Despite its enormous promise, artificial intelligence (AI) has yet to gain traction in most businesses. Yes, it has revolutionized consumer internet businesses like Google, Baidu, and Amazon — all of which are big and data-rich, with hundreds of millions of users. However, for predictions that AI would generate $13 trillion in value per year to be realized, businesses such as manufacturing, agriculture, and healthcare must discover methods to make this technology work for them.
The issue is with the rulebook used by these consumer internet firms to develop their AI systems — where a single one-size-fits-all AI system to provide service to a vast consumer base — will not work for these other industries.
Data is devouring the globe, and businesses must guarantee that data quality improves to advance their operations. Data fuels AI, and it's time for AI practitioners to switch their attention from model/algorithm development to the quality of the data they use to train the models.
Data-Driven AI/ML Models Have Single Source of Failure: Data Quality
In most situations, data quality proved to be a significant component in project success. Data warehousing, data integration, business intelligence, content performance, and predictive models are some examples.
In each scenario, the apparent issue was to query heterogeneous data sources successfully, then extract and convert data towards one or more data models. The non-obvious problem was the early detection of data flaws, which were sometimes undiscovered to the data owners.
According to Andrew Ng, under the prevailing model-centric approach to AI, you collect all the data you can and create a model good enough to deal with the noise in the data. The established procedure asks to keep the data constant and repeatedly refine the model until the desired outcomes are achieved. "Data consistency is paramount" in the fledgling data-centric approach to AI, as per Ng. To achieve the appropriate results, you fix the model or code and constantly enhance the data quality.
Data Quality in AI/ML Models
It is widely estimated that data cleansing accounts for 80% of machine learning. However, since data preparation accounts for 80% of our labour, why are we not assuring data quality for a machine learning team?
Everyone makes jokes about how ML requires 80% data preparation, but no one seems to care. A short peek at the arxiv will give you a sense of where ML research is heading. The competition to beat the benchmarks is at an all-time high. OpenAI has GPT-3, whereas Google has BERT.
However, even elaborate models account for just 20% of a commercial challenge. The data quality distinguishes a good deployment and outcome; everyone may get their hands on pre-trained models or licensed APIs.
The Importance of Data Quality
Data quality is essential, especially in the age of automated choices, artificial intelligence, and continuous process improvement. Corporations must be data-driven, and data quality is a crucial need.
Confusion, a lack of trust, and poor judgments
In most situations, data quality concerns explain business users' lack of faith in data, resource waste, or even bad judgments.
Consider a group of analysts attempting to determine if an anomaly represents a vital business finding or an unknown/poorly handled data problem. Worse, envision real-time choices being made by a system incapable of identifying and dealing with bad data that has been inadvertent — or even purposefully — included.
Failures as a result of poor data quality
Most Business Intelligence, data warehousing, and similar efforts fail due to a lack of involvement from key users and stakeholders. Typically, low engagement is due to a lack of confidence in the data. Users must trust the data; otherwise, they will progressively quit the system, affecting its key KPIs and success criteria.
Whenever you believe you've completed an extensive data discovery project, double-check for quality concerns first!
To fully realize AI's potential, leaders across all industries must embrace a new, data-centric approach to AI development. They should specifically seek to create AI systems while paying close attention to ensuring that the data adequately expresses what they want the AI to understand.
This necessitates focusing on data that covers critical instances and is consistently labelled so that the AI can understand what it is meant to do from this data. To put it another way, the key to developing these beneficial AI systems is to assemble teams that can program with data rather than code.
At Quantumics.AI, our mission is to make data continuously available in a clean format, be it for analytics that the business users can use to make business impactful decisions or for Data Scientists to train the ML/AI models. We call this Citizen DataOps.
To see it for yourself, signup for a free version. https://app.quantumics.ai/signup