Aligning Data with Business Objectives – Syniti’s CTAs
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⇨ The reasons that contribute to poor data quality, or the gap between perceived and actual data usefulness, are related to data governance and monitoring.
⇨ Improvements in data management are not necessarily dependent on investing in more technology.
⇨ Making better use of data quality solutions could play a critical role in improving data management as they can help to identify, comprehend, and rectify the issues causing the gap between trusted and actionable data.
Data management has become a C-suite topic. In recent research by Syniti and HFS, the C- suite sets and owns the data objectives for more than 90% of respondents. The research also reveals that the chief executive officer sets the data objectives for 46% of companies, while the chief data officer and the chief digital officer combined set objectives for about 33% of companies. This increased attention from the executive leadership has led to greater investments in data management systems.
But despite these statistics, the research suggests a disconnect between executives’ perceptions of trust in data and the operational realities. Although a vast majority (80%) of business leaders expressed confidence in their organization’s data, there seems to be a discrepancy regarding applicability. When asked to estimate how much of their data was actually usable or digestible, over half of the respondents cited a figure of 60% or lower—sometimes considerably lower. The need for better data quality is further underscored by the fact that over 95% of executives believe that doubling the quality of their data would enhance their company’s competitiveness, stimulate innovation, and facilitate quicker decision-making, highlighting the apparent gap between data trust and utility.
But the reasons that contribute to poor data quality, or the gap between perceived and actual data usefulness, are related to data governance and monitoring. This suggests that improvements in data management are not necessarily dependent on investing in more technology or hiring additional staff. A lack of proper governance is a significant problem, and an in-depth understanding of the pros and cons of the available solutions can lead to more effective data management.
Thus, making better use of data quality solutions could play a critical role in improving data management. These solutions, with their inherent features such as monitoring, parsing, metadata management, data curation, and enrichment, can help identify, comprehend, and rectify the issues causing the gap between trusted and actionable data.
Based on Syniti and HFS research, organizations should take four key actions to improve their data management strategies’ effectiveness.
Data-centric approach: Organizations should prioritize the strategic importance of data by adopting a workflow-centric perspective and integrating IT and business goals with a focus on business results rather than technological capabilities.
Data management and quality tools: Many organizations invest in data management and quality tools but are unclear on their benefits and aligning them with data policies. Maximizing value from these tools means utilizing these investments effectively to prevent them from becoming obsolete.
Change Management: To increase the proportion of valuable and usable data, organizations should implement effective change management practices by concentrating on governance and monitoring to gain a thorough understanding of the data cycle from start to finish.
Business Outcomes: Organizations should emphasize on a cultural shift as data management is considered as a technological issue in most companies. It is essential for organizations to clearly define and communicate data management goals.