Leveraging Analytics and Machine Learning for Data Quality Excellence​

For large companies and major corporations, data is an immensely valuable asset when properly harnessed. It’s no wonder the global big data analytics market is forecasted to grow to $103 billion US dollars by 2027.

High-quality, trustworthy data fuels game-changing benefits like optimised business intelligence, automated decision-making, personalised customer experiences, and innovative AI and machine learning capabilities. 

However, achieving and maintaining reliable and consistent data quality at an enterprise scale can be an uphill battle. With data flooding in from a myriad of sources, fragmentation and quality issues like duplication, inconsistent data governance, and a lack of metadata proliferate.

This “mess” of data becomes exponentially harder to deal with via traditional manual processes, especially when factoring in unstructured data formats which account for 90 per cent of the typical organisation’s data landscape.

Analytics + AI: The Data Quality Edge

To truly harness the power of these vast data assets at scale, organisations need to move beyond traditional data quality software and processes. Intelligent information management platforms with inbuilt analytics and AI technologies offer a new generation of intelligent capabilities to automate data quality processes with speed and accuracy.

Here are key areas where analytics and AI can optimise data quality:

Automated Data Discovery and Classification

Using natural language processing and other AI techniques, organisations can automatically discover and classify information from multi-format data sources including unstructured “dark data” previously invisible. This illuminates all enterprise data assets for downstream quality analysis.

The benefits of AI technologies in this space also include the ability to use your own taxonomy without having to train the system when it changes. More so, AI technologies help you improve your taxonomy by making intelligent suggestions for improvement based on the data it discovers as your business evolves.

Data Cleansing and Transformation

Machine learning models can comb through data repositories to identify anomalies, remove redundancies, fill in missing information, normalise taxonomies, and structure data in a more organised manner.

Machine learning models can also be more closely supervised, trained and improved, so they can better adapt to your environment and needs.

Adaptive Data Quality Monitoring

Analytics provide comprehensive monitoring of data pipelines, data provenance, and quality metrics. AI models can detect drift, deviations or patterns of errors in data streams and trigger resolution actions automatically. This maintains reliable data quality over time.

Data Cataloguing

With machine learning models powering cataloguing, organisations can implement intelligent, dynamic metadata management. Smart data catalogues can ensure metadata consistency, maintaining accurate data assets.

By injecting analytics and AI across the entire information management lifecycle, organisations can establish continuous, automated cycles of data quality improvement and generate the highest quality data ready to supercharge a myriad of business initiatives.

The EncompaaS Advantage

Of course, the insights derived from advanced analytics are only as accurate and actionable as the underlying data quality that feeds those models – and that’s where EncompaaS comes in. The EncompaaS platform uses next-generation AI technologies to find, enrich, organise and de-risk structured, unstructured and semi-structured content anywhere in the enterprise. This enables organisations to enhance data quality by:

• Automatically identifying, classifying and extracting information from all data sources, including unstructured formats previously ignored
• Enriching data through the automatic application of metadata to make content more usable, manageable and accessible to permissioned users
• Classifying discovered data using your taxonomy, relevant perspectives and growing areas of interest or concern
• Continuously suggesting improvements to the taxonomy, based on the evolving nature of the content created by your organisation, in a supervised yet seamless manner
• Cleaning up data by removing duplicate or redundant records, and by creating a foundation of normalised data
• Continuously monitoring data by leveraging advanced analytics in easy to digest dashboards to fully understand where information is located

With an increasing volume and complexity of data within the enterprise, organisations can no longer rely solely on manual processes to meet increasing organisational demands.

To truly leverage and capture the value of enterprise data, organisations must look to analytics and machine learning, within intelligent information management platforms, to enhance business outcomes.

EncompaaS can orchestrate these technologies to extend their reach to your entire data corpus, even when it is stored in legacy systems, as well introduce compliant and responsible frameworks to use these technologies with a configurable level of supervision and governance that is right for you.

To learn more about how EncompaaS can help your organisation achieve data quality excellence through AI, contact us or book a demo.