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All organizations need the capabilities provided by a data warehouse platform, says William McKnight, president at McKnight Consulting Group.

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Enterprise View

Start small and think large

Regardless of size, organizations can meet their information needs through data warehouse platforms.

As companies take steps to manage their information assets, choosing a platform and database management system (DBMS) is fundamental. The platform is the foundation of architecture and business intelligence (BI) and the starting point for tool selection, consultancy hires and more. In short, an organization’s platform is key to defining its information culture.

These platform decisions are made in a challenging and ever-changing landscape. Over time, data volumes soar as history accumulates, syndicated data is collected and new sources with more detailed information are added. Furthermore, the communities that consume the data continue to grow, expanding well beyond company boundaries to include customers, supply chain partners and the Internet. Organizations need to choose a proven, scalable platform not just for initial, known requirements but for future, yet-to-be-determined demands as data, users and applications emerge.

These challenges are not limited to the big players. Mid-size companies have similar data management needs, albeit with smaller data volumes and, sometimes, fewer users. All organizations need:

  • Rapid development that can be built upon over time
  • Quality, available data
  • Architectures that provide low, long-term total cost of ownership (TCO)
  • Good query performance that results in growing interactive usage
  • The ability to get to real-time feeds
  • A platform to support advanced workload management
  • A scalable path forward as data, users and application needs grow

Complicating matters, organizations are now faced with an exponentially greater number of variations and distinct departures from the traditional online transactional processing (OLTP) DBMS than ever before.

Enter the fray

The battleground on which many industries engage extends well beyond customary core competencies to the collection, management and use of data. As proof, even in a subdued economy, BI remains at the forefront of IT spending. This is due, in large part, to the applicability of information directly and indirectly to an enterprise’s bottom line.

SIDEBAR: Selecting an enterprise data warehouse platform

The technical architecture for a mid-sized or larger company’s enterprise data warehouse platform should be:

  • Scalable in both performance capacity and incremental data volume growth
  • Powerful for complex decision support in an advanced workload management environment
  • Manageable with a minimal amount of support tasks requiring intervention by a database administrator or system administrator
  • Extensible in its database design and system architecture so that it keeps pace with evolving business requirements and leverages existing investment in hardware and software applications
  • Interoperable with integrated access to the Web, internal networks and corporate mainframes
  • Recoverable in the event of component failure so the system continues to provide value to the business
  • Affordable, including hardware, software and services, providing a relatively low total cost of ownership over a multi-year period
  • Flexible in providing optimal performance across a full range of normalized, star and hybrid data schemas with large numbers of tables
  • Robust with in-database management system features and functions

—W.M.

Information must be flexible, manageable and actionable—all within the framework of a multitude of IT-related realities. These include multiple and complex applications serving a variety of users, exploding data volumes, and the competitive necessity of accessing data in real time.

As data accedes to its profitable use and platforms evolve to handle the workload, new demands to leverage data will arise, creating new requirements on a seemingly never-ending basis.

The EDW strategy

Regardless of an organization’s size, the efficacy of having a centralized data store with quality, integrated, accessible, high-performance and scalable data cannot be denied. Yet some with a decentralized orientation believe that initiating an enterprise data warehouse (EDW) is too laborious without a quick and clear return on investment (ROI). The assumption is that EDW architecture implementation requires an unbearably long, year-plus timeline before it will deliver business value. Fortunately, this is no longer the case.

Today, an EDW represents a commitment to organize a company’s information in the most efficient manner possible. It’s not developed using a big-bang approach but is accomplished by first meeting the objectives of a key subject area, data source, business objective or user department. Then, enterprises progressively build the environment with scalability. Another manageable aspect of an EDW strategy involves the consolidation of smaller, independent data marts into a centralized, money-saving architecture.

The most efficient way to accomplish one’s EDW objectives is by building a data warehouse to solve specific needs in a manner that leverages previous investment in the architecture, tools, processes and people, without limiting future growth. This enables a programmatic approach to data warehousing, created to deliver information to the enterprise.

Setting aside EDW implementation is particularly important for mid-market organizations that are starting to develop an architectural foundation. Too often decisions are made within departmental boundaries without considering an overarching data warehousing strategy. This has led many companies down the path of data mart proliferation—the creation of non-integrated data sets to address specific application needs, usually with an inflexible design. In the vast majority of cases, data mart proliferation is the result not of a chosen architectural strategy but of the lack of one. In any case, bringing the EDW to bear economically at the outset is critical to taking advantage of its vast promise down the road.

Target the mid-market

BI vendors have been slow to respond to the needs of the mid-market. This factor, combined with typically limited budgets, has required many of these companies to take alternative paths to BI. In fact, the multi-layered architectures and multi-quarter “timeframes to value” were barriers to BI in the mid-market long before the recent recession.

Enterprise-class BI, initiated with simplicity and scalability, is now available in a mid-market-oriented suite of affordable platforms delivered as pre-configured data warehouse appliances. These appliances offer low TCO for a mixed workload data warehouse environment.

Whatever a company’s information needs, the principles of scalability, power, manageability, extensibility, interoperability and flexibility will support its goals. With a range of data warehouse platforms available, organizations of all sizes can start small, think big and scale fast.


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