If leaders have confidence that building successful AI initiatives is always guaranteed with Jupyter, then it is time for a reality check, and it could be shocking.

As enterprises navigate the intricate complexity of AI in their operations, not everything surrounding the tech delivers good news. Studies show that nearly 85% of AI models may fail miserably for enterprises. And contrary to the popular belief that the models or algorithms were poorly built, the real culprit is poor data quality. Be it across acquisition avenues, processing nodes, or in actionable workflows, data insights being fed into AI models play a huge role in determining the success of the initiative.

But unfortunately, today’s organizations deal with significant problems in their data management approaches. It leads to poor performance, inefficient results, and costly mistakes that can derail ambitious digital investments.

The consequences of messy data

Messy and siloed data causes more harm than anything else when it comes to building AI and ML models that consume them. It becomes extremely difficult for such models to handle real-world scenarios when deployed for a range of use cases. Let us evaluate some of the core reasons why:

Inaccurate processing

Siloed or messy data may hoard a significant amount of information that is incorrect or error-prone. They could be missing key values, captured without entirety, irregularly standardized, and can even have inconsistencies, such as in the case of cyclically observed and captured data not adhering to normal traits. This messy data pipeline can create havoc when machine learning models are fed with the same data during training.

The ML models may interpret the data in a very different fashion than what they should be doing, and this causes the resulting computational analysis to go wrong by a huge margin. The model is taught wrong patterns because of erroneous or inconsistent data, and hence, it will generate outcomes or predictions inaccurately owing to the same. This can create serious consequences in avenues where such predictions form the foundation of decision-making like for example, in weather predictions.

Performance bias

When messy or stale data is used to train ML models, there will be an inherent risk of the model having a biased outlook in a real scenario. For example, when an inconsistent or error-prone data pattern is used to model and train an AI algorithm, it will exhibit long-term affinity towards interpreting scenarios based on the faulty data. This prevents the model from generalizing and understanding new or unseen circumstances, as the bias tilts its decision-making abilities to a wrong inference roadmap. The poorly collected data can amplify the bias when it is reinforced during the training stage.

The outcome of this bias will be harmful for the business as the AI or ML model may exhibit discriminatory results or predictions that could be unfair to customers who avail digital services powered by such models. This would be alarmingly dangerous in sectors like cybersecurity. If ML models are biased in deciphering and identifying threats, they may be tricked into vulnerable permissions, and fraudsters may gain access to critical digital infrastructure and cause irreparable damage.

Cost escalations

Data quality and inconsistency do not just harm the proper functioning of ML algorithms and models, but also result in significant economic repercussions for enterprises. Siloed and messy data streams require a high degree of cleansing and standardization effort, which consumes both time and money. Additionally, there is resource wastage across the entire digital ecosystem of the business due to poor approaches in acquiring high-quality data.

Adding to the cost burden are the consequences of using low-quality data for training AI models. The results or actions executed after the training can cause both reputational damage for the brand and severe issues across the customer experience domain, resulting in the brand incurring cost escalations to fix damages.

The fix

Data quality problems like siloes plague entire business outcomes as AI becomes a driving force of customer experience. To tackle them, organizations need more than just a couple of software tools. They need to build a robust ecosystem of an end-to-end data modernization framework that ensures a steady supply of clean and reliable data for all AI and ML models.

This framework needs to formulate a winning and comprehensive strategy that covers not just data management, but a deep-dive, granular focus on:

Data governance

Build policies, protocols, processes, standards, etc. to ensure that data assets are efficiently handled, assured of quality, security standards in place, and usage patterns are defined and executed tediously throughout the lifecycle of a data asset.

Data standardization

Ensuring that acquired data from across the business is standardized to ensure that consuming AI models are supplied with context-driven, clean, and filtered data streams in formats they require for processing.

Data validation

Assuring factual, contextual, and situational accuracy of the data to ensure efficient and reliable outcomes from the consuming AI services.

Continuous quality monitoring

Setting up surveillance and management strategies to ensure that data streams are continuously vetted for their quality, reliability, and accuracy.

Leaping forward with AI success

Reaping the benefits of quality AI initiatives requires businesses to strategically prioritize data quality in degrees far beyond traditional digital approaches. What enterprises need is to ensure the availability of the right tools, the right guidance, and strategic oversight of end-to-end data quality management when venturing into using critical data assets for AI initiatives. This is where a technology partner like Parkar can be a major asset. Our end-to-end data modernization and strategy guidance capabilities ensure that businesses get access to a one-stop destination to build the most reliable data foundation for their AI initiatives. From building efficient data meshes to assuring reliable multi-cloud ingestion accuracy, Parkar can strategically guide enterprises to build a healthy and secure data pipeline for their ML models and consequently enjoy its success. Get in touch with us to learn more.

FAQ

Why is data quality important for ML initiatives?

Data quality determines the accuracy and reliability of AI-driven business outcomes since models exhibit behaviour they learn from the data used to train them.

What does messy data do for ML models?

Messy or siloed data creates inherent bias in ML models, resulting in significantly damaging experiences being created from them.

How can enterprises fix siloed data problems in their AI initiatives?

Enterprises can fix their data quality and silo issues by following an end-to-end data modernization and management strategy guided by an experienced partner like Parkar.