Good data quality means that an organization's data is accurate, complete, consistent, timely, unique, and valid. Sound business decisions rely upon clean and consistent data sources that provide information about an organization's products, suppliers, financial details, and most importantly, their customers. The better the data, the more clearly it presents an accurate, consolidated view of the Customer, across systems, departments and line of business and the more likely that an organization will be in a position to meet or exceed it's goals.
Annik's Data Quality Capabilities
Annik works in harmony with trusted data sources [Dun & Bradstreet, Acxiom, InfoUSA, Experian, etc], data governance processes, our custom tools and skilled data analysts, and your business rules in order to resolve exceptions and improve the quality and effectiveness of data assets within your product, financial, supply chain, and customer information management systems.
Annik has developed data quality analytics solutions that enable you to profile and analyze your progress through customized Scorecards. These tools can be adapted and applied to independent data quality projects (or incorporated into enterprise Data Governance and MDM initiatives) Services provided by Annik in this area are :
Annik deploys its own Match Engine based on following Search criteria :
Is a centralized, enterprise-wide imperative that encompasses the people, processes and procedures required to create a single consistent, view of data owned by an Organization
Key Business Drivers
Data Governance initiatives improve data quality by assigning a team to develop and manage processes that ensure data's accuracy, accessibility, consistency, and completeness, among other metrics. It is imperative that this team consists of executive leadership, project management, line-of-business managers, and data stewards.
The team usually employs some form of methodology for tracking and improving enterprise data, such as Six SigmaTM, and tools for data mapping, profiling, cleansing and monitoring data. Many companies have numerous business applications and databases that might lack context-based integration.
Therefore, knowledge-workers don't always have access to the information they need to best do their jobs. When they do have the data, the data quality may be faulty due to lack of inconsistent quality standards and processes. By creating and enforcing a comprehensive data governance practice, these problems can be mitigated.
Annik's Data Governance Capabilities