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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.

Research & Tools

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

Call #1 - Review long-term data quality improvement strategies.