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Sameer Rane

Data is obviously a critical asset for today’s enterprises. However, this asset can be tantamount to value only when it provides useful insights. Also, data must be of prime quality for enterprises to derive meaningful and reliable insights from it. For maintaining data quality, it is also essential to provide good-quality input data to the right consumers, at the right time, and in an agile manner. However, huge data volumes and heterogeneous data formats could be a challenge here. To address these challenges, enterprises must use robust automation of data quality management processes and ensure their continuous evolution. Enterprises need to analyze the overall data quality management processes and identify opportunities to reduce manual activities and enhance the overall performance. Artificial intelligence (AI) and machine learning (ML) can play a vital role in implementing this.

Hence, both data quality and AI/ML complement each other for improving the overall data quality management process.

The Importance of Data Quality for AI/ML

While enterprises are keen to leverage AL/ML to acquire meaningful insights from the enormous data they hold, the accuracy of the AI/ML models depends on the volume and quality of the data used as an input for training them. Inaccurate input data results in misleading outcomes delivered by the AI/ML models. This makes it imperative to ensure high data quality for AI/ML-based data processing by implementing the following steps:

  • Auto-profiling of Data Prior to Analytics: Usually, data scientists examine the data before conducting a detailed analysis to study the general patterns within the data and identify any specific field values that are either very similar or identical. This may potentially influence the analysis process. A workable approach to remedy this problem is to auto-profile data across all possible data quality dimensions and analyze the statistical properties of data before correcting it.

  • Data Treatment: Data profiling results can guide data scientists to decide whether certain data anomalies must be treated (using value replacement, enrichment, standardization and such), or left as-is. Even data fields with valid values may require normalization or transformation so that the resultant data can be fed into the AI/ML model. Thus, it is evident that the scope of data quality goes much beyond the traditional aspects of data cleaning and correction.

  • Master Data Analysis: For scenarios wherein data analysis must be performed on master data, data deduplication needs to be performed.

The Role of AI/ML in Data Quality

Business users and data scientists require huge volumes of on-demand, quality data for analyzing and building data models. They prefer to invest their time in analyzing data rather than improving its quality and preparing it for analysis. Hence, enterprises need to focus on automating data quality operations and repetitive tasks to reduce manual interventions. To enhance the level of automation, enterprises must identify the areas of data quality management wherein AL/ML models can contribute. Some scenarios include:

  • AI/ML can be used to classify critical data elements and help identify relevant data elements in unstructured data feeds, such as social media formats, before performing data quality operations on target data elements. Such a classification can help in product categorization and hierarchy management thereby improving the product data quality.

  • Based on the diagnosis of data, AI/ML can send notifications for relevant data elements proactively and recommend rules for data profiling, data cleansing, standardization, and enrichment. Business users can validate and include these rules for data quality processing. This reduces effort in manual configuration of rules and helps to enhance the existing rule set. AI/ML can also effectively recommend the best-suited business data elements and relevant criteria for data deduplication.

  • Based on earlier instances, AI/ML models can learn the typical manual override tasks performed by data stewards, such as data correction, and merge and split operation, and perform relevant corrections from subsequent iterations.

  • AI/ML can be used for elevating multilingual data processing capabilities of a data quality management software. For instance, to process a social media comment in a language that the software does not recognize, AI/ML models can be trained and used to translate the content into one of the supported languages.

Data Quality and AI/ML - The Symbiosis

While high-quality data is essential for reliable AI/ML-based data analytics, such analytical models can, in turn, be leveraged to effectively automate data quality operations. Hence, data quality management and AI/ML mutually benefit each other, and their collaborative usage can deliver higher business value as against what they would have delivered individually.

About the author

Sameer Rane
Sameer Rane handles market analyst connect and marketing aspects of TCS MasterCraft™ DataPlus, which is a data management software from TCS. Sameer has 17 years of experience in the IT industry and has played a leading role in maintenance, deployment, and management of software products and solutions. His current areas of interest include data privacy and data quality management.
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