AI Data Preparation: Navigating Readiness in the AI Era

Artificial Intelligence has captured the attention and ambition of organisations across virtually every industry. With the incredible pace of AI innovation and immense potential that machine learning offers, business leaders have raced to implement AI tools.

However, in the scramble to take advantage of AI’s transformative capabilities, many organisations have overlooked a crucial aspect – the quality and readiness of their data to adequately fuel AI initiatives. Like constructing a house without a solid foundation, implementing AI solutions without proper AI data preparation is a recipe for failure and wasted investment.

The Limitations of Bad Data Inputs in AI Data Preparation

No matter how advanced the AI algorithms and models, they can only be as effective as the data used to train them. Poor, inaccurate or incomplete data leads to poor outputs and failure.

As the saying goes, “garbage in, garbage out.”

Biased, low-quality data results in AI systems that perpetuate harmful biases, inaccuracies and ethical risks. AI trust and transparency issues arise when data preparation best practices are neglected. According to Gartner, over 60% of AI projects will fail to deliver on business SLAs and be abandoned through 2026, and one of the key contributors is poor data quality, highlighting the necessity of data preparation software to standardise, cleanse, and optimise datasets.  

On the flip side, high-quality data fuels high-quality, accurate and responsible machine learning that can be trusted to drive real business impact. In other words, AI cannot be effective without first having reliable data.

Tackling the Unstructured Data Challenge

Achieving the level of data readiness to extract maximum value from AI investments is a persistent challenge. A major roadblock is the sheer volume and sprawl of unstructured data like documents, emails, audio/video files and other formats residing across silos in most enterprise environments.

With over 90% of an organisation’s data assets being unstructured, much of this “dark data” remains inaccessible, ungoverned, and riddled with issues like duplication, lack of categorisation and unidentified sensitive information. As long as this mountain of dark, unstructured data is unmanageable, it cannot be leveraged to fuel trustworthy AI initiatives.

The Path to AI Data Preparation

The journey to AI success starts with data readiness which can only be achieved through meticulous AI data preparation. To prime data for AI initiatives and avoid derailment, organisations need more intelligent, automated methods to:

• Discover, classify and extract relevant data points from both structured and unstructured sources
• Enrich and organise that data into a normalised repository
• Apply data governance policies and de-risk enterprise information
• Feed the enriched, normalised data pipelines to AI systems

EncompaaS optimises AI data preparation by leveraging next-generation AI technologies to find, enrich, organise and de-risk structured, unstructured and semi-structured content anywhere in the enterprise with speed and accuracy. This data preparation allows organisations to take advantage of the vast reserves of unstructured data and ensures high-quality, well governed data fuels AI initiatives.

“EncompaaS curates cutting-edge, AI services, and we orchestrate data through those services so you can understand what data you have, where it is in your organisation and its value to your business.”

Jaimie Tilbrook, Chief Product Officer, EncompaaS

Build AI Effectiveness Through Intelligent Data Preparation

Ultimately, AI data readiness comes down to extending intelligent information management capabilities across the entire information landscape, especially unstructured data sources. Implementing data preparation best practices enables organisations to:

• Generate the highest quality, normalised training data that AI/ML models need
• De-risk information by identifying and protecting sensitive data to enable responsible AI
• Maximise return by leveraging previously untapped reserves of unstructured data
• Build trust in AI systems through transparency, auditability and accuracy

The potential of AI and machine learning is immense, but that potential will remain unfulfilled without reliable, well-governed data inputs derived from intelligent AI data preparation. As organisations charge forward into the AI era, gaining visibility and control over their entire information estate – and particularly their unstructured data reserves – will be essential to navigating AI readiness and achieving meaningful, trustworthy results.

To learn how EncompaaS can help your organisation achieve AI readiness through effective data preparation, contact us or book a demo.