Navigate the complex landscape of data quality management with insights on what to look for in a data quality management tool, ensuring optimal performance and efficiency.

Gartner’s release of its updated 2013 Magic Quadrant for Data Quality has spawned a number of commentaries.

The report highlights several significant trends, including the rising popularity of data quality initiatives for transactional, financial, location, and product data. However, there has been a slight drop in customer data initiatives. Nonetheless, it’s worth noting that 78 per cent of data quality projects still focus on customer data.

Data quality requires a blend of technology and data quality approach

This year, for the first time, Gartner has recognised that vendors with a core data quality focus are able to provide much more than just data quality tools to assist their clients.

One encouraging sign is the increasing number of data quality initiatives driven primarily by data governance efforts. The report states that stronger information governance competency is essential due to the analytics involved, often utilizing big data techniques and sources. If the data cannot be trusted, its value diminishes, potentially affecting the organization’s efficiency and ability to execute critical transactions accurately.

Eight questions to evaluate a data quality vendor

Loraine Lawson asks eight important questions pertaining to data quality platform choice.

As data quality becomes more integrated into business operations, the requirements for data quality tools also evolve. Lorraine’s questions go beyond the standard features checklists common to most data quality RFP’s and dig into the deeper implementation and total cost implications that differentiate the enterprise players from the pack.

Before investing in data quality tools, consider the following capabilities and issues:

1. Ease of Management and Understanding: Can analysts and other business users easily manage and comprehend the tools?
2. Integration into Workflows: Can the tools be seamlessly embedded into business process workflows or other technology-enabled programs like MDM and analytics?
3. Integration and Architecture: To what extent are data quality functions integrated into a single architecture and product?
4. Deployment Flexibility: Does the tool support on-premise, hosted/SaaS, or cloud-based deployments, or all three?
5. Pricing Model: What is the pricing model for the data quality tool?
6. Deployment Time: How long does it take to deploy the tool?
7. Total Cost of Ownership: What is the overall cost of owning and maintaining the tool, especially considering the current economic climate?
8. Vendor Support and Service Capabilities: What type of support and service capabilities does the vendor offer?

While data quality is primarily a discipline rather than a technology solution, as emphasized by Gartner, the role of technology is crucial to its success. Data quality tools provide automation for activities that would otherwise be challenging or impossible given the vast volumes of data and the complexity of modern enterprise technology landscapes (multiple platforms, storage mechanisms, and diverse formats and semantics).

Investing in the right data quality tools can significantly enhance your organization’s data analysis capabilities, improve decision-making, and maximize the value derived from your data.

Navigate the trade-offs between ease of implementation and ease of use in data quality tools with valuable perspectives from ease of implementation vs ease of use in Data Quality tools, optimizing your selection process.

Embrace the potential of AI for data quality enhancement by exploring insights from Using AI for data quality, unlocking new possibilities for data-driven decision-making and innovation.