The sole function of data is to support the business activities. Each system requires a different view and characteristics for billing, customer management, financial reporting, or for any other purpose. Data quality can only be defined in order to achieve its intended purpose. The concept of expediency means understanding the concept of data quality, and knowing which data is available is not enough by itself; the purpose of this data must also be made clear. For many tasks, data is collected for a single purpose, but is usually used for different ones. As a result, data quality problem forms a complex and constantly evolving challenge for businesses.
The first step in any attempt for data quality is to define quality itself. The quality required must be identified prior to any project. Typical factors are availability, accuracy, integrity, consistency, completeness, validity, timeliness and accessibility.
Data quality should be viewed as a continuous process. Data potentially loses its validity from the moment it is collected. A high-quality flow of data can only be acquired with continuous measurement, evaluations and refinement.
Deloitte Data Management team uses pre-determined processes and industry-leading tools to solve data quality problems. Some of these tools can be named as evaluating the suitability of business units and locations, monitoring and ensuring adherence to data standards, identifying incomplete and inaccurate data, developing processes to correct and enhance the data and enhancing the single customer and product views on various business structures.
Deloitte data management approach combines three dimensions to create value to the business. Although technology implementation consultation and assurance is based on years of experience, a successful data management actually includes more than the simple integration of technology.
- Data Risk Management- Ensuring data assurance through research, analysis, negotiation and evaluation
- Data Administration- Determining the correct people and policies for keeping and regulating company data in order to create a business structure managed by data
- Data Management Technology- Selecting, installing, integrating and implementing the technology required for efficient data management
Despite that the focus of any data management initiative might be identified with a single dimension, success depends on the integration of all three. The interaction, vital to a successful initiative, is the center of these dimensions. A successful data management requires an understanding of risk, management and technology.
