Data Quality Approach

Approach

Traditional Data Management practices have failed.

Organizations have spent extensive time and money on disciplines in the realm of Data Governance, Data Cataloging, and Metadata yet these efforts have not scaled or met expectations without a focused approach towards Data Quality.

Current trends show that data is generated faster than data can be consumed and, in this climate, as a business you need a change in thinking from traditional data management practices towards a “Data Quality First” approach.

What is “Data Quality First” approach?

As organizations grow, you must be aware of where your internal practices land within the data maturity life cycle as this can drastically enhance or hamper the overall growth of your organization. DQLabs defines this data maturity life cycle in four stages:

Data Quality First Approach
Beginner

No data management practices implemented and more people-dependent processes using offline tools and methodologies

Low

Have used or tried individual or departmental efforts either using custom solutions or off the shelf solutions with no centralized focus

Medium

Have implemented a Data Quality, Data Catalog or Data Governance solution and are engaged in an effort towards centralized management practices, however, they are still struggling to streamline processes and have not seen real ROI.

High

Have a centralized data governance team and implemented technology and processes for Data Governance, Data Catalog, Data Lineage, Data Quality, DataOps, and Data Science but many are still struggling to have a modern collaborative data workspace with a focus on Data Quality.

Here’s the bad news - irrespective of which category you belong to, many organizations have no understanding of what percentage of bad data exists. This is because most of the efforts taken towards Data Quality are in conjunction with other disciplines of Data Governance, Data Catalog, or Data Lineage but there is not a primary focus or an actionable effort towards Data Quality.

That is why we preach a Data Quality first approach – an approach that focuses on understanding data from a business context via automated processes like semantic discovery and classification. This is further fueled by automation using proven machine learning technology that helps organizations measure, monitor, and remediate data quality issues in a more practical way and derive immediate value. If you are in the high maturity cycle, the good news is that a Data Quality First approach still takes into consideration your current learnings of business glossaries, governance practices and seamlessly integrates to automate an actionable data quality process.

Using this approach, you can benefit from trustable, actionable data and answer

  • What data is important and what is not?
  • What data needs to be improved or remediated?
  • What data can be used in reporting and analytics?
  • What data pipeline changes are needed to address schema/data deviation or drift?
  • What models can be built to enrich customer experiences?
  • Can I trust this report I am signing off on and am I as compliant as I think I am?

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Semantic Discovery for Data Quality Management

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Semantic Discovery for Data Quality Management

Fundamentals of Semantic Discovery

A data-driven business gains no value from its data which lacks a clear meaning and context. Such businesses will, therefore, constantly try to make sense of their massive data. How? Some businesses have teams of business analysts, data specialists, and other personnel employed to manually review, analyze, and classify all available data. In large businesses, it becomes difficult to do this manually, hence the need for automation. They are using ML algorithms to automate the process of analyzing and classifying massive datasets through self-learning capabilities. This automation has been proven to save up to 90% of project time.

What is Semantic Discovery?

Semantic Discovery is the approach to profiling data based on its semantic categories. Semantic Discovery supports the possibilities of exploring semantic categories of data in question and querying complex semantic relationships in datasets to create tabular analyses which have indicators and patterns which may be pre-defined.

Why Semantic Discovery?

Semantic Discovery is a process that helps businesses to automatically derive business meaning from data to enable understanding and automating business processes. With no clear meaning and context of data, the data may of be minimal value to a business, especially the data-driven businesses.

With Semantic Discovery, you can;

  • Explore semantic categories and query complex semantic relationships in the data to be analyzed.
  • Scan through data, analyze the characteristics and values of the data,
  • Create table analyses preconfigured with indicators and patterns that best suit your data.
  • Compare your data against other fields with an aim to propose semantic meaning and relationships with other available datasets.
  • Semantic Discovery for a data-driven company enables further automation.
  • Automatically generate data quality rules for a given dataset.
  • Provide the basis for protecting personal data to enable self-service data ingestion.

How does DQLabs Semantic discovery work?

Data quality measurement without meaning, semantics, or understanding of the business context of the data does not help get better business practices. With DQLabs’ Data Sense™ capabilities, a business can automatically enrich semantics for any type of data, whether it has metadata information or not. As a result, a business can automate the process of discovering, inventorying, profiling and tagging using a simplified form of metadata management and auto-discover rules and sensitivity classification in alignment with the business landscape.

With semantic identification and extensive integration into data catalog or data governance systems, you can derive end-to-end views of your data assets for the purposes of governance, privacy, compliance, and data quality. This will allow the data stewards to search and discover metadata as well as understanding the data quality associated with each attribute.

What are DQLabs.ai’s Semantics Discovery Features?

DQLabs’ Semantics Discovery will be of great help for any type of data source. It has built-in support and integration for simplified metadata management and automates the process of discovering, inventorying, profiling, and tagging data. Some of the features include;

Auto Discover Semantics

To help you discover and extract semantics from various enterprise data warehouses, operational databases, enterprise applications, cloud data stores, and nonrelational data stores with the help of just a few clicks using out-of-the-box connectors.

Automatic Sensitivity Classification

This will help you configure at ease your own sensitivity levels per your data governance programs and automatically identify the sensitivity footprint and classification at each attribute level.

Identify True Data Type

This is a feature that will help to ignore formatting, locale, and culture and identify the true data type at the attribute level to find relevant data quality rules.

Auto Discover Relevant Rules

With enriched semantics and business context for each attribute, let the platform discover all relevant data quality measurement rules for you.

Auto Detect Necessary Remediations

Measurement without remediation does not help a business to improve data quality. With enriched metadata and semantics, now you can enjoy remediation libraries that can perform smart curation at every attribute or dataset level.

Search and Discover with Relevance

Perform semantic searches across datasets and find the most relevant datasets by various metrics such as data quality score, drift level, sensitivity classification, etc., all within one platform.

Auto-tagging and Classification

Includes classification per the most up-to-date data privacy and security compliance regulations — such as the EU General Data Protection Regulation (GDPR) and California Consumer Privacy Act (CCPA).

How will your company benefit from DQLabs.ai’s Semantics Discovery?

Leveraging DQLabs’ Semantics Discovery will help you to scan through your data, analyze the characteristics as well as values, compare the data against other fields, and eventually propose semantic meaning and relationships with other datasets.

DQLabs.ai Semantic Discovery capabilities can be purposed for any company’s needs and data and will work in different languages and parameters resulting in metadata that will allow further automation.

Interested in a platform demo? Signup now.

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