Data & Analytics Management

Analytics that operate in real-time require designs that can be automated and data that is ready to use. Analytics driving real-time actions do not allow time for reworking or rethinking the action to take. To operate successfully in this environment requires business-led management of data and analytics. Use of technologies developed for managing data on Data Lakes enhance Governance to better manage master data, data quality, rules, and more as the Data Lake develops.

Business data management is the information management process that makes sure business data is trustworthy by establishing the meaning and rules for each element of data. There are four key areas addressed by business data management:

  • Data Governance: This is the information management process for ensuring the trustworthiness of business data and it is important to not limit governance to resolving data problems but to extend it to defining the meaning of business data elements, metrics, and KPIs, establishing the rules that make each data element trustworthy and able to support all business data use scenarios, and proactively enforcing compliance with data rules throughout the business;
  • Master Data Management: The official data representation of:
    • Real entities which are part of the business, such as Customers, Suppliers, Products, Orders, Employees, and so forth;
    • Reference data, the data representation of the organization, geography, classification, or other perspectives of the business that need to be used consistently throughout the business is called master data;
  • Data Quality Management – every business has problems with data quality because of inconsistent data rules in applications, purchased software, and individual Access databases and Excel workbooks, which need to be addressed in order to ensure trustworthy data;
  • Metadata Management – metadata is data about data and includes:
    • Business metadata, such as data definitions, data rules, security classifications, and so forth;
    • Operational metadata, such as when data was extracted, its source system of record, the transformations and integrations that occurred, and other factors useful to understand when using data;
    • Technical metadata, while not a management concern, describes the operation of the technologies used to acquire, process, and store data and is needed for resolving questions about failures, performance, and the operation of these technologies.