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Pervasive Business intelligence is an architecture or frameworkfor working with Business Intelligence. It combines the practices of Enterprise Architecture with Service Oriented Architecture and Active Data Warehousing to provide enterprises with a more holistic approach to Business Intelligence. The aim is to facilitate tactical decisions by establishing a data warehouse which stores both historical data and real time data that is available instantly and accessible 24/7/365. It could be valuable to share access to the data warehouse with various stakeholders, clients and suppliers as they might gain and provide insight. It is however important to note that the goal with Pervasive Business Intelligence is not only to provide near realtime data. The goal is to reduce lag or latency between events and the subsequent action[1].

Active Data Warehousing[edit]

An Active Data Warehouse is an important part of pervasive BI as it provides historical data infused with real time data on the fly either pushed or pulled to the user. The difference between a traditional data warehouse and one that is active is in the way it handles events. An active data warehouse will have pre-defined rules, schedules or event triggers which cause a certain automatic action such as notifying the user, giving a discount to a certain customer, etc.

The definitions of the ADW active elements are quoted from Teradata: Active Data Warehousing[2]: .

  • Active Access

Front-line users access the data warehouse for operational decision making with a service level agreement of five seconds or less (also known as "web speed").

  • Active Load

Near-real-time data enters the data warehouse via mini-batch loading, replication services or continuous streams of data from message queuing systems.

  • Active Events

Event-driven architectures and business activity monitoring detect significant business events and issue alerts for timely, informed decisions.

  • Active Workload Management

Dynamic priority management inside the data warehouse ensures service levels are achieved across multiple user communities and workload types.

  • Active Enterprise Integration

Integration tools and designs connect the data warehouse to web sites, portals, SOA, web services, enterprise service busses, workflow and batch systems.

  • Active Availability

Policies, procedures and redundant hardware ensure the entire information supply chain is protected when subsystem failure occurs.

Importance of Service Level Agreements[edit]

In order to take full advantage of Pervasive BI, Teradata[3] suggests that the data and architecture is held up against a Service Level Agreements (SLA) which guarantees that the system lives up to certain minimal expectations. The categories for the Data SLA are;

  • Data Freshness
  • Data Completeness
  • Data Cleansing and Accuracy

The categories for the BI SLA are;

  • Scalability
  • Performance
  • Reliability


Pervasive Business Intelligence has been used in the following tasks[4]:

  • Inbound and outbound cross selling by Customer Service


  • Point-of-sale fraud detection
  • Automated insurance claims triage and fraud detection
  • Personalized next best offer on web sites, ATMs, and POS


  • Supply chain business activity monitoring
  • Retail out-of-stock monitoring on promotional items
  • Labor and crew scheduling
  • Optimizing trailer and container loading and routing
  • Anti-money laundering
  • Dynamic pricing and yield management
  • Real-time manufacturing line quality alerts

See also[edit]


  1. ^ Joe McKendrick, Informatica (2008-08-31). "Pervasive Business Intelligence means BI for the masses". Informatica.  Text " " ignored (help);
  2. ^ Teradata. "The Active Data Warehouse". Teradata. p. 3. Retrieved 2010-11-18. 
  3. ^ Teradata, Informatica and Microstrategy (2007-08). "Critical Success Factors Deploying Pervasive BI". Teradata. p. 10. Retrieved 2010-11-18.  Check date values in: |date= (help)
  4. ^ Teradata, Informatica and Microstrategy (2007-08). "Critical Success Factors Deploying Pervasive BI". Teradata. p. 3. Retrieved 2010-11-18.  Check date values in: |date= (help)