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Principles of AI-ready data: Put yourself to the test

You can’t just throw data at AI programs and expect magic to happen. This approach may work for the first few AI projects, but it means that data scientists increasingly spend more time correcting and preparing data as projects mature. Instead, we have defined six principles that need to be followed to ensure that the data that goes in is accurate and reliable.


Because it may seem pretty obvious that AI can’t exist without good data, but you’d be shocked at how many organizations don’t put enough rigor into ensuring they have robust data foundations. That’s why in this episode of Visionary Voices, Ronald van Loon sits down with Accenture’s Managing Director of Data & AI, Tim Zhou, to discuss the importance of strong data foundations, the six key principles for making your data AI-ready and the challenges behind it.


Ronald and Tim dive into real-life use cases of AI in action. From financial services and automotive to the manufacturing industries, Tim talks about the ROI and impact these organizations have seen. And what is the common element throughout all successful AI implementations discussed? A solid and trusted data foundation.


So, if the pressure to scale AI projects quickly is tempting you to skip the critical first step of the AI journey, think again. Because, as Tim says, it’s important that “the data foundation is trusted, reliable and up-to-date, so the insights coming out of the data can really help the business achieve value.”


Now, it’s time to put your knowledge to the test and see how familiar you are with the six principles that make AI-ready data.

01/06

What kind of data do reliable and responsible AI models need to be built on?

Select one: 
A

You can use any data that you can find on the internet

B

You need singular, narrow, concise datasets

C

You need diverse data from a range of sources

What kind of data do reliable and responsible AI models need to be built on?

Incorrect...

You cannot trust the source of the data found on the internet. Without knowing the source of the data, you can’t know whether it’s true. If you base your AI system on false data, this can lead to inaccuracies and false results.

Incorrect...

Narrow and siloed data sets can lead to bias within AI systems. By only focusing on a small number of datasets, these can include prejudicial assumptions, meaning the AI system is more likely to make unfair decisions.

Correct!

Diverse data means you draw from a wide range of data sources that span different patterns, perspectives, variations, and scenarios relevant to the problem domain. This is crucial for stopping bias in AI systems and ensuring that AI applications are less likely to make unfair decisions.

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Qlik Perspective

Diverse datasets are vital for organizations deploying AI to ensure fair and unbiased outcomes impacting employees, customers, and stakeholders. Data diversity promotes innovation, productivity gains, and trust in organizational AI systems.

02/06

Does data timeliness matter for building quality AI applications?

Select one: 
A

Timeliness of the data doesn’t matter, only accuracy

B

Fresh data is important – outdated information can produce inaccurate results

C

You can mix old and new data as much as you like

Does data timeliness matter for building quality AI applications?

Incorrect...

While it's true that ML and Gen AI applications thrive on diverse data, the freshness of that data is also crucial. Just as a weather forecast based on yesterday's conditions isn't conducive for a trip you plan to take today, AI models trained on outdated information can produce inaccurate or irrelevant results.

Correct!

Fresh data allows AI models to stay current with trends, adapt to changing circumstances, and deliver the best possible outcomes. Therefore, one of the key principles of AI-ready data is timeliness.

Incorrect...

Timeliness does matter, so using old data will lead to inaccurate and incorrect results. Think about it like a weather forecast based on yesterday's conditions. That won’t be conducive for a trip you plan to take today, AI models trained on outdated information can produce inaccurate or irrelevant results.

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Qlik Perspective

Timely data is crucial for organizations leveraging AI to make informed, up-to-date decisions. Outdated data can lead to inaccurate AI insights, undermining organizational agility and responsiveness. Real-time, fresh data empowers AI to deliver relevant predictions, identify emerging trends promptly, and swiftly adapt to evolving market conditions, ensuring sustained competitive advantage.

03/06

How important is it that your AI model is fed accurate data?

Select one: 
A

The closer to 100% accuracy the more trusted the AI model will be

B

AI models can parse through what is and isn’t accurate so it doesn’t matter

C

Accuracy doesn’t matter, only quality of data

How important is it that your AI model is fed accurate data?

Correct!

The success of any ML or Gen AI initiative hinges on one key ingredient: correct data. This is because AI models act like sophisticated sponges that soak up information to learn and perform tasks. Inaccurate data, however, is like a sponge mopping up dirty water, leading to biased outputs, nonsensical creations, and, ultimately, a malfunctioning AI system.

Incorrect...

AI models cannot detect what is and isn’t true. We’ve all heard the saying “garbage in, garbage out” and that’s exactly what will happen if you feed or train your AI system on inaccurate data. You’ll get inaccurate results!

Incorrect...

While its important enough to have enough data to improve learning and performance, inaccurate data can lead to incorrect and unreliable outcomes. Many people take what their AI systems tell them at face value, but you cannot trust the results of AI that is built on inaccurate and incorrect data.

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Qlik Perspective

High-quality, accurate data ensures that AI models can identify relevant patterns and relationships within the data, leading to more precise decisions, generation, and predictions.

04/06

How important is it for data to be secure when training your AI model?

Select one: 
A

AI models have built in security protections, so data security doesn’t come into play

B

It’s important, but too much emphasis on it can limit your AI models output

C

Security should be one of your main priorities, as it’s crucial to improving the overall trust in your AI system

How important is it for data to be secure when training your AI model?

Incorrect...

AI systems can do powerful things with sensitive data like Personally Identifiable Information (PII), financial records, or proprietary business information, but this power comes with a responsibility. Unsecured data in AI applications is like leaving the vault door wide open. Malicious actors could steal sensitive information, manipulate training data to bias outcomes, or even disrupt entire Gen AI systems.

Incorrect...

Security of your data is critical and should always be prioritized, this is why security is one of the six principles of AI-ready data. If you do not prioritize security above all else, it’s like leaving the vault door wide open. Protecting your data is key to improving the overall trust in your AI system and safeguarding its reputational value.

Correct!

Unsecured data in AI applications is like leaving the vault door wide open. Malicious actors could steal sensitive information, manipulate training data to bias outcomes, or even disrupt entire Gen AI systems. Securing data is paramount to protecting privacy, maintaining model integrity, and ensuring the responsible development of powerful AI applications.

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Qlik Perspective

Data security is paramount for trustworthy AI deployments within organizations. Robust data security protocols safeguard proprietary data assets, protect user privacy, maintain regulatory compliance, and uphold the integrity of AI systems against nefarious threats.

05/06

What is a best practice to enhance data discoverability?

Select one: 
A

Creating a business glossary and indexing metadata

B

Restrict access only to individuals who absolutely need the data

C

Focusing on unstructured data sources

What is a best practice to enhance data discoverability?

Correct!

AI-ready data needs to be discoverable and readily accessible within the system. Imagine a library with all the books locked away – the knowledge is there but unusable. By creating a business glossary and indexing metadata you enhance human understanding of the data and make the information easily searchable via a data catalog.

Incorrect...

Governance is a key component to ensure data security, and in cases with highly sensitive information it needs to be in place. But if you unnecessarily restrict access you prevent other business users in the organization from discovering the data and using it to help make informed decisions.

Incorrect...

AI systems are capable of using structured and unstructured data sources. Whatever the source, results must be easy to discover and access within the system for all relevant users.

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Qlik Perspective

Easily accessible and well-cataloged data empowers AI practitioners to quickly locate relevant datasets, fostering efficient model development and deployment. Clear data lineage and provenance facilitate trustworthy AI by enabling comprehensive understanding and auditing of data origins and transformations.

06/06

Is it crucial for data to be in the right format for Machine Learning (ML) experiments or Large Language Models (LLMs)?

Select one: 
A

No, it doesn’t really matter how the data is formatted, the systems can sort it themselves

B

Yes, AI initiatives won't be successful if the data is not in the right format for ML experiments or LLM applications

C

You don’t need to clean and organize data before using it for AI

Is it crucial for data to be in the right format for Machine Learning (ML) experiments or Large Language Models (LLMs)?

Incorrect...

That’s not true. Data transformation is the unsung hero of consumable data for ML. The effort invested in cleaning, organizing, and making data consumable by ML models reaps significant rewards. Prepared data empowers models to learn effectively, leading to accurate predictions, reliable outputs, and, ultimately, the success of the entire ML project.

Correct!

AI’s potential rests on the ability to readily consume data. Unlike humans, who can decipher handwritten notes or navigate messy spreadsheets, these technologies require information represented in specific formats. Making data easily consumable helps unlock the potential of these AI systems, allowing them to ingest information smoothly and translate it into intelligent actions for creative outputs. That's why making data readily consumable is the final principle of AI-ready data.

Incorrect...

Wrong! AI’s potential rests on the ability to readily consume data. Unlike humans, who can decipher handwritten notes or navigate messy spreadsheets, these technologies require information represented in specific formats so cleaning and organizing data is a crucial first step. Making data easily consumable helps unlock the potential of these AI systems, allowing them to ingest information smoothly and translate it into intelligent actions.

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Qlik Perspective

For AI systems to deliver maximum value, data must be easily consumable in formats compatible with AI workflows. Disorganized, fragmented, or incompatible data creates bottlenecks, hindering AI model training and inference.

Data readiness is the cornerstone of any successful AI implementation.

Without high-quality, well-organized data, AI systems can't learn effectively or produce reliable results that can help you make informed business decisions with confidence and streamline operations. Download our whitepaper 'The Six Principles of AI-Ready Data’ to build your understanding of the topics covered in this quiz.

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AI can’t succeed without good data. So what is “good” data in the context of AI — and how can you be sure you’re generating it? This white paper answers that question, with content including:

  • The six principles to follow for creating AI-ready data
  • How to boost efficiency with an AI Trust score
  • Where Qlik Talend can help

Unlock your organization’s true AI potential — and avoid the pitfalls of AI gone wrong — with data that’s optimized for today’s revolutionary technology.

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