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Enterprise Data Management

Need an enterprise data management checklist for an information management strategy and want some practical time saving suggestions?

What are data storage analysis objectives?Enterprise Data Management

The objective of the data storage "as is" analysis is to complete an assessment of data management situation to determine what changes are needed for successful information management.

A significant component of information management is data management, which includes the structure, processing and delivery of information.

This frequently involves extracting data from operational systems, moving it into data warehouse structures, reorganizing and structuring the data for analysis purposes, and moving it into reporting structures called data marts. 

We need to understand what is currently available so that we can make informed recommendations for needed changes. The more information we gather now about system architecture means the more that we will know as we get into detailed architecture and design.

Enterprise data management checklist

Examine what repositories are required to support data model storage.
Review database management policy
Assess how data is currently stored e.g.
  • Legacy Systems;
  • Customer Relationship Management Systems;
  • Enterprise Resource Planning Systems;
  • Online Transaction Processing Systems (OLTP);
  • Operational Data Stores (ODS);
  • Data Warehouse(s); and
  • Data Marts and other reporting/analytical environments
Be sure to address any potential data movement issues between these systems and potential data warehouse solutions
 
Determine what on-line transaction processing systems (OLTP), are used. Are they legacy systems, CRM or ERP systems, which process transactions on a daily basis? These applications typically store sufficient data for transaction processing but limited history data.
 Are operational data store (ODS) utilized. These are similar to OLTP systems with the exception that there is usually a bit more history than normally found in source systems.

Some RDBMS systems provide data replication utilities and the ODS can serve two purposes:
    • They can be available for reporting; and
    • They can also be used as part of a disaster recovery process
Is a Data Warehouse part of the architecture? These contain data gathered from several source systems. The data is organized and integrated for efficient storage.  Data warehouses usually hold a significant amount of history.
Some relational database management systems and hardware are sufficiently powerful that all data can be stored in a data warehouse and reporting can occur here with no degradation for performance.
Are data marts part of the architecture? These are frequently used for reporting purposes. These data marts might be specific for sales analysis, shop floor analysis, human resources analysis and other decision support purposes.
Data marts are typically fed from a data warehouse but may also be created directly from the OLTP source systems.

Data is extracted from the data warehouse, or OLTP system, and moved into these data marts so that business intelligence reporting tools can then access the data mart for analytical purposes.

Assess the status of storage hardware e.g.
  • Server hardware--Are we using symmetric multi-processing, cluster technology or massively parallel processing technology? Each has it’s own design considerations.
  • Does the network hardware have sufficient capacity to move the anticipated data volume required to support information management?
  • Is client hardware sufficient to handle planned business intelligence applications?
  • What client software and reporting/analysis tools are available? Is staff sufficiently trained?
  • Does the relational database management software (RDBMS) have the capacity for large-scale operations?
  • What is the technology strategy?
Include an assessment of enterprise data management storage software  e.g.
  • Data storage;
  • Storage systems;
  • Data base management;
  • Data management software;
  • Data management tools;
  • DBMS;
  • Database architecture;
  • Database servers;
  • Data strategy; and
  • Data systems.
Look at what repositories are required to support extract, transform and load (ETL) software.
Are the environments appropriate for information management?

Assess security--Include a review of:
  • Data backup storage;
  • Offsite data storage; and
  • Data recovery procedures.
Determine what repositories are required to support metadata.
Are the environments appropriate for information management?
Are access and reporting capabilities sufficient for information management?
Determine if there any special repositories or requirements to store "dirty" data until it is "cleansed".
Review the project management methodology to determine if it includes provision for creating and testing data storage structures at in the appropriate project phases
Review enterprise information management (EIM) to determine the status of roles and responsibilities.
Identify the level of staff training and any additional training that might be required.
Identify any additional skills required to manage information management data storage.
Identify the existing business intelligence reporting/analytical tools.
Determine what types of business intelligence software software are available in the organization.
Determine the capability of business intelligence software and the skills and training that might be needed to use it effectively.
Review enterprise data warehouse to determine if data warehousing is included in overall enterprise data management plan. This should include a review of:
  • Data warehouse systems;
  • Data warehouse architecture;
  • Enterprise data warehouse solutions; and
  • Enterprise architecture tools. 
Review enterprise business intelligence solutions. This should include a review of:
  • Data warehouse;
  • Operational data store;
  • Data mart; 
  • Database design; and
  • ERP and business intelligence.
How do we gather enterprise data management information?

The IT department can answer most of the system architecture components questions. I find that questionnaires are the best way to get this information.
Summary...

Enterprise data management is the foundation for information management. In other words, it’s all about providing the information needed to support the business functions.

There are a lot moving components to make this happen successfully and it is critical that we understand what is available so that we can make informed recommendations for change if needed

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