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‘Active’ returns from the warehouse

An active data warehouse is an evolutionary step forward from a traditional data warehouse. Stephen Brobst, the Chief Technology Officer of Teradata, talks about the benefits of this technology. by Soutiman Das Gupta

How should an organisation go about building a data warehouse?

Successful data warehouse implementations deliver business value on an iterative and continuous basis. Each iteration builds upon its predecessor to increase the business value for information delivery. This explains the combined role of traditional and active data warehousing. Rather than two different poles, traditional and active data warehousing are integral parts of the data warehousing evolution process.

In the early days, data warehousing focused almost entirely on providing strategic decision-making capability to knowledge workers in the corporate ivory tower.

The underlying philosophy for implementing an active data warehouse is to increase the speed and accuracy of business decisions. The goal is to achieve decision-making as near real-time as necessary to deliver maximum value

End users for the data warehouse were traditionally from areas such as marketing, strategic planning and finance.

There is a lot of buzz around active data warehousing. How does this differ from traditional data warehousing?

Data warehouse deployments improve the execution of a business strategy and this evolution imposes an ever-increasing set of service levels upon a data warehouse architect helping make data warehousing an active process.

The underlying philosophy for implementing an active data warehouse is to increase the speed and accuracy of business decisions. The goal is to achieve decision-making as near real-time as necessary to deliver maximum value. The key point is to let the business application drive the data freshness and performance service levels.

Stephen Brobst

Is an active data warehouse more efficient?

An active data warehouse provides a new breed of decision support. The active data warehouse differs from a traditional data warehouse on many levels.

First, the active data warehouse is a business-critical system for an organisation. Since it supports operational decision-making, downtime cannot be tolerated. In the traditional world of data warehousing, downtime is certainly undesirable and decreases productivity for knowledge workers, but it is not usually considered business-critical.

When a traditional data warehouse is unavailable, decisions are deferred until it comes back up. Yet because those decisions are long-term in nature, the bottom-line cost of deferring a decision is not overly significant. On the other hand, the opportunity cost for downtime in an active data warehouse is very high because operational decisions cannot be deferred.

Does downtime have a substantial impact on an active data warehouse?

When an active data warehouse is unavailable, operational decisions are made without the benefit of quantitative decision support. The impact of downtime on an active data warehouse is the difference between optimised decisions and information-deprived decisions.

In an enterprise deployment, the cost of downtime is very high and cannot be easily recovered. Like a traditional data warehouse, a best-of-breed active data warehouse spans functional and departmental boundaries within an organisation. It provides a single source of truth for both tactical and strategic decision support.

What makes the design of an active data warehouse special?

An active data warehouse is designed to support enterprise-level business objectives, and typically reaches further into the organisation than traditional data warehousing. This often means integration with multiple channels across the organisation such as the Web, call centre, and other customer touch points.

A key feature of an active data warehouse is to reduce the time between critical business events and resultant actions. It is essential that the data analysis that takes place in an active data warehouse be translated to actionable decisions to maximise the value proposition from its deployment.

An active data warehouse enables whatever service levels are appropriate, and should be able to scale to an enterprise level in doing so. Despite this, when designing an application, it is important to be prudent in matching desired service levels with the application requirements.

An active data warehouse, when architected properly, allows each workload to be assigned its own service levels in order to optimise the economic equation between business value delivery and capacity requirements for delivering to the desired service levels.

What role do ETL and EAI tools play in a data warehouse?

From a business perspective, Enterprise Application Integration (EAI) means providing unrestricted sharing of data and business processes among connected applications and data sources in the enterprise.

To realise this goal, architects must provide a technical infrastructure capable of combining business processes, software and hardware platforms, and standards to allow seamless integration of two or more enterprise systems so that they operate as one or at least provide the illusion of doing so.

A variety of industry buzzwords all focus on this goal. Web services, message brokers, application servers, and middleware tools all provide aspects of the infrastructure necessary to realize the EAI vision.

Extract Transform Load (ETL) is generally used to move large sets of data, transform it mid-stream and load it in the target system. ETL is usually a pull system; however, some vendors are heading toward push/pull ETL.

ETL has become a commodity in the marketplace. Nearly every data-warehousing vendor offers it, and most databases understand what it takes to prepare data for loading into a data warehouse.

Is the difference between ETL and EAI tools blurring?

Thanks to active data warehousing, data is bypassing ETL completely and being deposited by EAI and other mechanisms directly into the enterprise data warehouse, requiring that transformation be embedded in the DBMS systems. This may however not be true for data warehouses that still operate in batch mode.

Volume, latency and functionality have hit a convergence point. Businesses no longer have the time to perform substantial transformation on massive data volumes before making tactical decisions. Traditional ETL methods create bottlenecks because in some cases they offer a single-point solution for either batch or near-real time, but do not offer a complete view of the data.

In order to survive going forward, ETL engines are shifting their focus to either Extract Transform Load Transform (ETLT) or Extract Load in Real-time with Dynamic restructuring capabilities (ELRD).

EAI infrastructure provides a bridge between the world of bookkeeping and decision-making

Will these technologies transform a company’s information infrastructure?

An active data warehouse deployment relies upon EAI infrastructure for both data acquisition and decision delivery. It requires extremely up-to-date data from the transactional processing systems within an organisation.

Advanced EAI infrastructure can facilitate real-time or near real-time data acquisition. The EAI infrastructure provides a bridge between the world of bookkeeping and decision-making. When a business event is recorded in the bookkeeping systems, the EAI infrastructure updates the decision-making environment (active data warehouse) on a real-time basis.

The bridge works in reverse as well. When analytic applications in a tactical decision-support implementation detect the need for an action, the EAI infrastructure is used to deliver decisions to the OLTP systems that will be responsible for the associated bookkeeping activities to make each proposed action a reality.

EAI with process integration allows for ‘closed loop’ decision-making. Data fed from the bookkeeping environment into the active data warehouse will cause event-based triggers to fire based on business rules, and initiate decisions that are fed back into the operational bookkeeping systems for execution.

Give us an example of a closed-loop decision-making scenario:

For e.g., consider a retail environment where purchase transactions are captured using an electronic Point-of-Sale (ePOS) network in thousands of stores distributed across an extensive geography.

Transactions from the ePOS systems are published to an EAI message bus as they occur in the stores. These business events are then delivered to all appropriate subscribers, including the active data warehouse.

Under certain conditions, such as when sales trends indicate a rapid depletion of inventory, the business rules embedded in the analytic capabilities of the active data warehouse will arrive at a decision to order additional items for delivery to those stores that would otherwise end up with empty shelves.

This inventory ordering decision is published using the EAI message bus and divisions such as ERP and general ledger systems subscribe in order to be involved in the realisation of the inventory re-order decision. EAI provides the ‘glue’ to facilitate the closed loop cooperation between the bookkeeping and decision-making systems.

Soutiman Das Gupta can be reached at

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