For most businesses, data is a crucial asset. However, it is unreliable if the data is worthless. Although data support major business initiatives, the data accuracy is still low in many organizations, which is a growing concern. Poor data quality contributes to a crisis in business value and trust in information.

Data collection systems and database always have issues with data quality, and ML raises new concerns with transactional databases. Complex, unstructured and streaming data and the use of vast sources of data add to data quality issues, and new concerns are raised by training and modelling.

Data quality issues impacting AI Ecosystem

Obtaining and maintaining data is a complicated task. Most of the projects in the AI ecosystem are based on Machine learning (ML) systems. Various data quality issues threaten the AI ecosystem. 

Data quality is useful to enhance business process and improve customer experience. Organizations with high-quality premium data can leverage data as a competitive and valuable asset to drive more revenue.

Business organizations make misguided decisions when using poor quality data that is outdated and inconsistent, resulting in inaccurate business plans and contradictory reports.

Data quality issues in AI are as follows:

Incomplete, Inaccurate and Improperly labelled Data 

Incomplete and improperly labelled Data can lead to AI project failure. Bad data at the source or unprepared data can lead to these issues. The entire data preparation industry is addressing the data cleanliness issue.

With the enormous volumes of data, traditional approaches are insufficient to clean the data, resulting in AI-powered tools to spot and clean data quality issues.

Having too much or too little data 

Having more data is crucial because data is essential for AI – projects. However, having too much data when using Machine learning (ML) doesn’t help sometimes. Therefore, sometimes having too much data results in data quality issues. Organizational resources are wasted while going through the entire data sets to separate useful data.

In contrast, having too little Data also comes up with its own set of problems. Small data sets produce results that may be biased or of low complexity. It results in inaccuracy while dealing with new data.

Systematic Bias 

Large data sets are required by Artificial Intelligence (AI) and Machine Learning (ML) to train their models. These large data sets may be subjected to systematic bias leading to the violation of social norms and creating accuracy issues. Identifying bias is the most challenging part, especially within the training data. 

Unbalanced Data 

Several factors are needed to be considered when addressing biased data. One of these factors could be unbalanced data. The performance of Machine Learning models can be hindered with unbalanced data sets. These data sets have the presentation of data from a group or community, reducing the representation of another group.

Data Silos

A data silo refers to limited individuals or a specific group of an organization having access to a data set. Various factors such as restrictions on integrating data sets, technical challenges or security access control of data can result in a data silo. 

Data silo limits an organization’s ability to work on AI projects to access comprehensive data sets by lowering the quality of results.

Inconsistent Data 

Collecting too much data results in the collection of irrelevant data, which will be used for training. Training the AI model with clean but irrelevant data can result in training system issues because of the low quality of data.

Different data sets can include the same records multiple times with different values in a few circumstances resulting in inconsistency in data. For data-driven businesses, duplicate data is one of the most challenging issues. Inconsistency is an indicator of data quality problem when dealing with multiple data sources.

Data Sparsity

Insufficient quantity of data or missing data in a data set results in data sparsity. The performance of machine learning algorithms and the ability to calculate predictions accurately is impacted by data sparsity. Unidentified data sparsity results in models trained on noisy and insufficient data, impacting the project results’ overall accuracy.

Data Labeling issues

Machine learning requires the labelling of data with correct metadata to be able to derive insights. Lack of proper labelling of machine learning data for training purpose is one of the most significant data quality issues in the AI ecosystem.

 When the data is accurately labelled, it ensures that machine learning systems establish reliable models for recognizing patterns. A good quality labelled data accurately trains the AI system upon which data is fed.

Finding a solution for data quality problems 

Organizations need to consider innovative solutions to address their data quality issues better rather than sticking to standard Business Intelligence (BI) Tools. More enterprise architectures can address data quality challenges because the data quality solutions market is growing.

For any data quality problem to be solved, you need to ensure that your training data and working data are of high quality for the task to be completed.

To make sure the data quality is high, you need to perform a few tasks. They are:

1. Data analysis- Analysis of data including data distribution, characteristics, relevance and source.

2. Domain expertise-   Gaining insights from the subject experts to explain unexpected data patterns so that valid and potential information is not lost and the invalid results do not influence the results.

3. Documentation- The process of documentation needs to be repeatable and transparent. An excellent way to maintain metadata is a data quality reference store that makes adjustments and creates new algorithms easier.

4. Review- Conduct a review of outliers or anything which may be suspicious considering business conditions.

The improvement of data quality impacts the speed of AI implementation. To improve data quality, you need to pay attention to data selection, capturing, cleaning and storage. There are various tools and software that are available to achieve this. 

Data can be corrected and validated against third party databases with the help of verification software. Data should be processed in such a way that it becomes suitable for AI and ML models.

Conclusion

Organizations working to implement successful AI projects need to be attentive to their data quality. Data credibility is one of the most useful and valuable assets that organizations have in today’s competitive environment. 

An AI-analytics solution can address data integrity issues at the earliest point of data processing. Using proactive alerts around data quality and integrity can help save resources and offer valuable opportunities to the business.