools for data labeling and model training, making it easier to develop full-spectrum AI applications.
• Microsoft Azure AI. Azure’s platform supports a full suite of data preparation and machine learning services, including data annotation, transformation, and model training. It’s quite a common choice for enterprise-scale AI applications.
• Google Cloud AI. Google’s AI platform includes BigQuery for data storage and DataPrep for cleaning and structuring data, making it a fantastic option for preparing data for AI applications.

These tools simplify preparing data for AI and aid organizations to have powerful, consistent data assets. For additional insights, take a look at AI consulting.

Enjoy the benefits of AI-ready data

Preparing your data for AI provides many benefits, from improving your model accuracy to speeding up innovation. Key edges involve:

• Better decision-making. Thanks to high-quality AI-ready data, you will make better decisions providing accurate insights and predictions.
• Operational efficiency. Data preparation tools automate repetitive data management tasks, saving time and money.
• Improved consumer journeys. AI-ready data enables more tailored services, especially in fields like online shopping, medical care, and SaaS, where understanding user preferences is vital.

Read more information about the role of AI in SaaS and how it is enhancing its customer paths.

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Prevent the future trends in AI-ready data (data readiness for AI)

Do you want to know the future trends of AI-ready data before your competitors? Let’s start your AI exploration by revising the following list:

1. Data-centric AI development. In the next years, companies will need to focus more on data than the model. So, before refining algorithms, they will clear data to get high quality and readiness.
2. Increased automation in data preparation. Thanks to the progress in generative AI and machine learning, the data preparation process will be automatized without the need for people for tasks like labeling or cleansing.
3. AI-driven data governance. We will use AI to rule data governance and manage compliance checks, labeling, and security protocols. Accordingly, we will reduce handling time while enhancing data security and consistency.
4. Real-time data insights. Edge computing and IoT will enable real-time AI data processing directly on devices, allowing for faster decision-making and reducing latency.

Consistent AI implementation strategy will be essential as these future trends in AI-ready data continue to evolve, ensuring that businesses not only adopt new technologies effectively but also maintain alignment with their long-term goals and data integrity standards.

Face the challenges in collecting data for AI and secure high standards

Data collection is a challenging process for making AI-ready data. This is particularly true for industries like finance or healthcare, which have complex data sources.

Diverse data sources

Data for AI applications often come from many sources, such as customer interactions, sensor data, or external databases. Considering their different nature, they require intensive data integration processes.

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Volume and velocity of data

Thanks to the development of IoT and edge computing, it is possible to produce data quickly. Data engineering teams have to work accurately and timely to collect, store, and process large volumes of data in real time.

Data privacy and compliance

Data compliance is fundamental to safeguarding user privacy in all regulated sectors. If an AI company wants to comply with data governance, it should filter, anonymize, and safely store data before using AI models.

AI-ready AWS solutions are a clear example as they offer storage, processing, and compliance capabilities for companies managing huge datasets. McKinsey’s early 2024 survey highlights how companies across various industries budget for generative AI development. Many organizations are putting comparable portions of their digital budgets toward generative AI and traditional analytical AI.

However, more respondents indicate that their companies are dedicating more than 20% of their budgets to analytical AI than to generative AI. Looking ahead, 67% of respondents expect their companies to boost AI investments over the next three years, making a significant step forward in AI adoption.

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Shed light on the role of AI in data preparation

AI can make a difference in data preparation. It creates a self-reinforcing cycle, optimizing data quality for future applications. The so-called AI-driven data preparation employs machine learning to automate many duties, such as:

Data cleaning. AI algorithms detect mistakes, deviations, and empty fields, guaranteeing consistent, high-quality data. Accordingly, data scientists can save time during data cleansing.

Data labeling. Those who work in fields like image recognition and NLP know how important data labeling is for their work. AI models can now label data naturally, making huge datasets available for AI model training.

Real-time data transformation. Transforming data coming from multiple resources is essential in sectors like customer service, which deal with revelations coming from unstructured interchanges. AI tools can turn unstructured data like emails into regulated patterns.

Want to learn more about AI-powered data preparation? See this overview on Big data analytics.

Key takeaways

Any company aiming to take advantage of AI must create AI-ready data. Firms can exploit the AI potential to improve problem-solving, workflow optimization, and customer experiences by solving typical data readiness obstacles, carrying out best practices, and employing the newest tools. As AI technology keeps evolving, companies have to invest in data readiness to uphold market competitiveness in the rapidly advancing AI ecosystem.