Data Quality for Competitive Advantage
Modern enterprises rely on data to drive operational and strategic decisions, making it a critical asset for organisations looking to gain a competitive advantage. Poor quality data increases risk and costs time and money. According to Gartner research, the financial impact of poor data quality on organisations costs on average $9.7 million per year.
Organisations leave 97% of gathered data unused, leaving them unable to make informed decisions. As data volume grows, so does complexity, increasing the number of data management challenges. For organisations looking to leverage their data effectively, developing a data quality management plan is the first step in achieving accurate, complete, and consistent data.
Here are 5 points to consider for data quality within enterprises:
The DAMA Data Management Body of Knowledge states that experts believe that organisations spend between 10-30% of revenue handling data quality issues. Improving the accuracy and completeness of data can increase efficiency and reduces costs by minimising the need for rework and data correction.
Decisions are only as good as the data they are based upon! Companies with CEOs that make data-driven decisions are 77% more likely to succeed. Organisations that have high data quality can trust their data and use it to inform their business decisions, leading to better business outcomes and greater efficiency.
Enhanced Business Intelligence
Good quality data can enhance business intelligence, providing a clear and comprehensive understanding of the market. As a result, operations will be improved, and competitive advantages can be gained.
Better Customer Insights
By ensuring customer data is accurate and up-to-date, organisations can use this information to develop more targeted marketing campaigns and improve customer service by better understanding customers' needs, preferences, and behaviours.
Organisations must comply with regulations governing data storage, security, and processing. It is possible for organisations to ensure elements of compliance by keeping data accurate and up to date, such as marketing preferences.
It can be hard to measure the impact and true costs of poor data quality, with indirect costs such as reputational damage hard to gauge. Enterprises must be prepared to invest in their data quality infrastructure and data management solutions.
Preventative measures and effective data quality management must be embedded from the point of data capture.
High quality, reliable data underpins business decisions, instilling confidence, increasing efficiency, and driving improved decision making, all leading to competitive advantage within the marketplace.