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Improve Data Quality in the Small Enterprise
Better data quality today makes for better decisions tomorrow.
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Your Challenge
- IT and the business do not understand the root causes behind data quality issues when developing corrective initiatives.
- Small enterprises lack the resources that larger organizations possess to dedicate toward improving and maintaining high data quality.
Our Advice
Critical Insight
- Maintaining data quality requires a culture that views data quality as an ongoing, iterative process and not merely a one-off project.
- Building a data glossary is a low-cost, high-impact method of maintaining data quality in a centralized location that small enterprise IT shops can leverage. The data glossary serves as a solid stepping stone in the process of building a data-centric mindset amongst stakeholders and end users.
Impact and Result
- Info-Tech’s tailored small enterprise research provides a clear roadmap to help IT leaders better understand the underlying issues from which poor data quality frequently arises.
- The organization is then able to develop a set of short- and long-term initiatives to improve data quality.
Improve data quality in the small enterprise
Use Info-Tech’s approach to data quality to guide the small enterprise in improving data quality in both short- and long-term.
1. Determine business challenges
Understand the context behind data needs to pinpoint and prioritize the most important data quality issues.
2. Identify data quality issues
Perform a technical assessment and define the details around key data quality issues.
3. Create improvement strategies
Use Info-Tech’s recommendations to implement long-term data quality improvement strategies and communicate effectively to stakeholders.
Guided Implementations
This guided implementation is an eight call advisory process.
Guided Implementation #1 - Scoping call to understand the organization’s data quality concerns
Call #1 - Overview of Info-Tech’s applications practice and its services.
Call #2 - Review the blueprint’s structure, outputs, and content.
Call #3 - Determine the most pressing data quality issue.
Call #4 - Establish the desired data quality standard.
Guided Implementation #2 - Create short-term improvement initiatives
Call #1 - Review interview results.
Call #2 - Walk through the process of performing a technical assessment.
Call #3 - Walk through the data assessment scorecard.
Guided Implementation #3 - Review possible long-term improvement strategies
