Many factors can contribute to the success or failure of business and data analytics programs -- some are data...
management challenges, while others can be traced to political or cultural issues within the organization.
Data management challenges may include:
- Lack of an enterprise data management program based on a complete enterprise data management framework. The omission of an enterprise data management framework, such as the DAMA International Data Management Body of Knowledge wheel, is a sign that the organization doesn't understand data management as a coordinated set of disciplines that contribute to the health and success of the management and use of data throughout the enterprise. Without such a framework, data used by an analytics program in such an organization may not be reliable, valid, complete, well-defined, from trusted sources, etc.
- Lack of an enterprise data governance program. As part of the enterprise data management framework implementation, the organization needs to have an enterprise data governance program, supporting operations, as well as analytics. Data governance ensures that the organization's data is managed with policies, practices and standards that are based on best practices found in the industry and deployed across the enterprise. Without an enterprise data governance program, the analytics data will be fragmented.
- Lack of an enterprise metadata management program as a companion to data governance. Along with data governance, metadata management at the enterprise level ensures that the data used for analytics follows established standards for format, usage, data integration, data lineage (sources and targets), etc. Without proper metadata management, the data analytics program will not know which format is the right one for its purpose (feet or meters, rounded or decimal, etc.)
Beyond data management challenges, the following political and cultural factors may stymie analytics programs:
- Lack of executive support for data management and analytics. Many organizations start a data management program to support either operations or analytics, but they do not engage sufficient lasting support to ensure continuation. A struggling or dormant data management (or data governance, metadata management, data quality, etc.) program will lead to eventual poor analytical results and a lack of confidence in the analytics program.
- Lack of education and continued training for employees. Data management is a complex discipline, and excellent education and ongoing training are necessary for all professionals who engage in the usage of data and its management.
In the final analysis, analytics programs can fail for many reasons; some of which can be attributed to an organization's lack of attention to data management challenges. This situation can be corrected, and every organization's analytics efforts can succeed, based on effective enterprise data management.
Dig Deeper on Big data analytics
Related Q&A from Anne Marie Smith, Ph.D.
Consultant Anne Marie Smith details five challenges that an organization may face in applying data governance policies to data lakes and offers ... Continue Reading
An enterprise data catalog can help data stewards and other users in an organization manage metadata and explore data assets. Here are 10 key steps ... Continue Reading
Defining a data strategy can help focus an organization's data management initiatives -- but it isn't the same as data governance. Expert Anne Marie ... Continue Reading