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Data Quality In Artificial Intelligence Projects

Having a large volume of data does not equate to having data quality in artificial intelligence projects. Understand the problem and how to deal with it.

Data and the ability to process them abound. However, like most things in life, data quantity does not always mean data quality.

Data quality is critical to scaling AI projects. However, it is not uncommon for organizations to discover a problem with the quality of their data – which was not noticeable at first glance – during the execution of their projects or, worse, to be unaware of it.

And not just a problem. Organizations are dealing with multiple data-related issues simultaneously, directly affecting their ability to generate value through data for their AI projects.

But, if you think companies are already implementing actions to deal with this, take a step back: because they are not. Most lack the features they need to clean their data. The basics of data governance are often lacking. For example, they have difficulties tagging and monitoring data, creating and managing metadata, managing unstructured data, and other such actions.

However, the level of awareness of the problem is increasing. Organizations are increasingly aware of data quality’s importance and what they miss out on when they don’t clean up nicely.

In this post, we will talk about the main problems organizations have about data quality, the causes and consequences of this, and, finally, what kind of actions they can take to start solving.

The Main Problems Of Organizations In Data Quality

According to O’Reilly’s The state of data quality, organizations are not dealing with just one data quality issue but with a scenario where, on average, at least four or more joint problems like:

  • Inconsistent data from too many sources
  • Cluttered storage and lack of metadata
  • Poor data quality control at the input
  • Few resources available to deal with data quality issues
  • Unstructured data that is difficult to organize
  • Poor data quality from external sources
  • Little data categorized or not even categorized
  • Need for uncollected data
  • Addicted data.

Causes Of Data Quality Issues

Nor do such problems have a single cause. Among the several possible, isolated or combined, are:

  • non-integrated systems
  • Multiple sources for the same data
  • Information subjectivity
  • Errors, discrepancy, incompleteness, or missing data
  • data volume
  • Reality biased clippings
  • data not collected
  • Modifications, distortions, and data breaches.

The Impact Of Data Quality Issues On AI Projects

The level of accuracy of an analysis or a model is directly related to and dependent on the accuracy of the data and the ability to quickly provide the source of all the data used to produce it.

This relationship is apparent. After all, if you start from the wrong premises, the conclusions will be wrong no matter how correct your algorithm, that is, your logic.

Data quality issues like the ones mentioned above, if neglected, can put the reliability of analyses and entire projects at risk and, at worst, lead to biased models, which lead to wrong decisions, loss of business, customer dissatisfaction, and, therefore,,, losses.

A reactive attitude towards data quality also leads to high costs in fixing problems. Work on data quality and governance runs through all work with AI.

Data Quality: How To Implement To Improve The Effectiveness Of Models

Use Machine Learning And Artificial Intelligence Tools Applied To Data Quality

Using machine learning tools to simplify and automate some of the tasks involved in discovering and modeling data can speed up cleanup and impact activities, especially for companies challenged by volume, diverse sources, low quality, and unstructured data.

According to the O’Reilly survey, 48% of respondents used data analytics, machine learning, or AI tools to address data quality issues. These organizations are more likely to solve problems of this type.

Another technology that has been used to automate structured data cleaning – the tool doesn’t work for big data – is RPA.

Have A Dedicated Data Quality Team

Not everything is a tool: people and processes are almost always involved in both the creation and the perpetuation of problems with data quality; after all, data is created by humans or by sensors calibrated by humans.

Therefore, the commitment to governance necessary to diagnose and resolve such problems must also come from people. And so, we come to the need for a data quality team and the organization’s maturity in artificial intelligence in a data center of excellence.

However, this is not the reality for organizations: according to the O’Reilly report on data quality, 70% do not have teams dedicated to this function.

According to the researchers, they lose with this. A team focused on data quality can provide space and motivation to invest and learn about tools that optimize the improvement process. In fact, according to the survey, organizations with dedicated teams use AI and analytics tools to a greater degree (59% versus 42%).

Data Quality: A Continuous Work

Dealing with data quality issues is an ongoing process that is neither easy nor cheap. It will likely make the organization need to make decisions about where and how to apply its resources.

As we have seen, having AI projects that need quality data can catalyze and give direction to resolution actions, as it is a way to discover these problems.

In addition, it will be necessary to gain C-level sponsorship, study tools to achieve scale and productivity in data cleaning, and, finally, involve people in a dedicated team.

Also Read: What Are The Main Risks Of Artificial Intelligence?

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