#AI: Differentiation is in the data, not the algorithm

What makes the difference, for any AI project, is the quality and quantity of data that is available to feed the model.

Last week I was at the BI and Analytics conference in Sandton, talking about prerequisites for artificial intelligence.

Unlock the power of accurate data with Masterdata’s innovative data quality solutions.

I came across this video of a presentation done by Collibra CTO, Stan Christiaens in late 2017 in which he discusses AI, Big Data and Data Governance

He talks about a primary frustration of data scientists “I can’t find the data” and talks about how curating data through a governed data catalogue helps to solve this problem.

He continues to talk about the emerging competence of artificial intelligence and makes a telling observation

The differentiation is in the data

When talking AI the differentiation is not in the algorithm. AI processes are open-source, easily available and commonly shared. Data scientists “sit in Jupiter or Zeppelin typing in Python commands” and getting immediate results

What makes the difference, for any business, is the quality and quantity of data that is available to feed the model.

How does bias affect analytics?

Bias has a profound impact on analytics, influencing the accuracy and reliability of insights derived from data. Whether it’s cognitive bias in decision-making or systemic bias in data collection, these biases can distort the interpretation of data and lead to flawed conclusions. Recognizing and mitigating bias is essential for ensuring the integrity and validity of analytical findings. Explore more about how bias affects analytics.

How does bias hinder ML and AI?

Bias presents a significant challenge in the realm of machine learning (ML) and artificial intelligence (AI), affecting the fairness and effectiveness of algorithms. When algorithms are trained on biased data, they perpetuate and amplify existing biases, leading to unfair outcomes and discrimination. Addressing bias is crucial for building ethical and equitable ML and AI systems. Learn more about how bias hinders ML and AI.

What is intelligence bias?

Intelligence bias refers to the inclination to rely on preconceived ideas or stereotypes when interpreting data or making decisions. This bias can lead to erroneous conclusions and failures in leveraging big data effectively. Understanding and addressing intelligence bias is crucial for ensuring the accuracy and relevance of data-driven insights.

What is reporting bias?

Reporting bias arises when data collection or reporting processes are influenced by external factors, leading to skewed or incomplete datasets. This bias can distort the interpretation of data and compromise the validity of analytical insights. Addressing reporting bias requires meticulous attention to data collection methodologies and validation processes.

Is a lack of trust inhibiting adoption of AI?

A lack of trust acts as a significant barrier to the widespread adoption of artificial intelligence (AI) technologies.

Concerns regarding privacy, security, and ethical implications hinder the acceptance and integration of AI-driven solutions into various sectors.

Building trust through transparency, accountability, and ethical AI practices is essential for overcoming resistance to AI adoption.

What is the impact of poor data quality on machine learning?

Poor data quality significantly impacts the performance and reliability of machine learning (ML) models. When training data is inaccurate, incomplete, or biased, ML algorithms produce unreliable predictions and suboptimal outcomes. Recognizing and addressing data quality issues is essential for maximizing the effectiveness of ML initiatives. Delve deeper into the impact of poor data quality on ML.

Why Data Quality is Essential for AI and ML

Download the Precisely whitepaper Debugging Data: Why Data Quality is essential for AI and Machine Learning for a discussion on the types of data quality concerns that must be considered when embarking on your AI journey.

Three tips for machine learning

Success in machine learning (ML) hinges on foundational principles and strategies. Three fundamental tips laying the groundwork for effective machine learning include meticulous data preparation, thoughtful feature engineering, and rigorous model evaluation. By prioritizing these aspects, organizations can enhance the accuracy and reliability of their ML models. Explore actionable tips for machine learning success in this insightful guide.

Google investing in data

Why do you think Google has been buying data acquisition companies for years?

They do this to get the proprietary data that makes all the difference!

AI and machine learning bring new data quality challenges – it is not enough to know what you have, you must also begin to understand what you are missing.

Bias is truly the snake in the data grass.

Of course, AI is the pinnacle but some of us are still struggling with traditional BI. Read about strategies for dealing with the top challenges of BI.

AI and analytics are just one of many factors to consider when building the business case for data quality. Learn more!