Data is the driving force behind any successful business. Having an infrastructure designed to manage information as a critical business resource—to move it from its point of origin to any point of need quickly and efficiently—is imperative to support the various business demands that will emerge in the future.

So how do you create an enterprise IT architecture that is optimized for data management? It starts with a clear vision that factors in the value of your company's data assets, where the business is heading and what technologies are available to take you there.

Finding value in data: TIAA-CREF TIAA-CREF was founded by Andrew Carnegie in 1918, and its present-day data management goals remain aligned with its original mission to offer high-value products and services to customers. That mission statement is echoed in the data integration services group's vision: "To provide expertise and achieve a competitive advantage in the way we create, handle and present data," says Mark Clare, vice president of data integration services at TIAA-CREF. To achieve that vision, the organization recently launched a formal enterprise business architecture (EBA) initiative. "We're working to build a long-term data management program that is aligned with the EBA and driven from the corporate strategy through the corporate functions and business processes," Clare says. Clare's position at TIAA-CREF was established just over a year ago when the entire executive management team sought to expand data management across the organization. To assist in carrying the message, Clare can rely on the enterprise data management governance council and its workgroups on data quality, service-oriented architecture, business intelligence (BI) and data modeling. The directive being carried is "a business value message, not a technology message," Clare says. Still, current technologies help. The technological options used at TIAA-CREF "allow us to be more cost-effective and efficient in how we deploy business solutions," Clare notes. "It gives us greater flexibility." The measure of success, of course, is how data management contributes to the organization. "There's something very exciting about data management," Clare shares. It's not the data per se, but "it's finding the value in the data" that's so important. "It's amazing to see how technology can help enable our business," he says. —Shirley S. Savage

When you look at how IT can best support the vision of an organization, it boils down to one thing: the value of the data. How an organization uses its data can be the difference between average performance and competitive advantage. For this reason, it is vital to have a data management strategy that focuses on the creation of accurate, consistent and transparent data content that can be integrated into the business applications and business processes. Additionally, it is necessary to foster a corporate culture that recognizes the data as an asset and the data management strategy as vital to support the corporation's goals and objectives.

Envisioning change
Change is inevitable. Implementation of an effective data management strategy across the enterprise enables the organization to adapt quickly in the dynamic world of business. The agility to adapt and respond to evolving business needs, or even predict those changes in advance, will be the differentiator that allows a company to thrive. A consistent view of data throughout the enterprise is the key to be able to make informed, actionable decisions that support the vision and business strategy.

A well-thought-out data management architecture can help you take advantage of any number of opportunities that change presents. Shifts in organizational leadership, mergers and acquisitions, evolving marketing tactics and dynamic regulatory requirements can all be accommodated. Data-driven processes help you make decisions with confidence, and an enterprise data warehouse (EDW) architecture provides the ecosystem that will serve your new data management processes while accommodating future needs. The EDW also easily supports strategic operational decisions.

To aid your strategic decision making and properly direct your data management vision, you must consider what current resources are required to support your vast collection of data stores, data marts and databases, and how much value you are deriving from them. Any strategic analysis of data management possibilities would be incomplete without considering meaningful data consolidation into an EDW. For example:

	How would a single view of the business solve current challenges and deliver competitive advantages?
	How much time and effort go into reconciling numbers and validating data accuracy?
	Would greater accuracy, insights and confidence in data increase the use of decision-support and analytical tools?
	How much redundancy and inefficiency (perhaps as a result of redundant licensing, recurring infrastructure investment or administration expense) can be eliminated via consolidation?
	How would a single source for analysis and report generation mitigate business risks and support compliance initiatives?

No department wants to wait in line for business-critical reports. And they won't have to. Data processing techniques, processing power and enterprise performance management capabilities have undergone revolutionary advances in recent years.

Organizations benefit greatly from timely access to fresh data about sales, inventory movement, promotions and customers. Moving from monthly refreshes to weekly, daily, hourly or even more frequently can provide substantial incremental value.

An active data warehouse makes such timely availability a reality. In an active environment, it is possible to update information and provide intelligence throughout the organization. Corporate decision making and agility improves with new data sources, the constant inflow of fresh data and the ability of the right people to have access. Additionally, fresher data powers applications such as dashboards that provide at-a-glance status or alerts to the people who can take appropriate action on them.

Information time-sensitivity is often dependent on its type and purpose. Reports, for example, may need to be run only once a week, while certain customer or business information may be needed in real time to make operational decisions. Once your organization understands the value of its data assets, it is a matter of determining how those assets can support the corporate vision, who can best benefit from the data, when they need it and where to acquire it.

Your data, my data and our data
The days of IT telling business units what they need are long gone. Line-of-business leaders must be active in the data strategy discussions. Do not be surprised when territorial issues arise. After all, the value of corporate data is surpassed only by the territorial instincts of its departmental owners.

To address these potential challenges, it is important to work as a team and brainstorm without blinders. The discovery process is no time to be territorial, fear change or lose sight of the big picture. Nothing stifles innovation more quickly than "We've never done things that way before."

Existing processes have constraints. A major part of your discovery process should involve determining which operational processes can be automated—or even totally re-engineered—by giving access to new data resources or by linking existing data sources in new ways.

Goodbye, packaged applications; hello, innovation
Companies have more data sources than ever before. Those that effectively align their data strategies with their corporate vision are in a good position to offer innovation that can differentiate the organization from its competitors. Increasingly, gaining real competitive advantage requires unique data-driven processes and capabilities. After all, standardizing on the same data and applications as your competitors does little to help you pull away from them. According to AMR Research analyst Lora Cecere, "We are seeing a shift from packaged applications to custom capabilities that use data in new ways."

Turning goals into action: Haggen, Inc. At Haggen, Inc., a privately held grocer in the Northwest region of the United States, vision and strategy are guiding lights for the company's IT group. "IT enables the company to achieve its business goals," says Harrison Lewis, vice president and CIO for Haggen. When Lewis joined Haggen two years ago, he made it a priority to understand the goals of the key stakeholders "not from an IT standpoint, but in business terms." Lewis considers that knowledge to be critical. "We need to understand what their needs are, in their words," he explains. "Where's the current pain? Where are we going? What's the timing to achieve that?" This intelligence gathering is key to IT's success. "It puts IT in a position where there are no surprises," Lewis states. Furthermore, because of the complete understanding of the business needs, IT can "communicate the benefits of IT in business terms," Lewis says. "People down the line want to know, 'Is IT delivering solutions that can help me today?' " How does Haggen's CIO measure success? "By how quickly we can respond," Lewis says. When executives say, "We have a flexible IT department," Lewis knows that's because his team understands where the company is heading, anticipates what is needed and builds those needs into IT. —S.S.S.

In the process, new technology must be piloted—not just to ensure performance and reliability but also to determine risks and rewards before moving on to the next phase. Because line-of-business owners are now the gatekeepers who evaluate each phase, it is essential to have them clearly define success criteria for each gate before development.

This technology evolution and innovation requires fundamental changes in the way IT develops the data management infrastructure to leverage data assets via an EDW. It has also changed the role line-of-business leaders play in the process.

Cecere also notes the phased technology approach. "Today, risk can no longer be controlled through straight program management," Cecere insists. "In the past, IT primarily focused on meeting timelines and budgets while implementing technology. Now, it's more of a phase-gate process that goes far beyond implementation. Innovation creation is broken into a series of sequential phases, with gates that must be cleared before advancing to the next phase."

Creating your strategy for optimizing data management begins with understanding the role effective data management can play in achieving the overall vision for a business. Technology initiatives that affect other business units often fail when they are driven from inside IT. Executives must be on board from the start and involved in a planning process that includes all key business units. If finance, marketing, sales, production, purchasing, shipping and human resources all have input regarding what they would do with better data and what the business benefits would be, everyone will understand the grounds for prioritizing objectives, estimating return on investment and setting hard targets.

Once executive leadership understands what is possible and the business value of your data strategy, it is essential to sell the vision and drive it forward through a collaborative process that includes key business constituents. It is also important to establish realistic criteria to measure success and timelines to achieve this value.

The same is true for enterprise data management. Effective strategies are best viewed as a development continuum that is continually refined. You should revisit your data strategy at least once a year. According to AMR Research, "Best-in-class companies are measuring progress and fine-tuning their data management strategies quarterly."

Whether you're simply focusing your data management strategy or considering major direction change for the corporate vision, be sure to look to the business unit experts—it's the best way to see the whole target and aim for meaningful, continuous results. T

Sell the vision Even the most innovative and compelling vision is destined to fail unless people buy in to it. Remember, many influential employees do not have the shared insights gained during your business planning and brainstorming discussions. And while the benefits of your data management vision may be obvious to you, others need to be educated to fully understand its strategic value—as well as the all-important "What's in it for me?" Here are some tips to help you package and sell your vision: > Evangelize your vision, but package it as a business vision—operational excellence that is IT-enabled. > Promote early successes, and help others understand how this new capability supports the corporate vision. > Ask members of the first business units that receive substantial value to help evangelize their success. What should you do if certain groups don't buy in to your data management vision? > Use case studies that show the value of differentiation and how people achieve better business results. If case studies don't yet exist in your enterprise, find relevant success stories within your industry. Many case studies are available on Teradata.com and TeradataMagazine.com. > Educate the business about why it makes a difference. > If they still don't get it, or simply enjoy contention, use influence management with the CFO and CEO. —S.F.

Make your move While a strategic vision is essential for successfully mobilizing your information assets, no IT vision ever got off the whiteboard without a detailed roadmap for implementing the physical and logical infrastructure. Ultimately, technology and vision must work together to support the organization's business strategy. In populating your information management solution stack, you'll need to address three distinct infrastructure tiers: Tier one: The enterprise data warehouse Much more than a simple shared repository, the enterprise data warehouse (EDW) is a multi-purpose platform that provides global data aggregation and management, centralized reporting and analytical processing and, increasingly, active support services for front-line business systems. Any solution considered for the EDW function must be: > Powerful enough to support many concurrent workloads, maintain very short response times for tactical queries and meet strict service levels for other key services > Predictably scalable to very large data volumes and processing capacities, providing a viable growth path that will support any level of organizational expansion or any shift in strategic direction > Easy to manage, requiring a minimum of routine administrative labor and expense, and shipped with all necessary management tools and utilities > Highly interoperable, providing open, standards-based interfaces and a proven history of successful interoperation with other key data management technologies and solutions, including extract, transform and load (ETL) platforms; messaging and application integration middleware; business intelligence (BI) tools; and business applications Tier two: Data management support solutions A master data management (MDM) solution provides standardization and quality control for core reference data, which is the fundamental business data in an enterprise. MDM is essential to managing data assets and relationships and helps: > Apply data quality standards for clean master data > Extract master data from operational and reporting systems to a central location > Reconcile data to achieve one view of the master data Tier three: Analytical solutions Analytic capabilities enable the organization to move beyond reports and make better-informed actionable decisions. For example: > Proper management of customer relationships can help the organization orchestrate customer communication and marketing across multiple business channels. Customer relationship management solutions also deliver a unified view of the customer. > Finance groups can extend quantitative discipline to a wide variety of management decisions across the enterprise and provide an integrated view of financial data with financial performance management solutions. > Organizations can optimize resource allocation with global costing and profitability metrics based on multi-dimensional modeling. Integrated profitability analytics accurately forecast value outcomes from specific investment choices, allowing resource concentration on the most profitable customers and products. > Key metrics from purchasing, sales, inventory and logistical data can be used to optimize on-shelf availability and inventory levels. Supply chain management solutions help organizations and their trading partners continuously manage business performance across complex supply chains. > Customer service and inventory levels can be managed simultaneously by providing item- and location-specific demand forecasts with demand chain management solutions. —Bill Tobey

Scott Ford is a senior writer and creative director at Ford Sherman, a Salt Lake City-based writing firm for technology clients.

Teradata Magazine-December 2007