experian-2020年全球数据管理研究报告（英文）-2020.3-19页_2mb

2020 Global Data Management Research Summary

Core Content

This document outlines the findings of a 2020 global data management research study conducted by Experian, in collaboration with Insight Avenue, and published in October 2019. The study surveyed over 1,100 professionals across six countries: the United States, the United Kingdom, Germany, France, Brazil, and Australia. The participants represented a wide range of industries, including IT, telecommunications, manufacturing, retail, financial services, healthcare, and public sector. The research focuses on the challenges and opportunities surrounding data management, particularly in the context of becoming a data-driven organization.

Main Themes and Key Findings

1. The Data-Driven Organization: A Transformation in Progress

• Data as a Strategic Asset: Data is increasingly viewed as a key competitive advantage, a source of insight, and a strategic financial asset.
• Distrust in Data: Despite the importance of data, many professionals do not trust it. On average, nearly one-third of customer and prospect data is suspected to be inaccurate.
• Impact of Inaccurate Data: Poor data quality leads to wasted resources, unreliable analytics, and negative impacts on customer experience and business outcomes.
• Lack of Trust in CRM/ERP Data: Only half of organizations consider their CRM or ERP data to be clean, limiting their ability to fully leverage it.
• Data Literacy as a Priority: Companies are increasingly recognizing the need for data literacy across all levels of the organization. It is seen as a core competency for employees in the next five years.

2. Data Debt: A Growing Challenge

• Definition of Data Debt: Data debt is likened to technical debt and refers to the accumulated cost of sub-optimal data governance and management practices.
• Prevalence of Data Debt: 78% of organizations believe they have a data debt problem, yet only 24% have a strategy in place to address it.
• Barriers to Addressing Data Debt:
  • Lack of Understanding: Many stakeholders do not fully grasp the impact of poor data.
  • Legacy Practices: Data is often treated as an IT function rather than a business-wide asset.
  • Difficulty in Prioritization: 59% of respondents say it is hard to know where to start tackling data debt.
• Impact on Business Outcomes:
  • 35% cannot see ROI from data management initiatives.
  • 33% cannot extract value from new technology investments.
• Steps to Address Data Debt:
  • Understand the full scope of the data debt problem.
  • Invest in data quality and governance.
  • Break down silos and encourage cross-functional collaboration.

3. The Skills Gap: A Major Hurdle

• Rising Demand for Data Roles: There is a growing demand for specialized data roles such as data analysts, data engineers, and data scientists.
• Skills Shortage: The number of available qualified candidates is less than the number of open roles, creating a talent shortage.
• Data Literacy as a Solution: Many organizations are turning to data literacy to bridge the gap. 84% of organizations see data literacy as a core competency for all employees in the next five years.
• Role of the CDO: The Chief Data Officer (CDO) is critical in driving data literacy and enabling data-driven initiatives. Over half of organizations have a CDO, and many are planning to appoint one in the next 12 months.
• CDO Impact:
  • Companies with a CDO are more likely to have effective CRM systems.
  • They are better positioned to monetize data assets and implement sophisticated data management practices.
  • CDOs are seen as a critical part of the leadership team.

Key Recommendations

• Build Trust in Data: Organizations should focus on data initiatives that demonstrate tangible business outcomes to build trust.
• Prioritize Quick Wins: Quick, measurable improvements in data quality can serve as a catalyst for broader change.
• Foster a Data-Driven Culture: Embed data literacy and ownership across the entire organization to support long-term transformation.
• Invest in Data Quality: Address data debt by investing in quality standards, checks, and processes.
• Break Down Silos: Encourage cross-functional teams and collaboration to improve data governance and management.
• Develop a Strategy: Organizations must create a clear strategy to tackle data debt and invest in the right skills and tools.

Conclusion

The research highlights that while data is widely recognized as a strategic asset, organizations are still struggling with data quality, trust, and skills. The path to becoming a truly data-driven organization requires addressing these issues through a combination of improved data management practices, cultural change, and investment in both people and technology. The CDO plays a pivotal role in driving this transformation, but their success depends on collaboration, data literacy, and a clear understanding of the challenges and opportunities in data management.