5 Most Common Data Quality Issues
As described in our previous article on Data Fabric Architecture should focus on Data Quality, we talked briefly about what data fabric entailed. This article will dwell deeper on what it
With a Data Quality First approach and higher degree of automation, we help both business and technical stakeholders to improve ROI and enrich customer experiences by discovering trustable data and business insights in minutes.
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Using DQLabs smart connectors that come out of the box, you can connect to an unlimited amount of data sources in any form, shape and any location. No need to download or install any drivers.
With no code, you can immediately connect to any source necessary with multiple options such as scan, pull, discover metadata, lineage, query based or cross join views or even partitioning to process datasets at high performance and throughput for data quality analysis.
Data quality measurement without semantics or understanding of business context of the data does not help drive better business practices. With DQLabs’ Data Sense™ capabilities, you can automatically enrich semantics for any type of data with or without metadata information.
Now you can not only automate the process of discovering, inventorying, profiling and tagging using a simplified form of metadata management but also auto discover rules as well assensitivity classification in alignment with your organizational landscape.
The traditional manual process of creating business glossaries, mapping assets, identifying business rules, and continuously going through the process of upkeeping has failed.
With DQLabs out of the box capabilities, you can perform all forms of data quality tasks automated efficiently to measure, score and identify bad data using auto-discovered rules, patterns, insights, fraud, missing values, and correlations across data source, data set, attribute, department/function or organizational domain level.
DQ monitoring or data observability without semantics or business context results in a high level of false positive alerts, hampering rather than helping your organization. DQLabs’ process of semantic discovery allows us to focus on refining business context across all your data without any handwritten rules or manual efforts.
Monitor with benchmarking, trend forecasting, and actionable alerts to cover a wide variety of DataOps and Data Observability use cases such as data pipeline monitoring, source to target checks, schema or data level deviations or abnormalities.
Measurement without remediation does not help to improve data quality. Using DQLabs you can not only identify the bad records as measured by the DQScore™ but our platform also allows you to remediate and fix any kind of data quality issues within your organization.
DQLabs remediates your data quality issues by cleaning, enriching, and merging good records using ML-based smart curation libraries, modules and it identifies the optimal data preprocessing strategies to make this process faster while it also automates data curation with controls on data quality thresholds.
Using DQLabs both business and technical stakeholders can analyze and make sense of bad data to their own ends. All stakeholders can better understand their own data and achieve their functional goals faster than ever before.
The platform is built to empower users to derive usable insights and to recommend the next best actions both strategically and tactically. With the platform, you can generate all the metrics you need and measure across any of the data dimensions used in your organization.
Whether you're just beginning to explore your options or you're ready for data quality solution, everything you are looking for is right here. Just invest your quality time and see how DQLabs is helping global organizations by empowering them with reliable and high-quality accurate data.
BLOGS
As described in our previous article on Data Fabric Architecture should focus on Data Quality, we talked briefly about what data fabric entailed. This article will dwell deeper on what it
CASE STUDIES
The City is one of the top-ranked metropolitan areas in the United States. The City’s regional economy is versatile and spread across various verticals, with a robust emphasis on life sciences, agribusiness education and research, logistics, manufacturing, aerospace, and professional services.
Conference
Modern-day systems are transforming into complex, open-source, cloud-native services running on various environments and being developed/deployed at lightning speed by distributed teams. When working on these systems, identifying a broken link in the chain can be near impossible. Everything fails at one point or another, whether due to code bugs, infrastructure overload, or changes in end-user behavior or market driven factors or errors in data collection. This has led to the rise of DataOps with a focus on changing the organizational speed and trust in delivering data pipelines and the related artifacts by co-creating “decision quality” data with the consumers. This development has led to the idea of observability that includes monitoring, tracking, and triaging incidents to prevent downtime of the systems and around several factors such as freshness, distribution, volume, schema, lineage.
We understood everyone preaches Data Quality however don’t have an out of the box platform for an end-to-end data quality across both business and technical users. Bad data will never go away but understanding and fixing where possible using automation is much more important with growth in data.
The efforts spent towards traditional rules based DQ measurement haven’t scaled and newer thought process such as observability without the lack of semantics or business context results in more false positives than being useful.
