How AI Can Ensure High-Quality Data
October 1, 2022

How AI Can Ensure High-Quality Data

It’s no longer a secret that artificial intelligence (AI) has the potential to be a crucial tool for businesses. That’s because it can extract informational nuggets from massive volumes of data that first seem to be unconnected.

However, early users of AI are beginning to understand that feeding AI random data is a certain way to fail.

Indeed, as AI models are being trained, data quality is becoming a crucial success element. The organisation can accelerate the creation of more AI-driven apps, cut expenses, and increase the performance of its AI strategy using high-quality data.

As it turns out, AI could potentially provide a way to guarantee high-quality data.

Here’s how you get started with a successful data quality management strategy:

How AI Can Boost the Quality of Data

In most business models, AI is the only technology that can handle the amount and complexity of data necessary without blowing your IT budget, making it the perfect solution for data quality management (DQM). A few essential aspects of data quality, including correctness, completeness, dependability, and relevance, may also be directly impacted by AI. Each of these areas involves extensive investigation, which AI can do more quickly, more efficiently, and at a lower cost than a large staff of analysts.

But in order to fully get why AI is the ideal tool for DQM, we must first comprehend why DQM is a special multidimensional challenge:

The chief scientific officer of data analytics company Mastech InfoTrellis, Pradyumna S. Upadrashta, lists the numerous aspects of data quality management. These, for instance, include:

Data sets have many different characteristics, such as accuracy, relevance, and validity.

Each department that works with a data collection has a unique perspective on it.

Therefore, enhancing data quality requires a variety of procedures, such as establishing data profiling methods that take into account the kind of data, the location and method of storage, the applications it supports, and the stakeholders that utilise it.

Taking into account the data quality reference repository that maintains the metadata and validity standards required by external procedures.

A portion of these procedures are taken into consideration by data-centric AI, a trendy issue at the moment that places an emphasis on data quality above quantity, particularly for commercial applications of artificial intelligence.

Automation can make sure the pipeline of the process can update the rules that determine the quality of the data and verify it regularly.

The Issues with AI-Powered Data Quality Management

The Paradox of Data Quality

Because the AI must be trained with high-quality data, using AI to enhance data quality may be challenging. In other words, before your AI solution can recognise high-quality data, it has to be trained on high-quality data.

So what is the answer?

Patrick McDonald, director of data science at Wavicle Data Solutions, offers one possible response. McDonald advises building a strong foundation of data governance and stewardship, ideally under the direction of an in-house manager, and then connecting it to an extensive data monitoring programme as the first step to AI-driven data quality control.

Since it is the most straightforward to manage and often the most vital to the business model, the master data store is an excellent place to start.

The Problem of Observability

According to Krystal Kirkland of Arize, the effectiveness of the resultant AI models may be significantly impacted by the capability to not only “see” data in the pipeline but also to watch its mobility and progress. For developing machine learning operations (MLOps) environments, this is especially crucial.

Increasing observability when data is generated, stored, merged, and evaluated is another need for improving data quality.

When deciding how to increase observability, it’s crucial to take into account both category and numerical data since sudden changes in different data properties, as well as missing and mismatched data, may effect both. Organizations will have to work considerably harder to choose the right degrees of accuracy, relevance, and usability when the data is unstructured.

But the fact that it is an ongoing effort is likely the largest obstacle to developing excellent data quality. One is that “quality” is an ill-defined measure. The values that data reflect in the actual world are also constantly changing.

How to Improve Data Quality to Begin with

If the idea of creating an AI-driven data quality management plan is giving you a headache, don’t worry. Understanding the source of poor data is the first step in any DQM approach, according to tech author George Krasadakis.

The main causes of poor data quality in most businesses are often faulty software, system-level problems, and the frequently changing formats that muck both source and destination data repositories.

In other words, the data ecosystem that the average organisation has spent millions of dollars developing is the source of the problems with data quality.

Identifying what “quality data” means to your company is a crucial first step. You must develop benchmarks to decide what you mean by “quality” since data is only useful in respect to other data.


It is probable that creating and managing high-quality data will eventually become a crucial task for the organisation that has undergone digital transformation. And it’s a task that will keep humans and AI working together for a very, very long time.

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