the cognistx blog

Are tech blockers getting in the way of AI innovation at your organization?

April 20, 2023
Romy Dhiman

Prioritizing data quality can help organizations in multiple ways. It ensures reliable data for informed decision-making, reduces the time and effort required to work with data, enhances customer experience, avoids penalties and provides a competitive edge.

But there can be blockers at organizations for investing in data quality. Some of the common ones are:

1.   Lack of awareness: Many organizations may not fully understand the importance of data quality and how it can impact their operations and decision-making processes. This can make it difficult to convince decision-makers to invest in data quality initiatives.

2.   Limited budget: Investing in data quality initiatives can be expensive, and some organizations may not have the budget to allocate to these efforts. This can be especially true for smaller organizations or those in industries with tight profit margins.

3.   Lack of skilled resources: Implementing and maintaining data quality initiatives often require specialized skills and expertise. If an organization lacks employees with these skills, it can be challenging to execute data quality efforts effectively.

4.   Data silos: In some organizations, data is fragmented and stored in different systems, making it hard to ensure consistent quality across all data sources. This can be a significant blocker for data quality initiatives, as it can be time-consuming and expensive to consolidate and clean data from multiple sources.

5.   Resistance to change: Data quality initiatives often require changes to existing processes and workflows, which can be met with resistance from employees who are accustomed to doing things a certain way. Overcoming this resistance can be arduous, especially if those employees do not fully understand the benefits of the changes.

6.   Lack of metrics: It can be challenging to quantify the ROI of data quality initiatives and therefore to justify the investment to decision-makers. Without clear metrics, it can be difficult to determine whether the investment in data quality initiatives has been successful.

Addressing these blockers may require a combination of education, resources and culture change. Organizations that prioritize data quality can gain a competitive advantage by making more informed decisions and delivering better customer experiences.

Ready to learn more?

Contact Uxue Zurutuza,, to learn more about how the Data Quality Engine and to set up a demo.

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