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What You'll Learn
You think good data quality is a nice-to-have concept for your organization. It’s more than just that. Data quality drives business outcomes. As a modern business, you depend on data to identify new markets, optimize customer experiences, and drive operational efficiency. But the moment your data is inaccurate or inconsistent, it creates risk instead of value. According to Gartner, poor data quality costs companies an average of $12.9 million annually. It’s important that organizations invest in data quality management to enable confident decision-making and maintaining trust across systems, processes, and customer interactions. This blog will provide you with an end-to-end data quality management strategy for your organization. Read on.
Before we discuss the data quality management framework, please note that ignoring data quality can undermine entire business functions.
What happens when you work with bad data
- Analytics and AI models can falter when developed on inconsistent data.
- Regulatory compliance becomes risky when data cannot be traced or validated.
- Duplicate or missing data records can negatively impact your marketing efforts.
- Outdated insights can cause your sales team to underperform.
You can also read: How to Improve Data Quality. 5 Best Tips
What is data quality management?
We could define data quality as the end-to-end practice of ensuring that your enterprise data is accurate, complete, consistent, valid, timely, and relevant. These are the most common data quality domains. Data quality management involves the people, processes, rules, and technologies that are used to evaluate, correct, and continuously monitor data quality across your systems and workflows.
The goal of data quality management is simple – to enable decision makers and systems to trust the data they use!
What are the popular types of data quality frameworks?
The frameworks we are going to talk about are particularly well-suited for enterprises operating under regulatory scrutiny or investing in AI/ML capabilities where reliable data is non-negotiable. If you want to implement successful data quality management for your organization, you must follow the structured frameworks below:
- DAMA-DMBOK (Data Management Body of Knowledge)
It is a foundational model in enterprise data practices. DAMA-DMBOK defines data quality management as one of the core tenets of modern data management. It covers dimensions like accuracy, completeness, and consistency, and guides how organizations should assign stewardship, set data quality standards, and scale data governance.
- Six Sigma for Data Quality
Six Sigma methodologies are known for their focus on reducing defects and improving process efficiency. They can be directly applied to data. Using DMAIC (Define, Measure, Analyze, Improve, Control), your business teams can reduce error rates in data pipelines and improve data quality.
- Total Data Quality Management (TDQM)
Developed in academic-industry settings, TDQM emphasizes aligning data quality with business goals. These goals could be improving marketing performance, reducing fraud, or streamlining customer service. It encourages the use of metrics to quantify data quality ROI.
How to enable high-impact data quality management: Best strategies
#1 Start by defining data governance & ownership
You do this by assigning data stewards and business owners to take responsibility for specific data domains. Establish data quality policies and data stewardship playbooks to manage expectations across different business departments. LumenData can help develop governance frameworks based on the DAMA-DMBOK model and tailored to specific business objectives.
#2 Consolidate. Consolidate. And consolidate your data sources
For you to enable high-impact data quality management, it’s important to integrate data from siloed systems like CRMs, ERPs, and marketing tools into a unified architecture. You can host them on leading platforms like Snowflake or Databricks. They are scalable cloud data platforms. Also, consider applying Master Data Management (MDM) via modern data platforms platforms like Informatica or Reltio to create golden records and reduce data duplication.
#3 Don’t forget to profile and audit your data
Ask your team to run diagnostic scans. This uncovers null values, outdated fields, anomalies, and format inconsistencies. LumenData can help leverage Informatica IDQ and custom Databricks notebooks to continuously profile enterprise datasets and surface hidden data issues.
#4 Cleanse, Standardize, and Automate
The first thing here is to normalize data formatting (e.g., date structures, country names). Ask data teams to apply fuzzy logic to detect and merge duplicates. Your team can enable data validation rules at ingestion points using ETL and API-based controls.
#5 Leverage top data quality tools
Consider using data quality tools like Informatica Data Quality, Oracle Enterprise Data Quality, Collibra Data Quality & Observability, and more. These data quality tools will help you leverage data as a true asset. More details on these tools here. (link to data quality tools blog)
#6 Enrich your data. Refresh it for relevance
You could use external enrichment sources to add missing firmographic or behavioral data. Set automated data refresh schedules for core entities like customers, vendors, and assets to reduce decay. By this we mean, prioritize enrichment for high-impact datasets like customer segmentation fields, product master data, and regulatory attributes. At LumenData, our expert consultants can help you design data enrichment pipelines within platforms like Snowflake. We help ensure that your datasets stay fresh, compliant, and ready for downstream consumption.
#7 Monitor! Use dashboards & alerts
Track quality in real-time through custom dashboards using Tableau, Power BI, or native Informatica dashboards. LumenData’s expert technical consultants and architects can help create custom monitoring dashboards and notification workflows tied directly to your data quality rules.
How can LumenData help with data quality management?
If you’re looking for premium data quality services in the U.S., you are at the right place! Choose LumenData for accelerated data quality management. Implement a data quality management plan that can drive real results. We have years of expertise and partnerships with leading modern data quality and management platforms such as Informatica, Snowflake, Databricks, and many more. You can find all our partners here. We have led several data quality projects and provide on-demand & scheduled support for data quality management and other modern data initiatives. To get started with your data quality project and learn how we can build your success story, contact us today.
About LumenData
LumenData is a leading provider of Enterprise Data Management, Cloud and Analytics solutions and helps businesses handle data silos, discover their potential, and prepare for end-to-end digital transformation. Founded in 2008, the company is headquartered in Santa Clara, California, with locations in India.
With 150+ Technical and Functional Consultants, LumenData forms strong client partnerships to drive high-quality outcomes. Their work across multiple industries and with prestigious clients like Versant Health, Boston Consulting Group, FDA, Department of Labor, Kroger, Nissan, Autodesk, Bayer, Bausch & Lomb, Citibank, Credit Suisse, Cummins, Gilead, HP, Nintendo, PC Connection, Starbucks, University of Colorado, Weight Watchers, KAO, HealthEdge, Amylyx, Brinks, Clara Analytics, and Royal Caribbean Group, speaks to their capabilities.
For media inquiries, please contact: marketing@lumendata.com.
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