Government agencies are in the midst of a paradigm shift a transformation in how business is conducted at federal, state and local organizations. Government professionals are being forced to do more with less, compete with the private sector, operate with tighter budgets and smaller staffs, and provide better service to the people. As a result, agencies are being forced to evaluate their core strengths and weaknesses and find new ways of doing business. Information technology has served a vital role in the drive to meet these new challenges. It is no surprise that data warehousing one of the hottest developments in the IT industry is quickly becoming an integral part of the strategic direction of government organizations. Data warehousing could be the most significant advance in government computing in the coming years.
The proven benefits of data warehousing for commercial organizations are clear. Recent surveys indicate that a large percentage of Fortune 2000 corporations are either planning or have already built large scale data warehouse initiatives as a means to increase sales, reduce costs and maximize profits. These initiatives will enable sophisticated decision support systems to deliver necessary information throughout organizations and beyond. But the question remains: How will public organizations benefit from jumping on the data warehousing bandwagon? The answer lies in the realization that information is one of the government's largest and most underutilized assets.
Both private and public organizations face the reality that resources are limited. Capital assets are scarce and will only continue to be so, and rightsizing and downsizing results in limited human resources. As these resources decline, organizations continue to amass large amounts of data information that often holds the key to more efficient organizational operation. However, government organizations are struggling with the fact that the means to access this information are often crude and underdeveloped. The information exists, but creating smooth, enterprise-wide access to these data stores is another matter. Why is strategic access so important?
Regardless of the product or service being offered, a corporation, agency or department needs to thoroughly understand its customers and constituents. An organization needs to know how well it is executing its mission and how it can improve service. Organizations must manage costs as well as human and capital assets, while continually finding new ways to reach customers, constituents and business partners. The ability to fully leverage information assets can have a dramatic influence on each of these areas.
Leveraging the Value of Government Information
Data warehousing and decision support technology present an opportunity for information management to change the way organizations leverage and value their information assets. With the ability to easily access information, mission delivery, resource management and data dissemination can be raised to levels previously unimagined.
Mission Delivery: Government organizations are measured not by profits and losses, but by their ability to deliver upon their mission. Regardless of this mission, the ability to understand the customer, or constituent, is a key factor in matching services to customer needs. The IRS, for example, needs to understand the profile of each taxpayer to increase the level of voluntary compliance. Identifying the most common filing errors for each age, income and education level would enable the agency to make changes to the filing process, thereby improving customer service and mission effectiveness. The data clearly exists, but offering the best means of accessing it has not been determined.
Resource Management: Agencies also have an opportunity to leverage information assets to improve the management of resources. Tremendous benefits can be gained through integrating strategic financial, human resource and programmatic information into "Enterprise Resource Management" decision support systems. In addition, an agency can use information to better understand the skills, tenure and performance of its employees. For example, a recruiting officer in the armed forces needs to understand the strengths and weaknesses of each unit to effectively plan recruiting initiatives for the coming years. Organizations tasked with logistics management, such as the Air Force Materiel Command (AFMC), need to have current information to better maintain capital resources and to improve mission readiness. A maintenance officer with the AFMC may need to know the top three weapon systems on the F-16 which require the most frequent repair, the manufacturers of these weapon systems and the reasons for repair. Again, the data exists, but the information is not easy to access.
Information Dissemination: Agencies tasked with disseminating information have perhaps the greatest incentive to manage and broadcast information to relevant parties. The Bureau of Labor and Statistics, for example, could automatically generate and send labor statistics to each member of Congress specific to his or her district. The IRS could notify citizens via e-mail when their tax returns have been received and successfully processed, or when a filing error requires action. The FDA could automatically notify doctors and patients when new drugs and treatments have been approved. The applications are numerous, but success is contingent upon one factor access to information.
By employing the latest decision support technology agencies can not only maximize access to information, but can bid farewell to the massive paper trail often associated with government bureaucracy. Rather than providing non-detailed information to uninterested constituents, agencies can now direct the right information to the right people at the right time. Instead of viewing information as possessing limited insight, agencies should view information as an enterprise-wide asset from which hundreds, thousands or even millions of individuals can derive value. Figure 1 illustrates the exponential value that may be derived from an organization's information assets.
Understanding the immense benefits at stake, what then are the guidelines government organizations should follow to build mission-critical decision support systems (DSSs)? Agencies must first consider the successes and failures of data warehousing endeavors spearheaded by corporations in the private sector.
Commercial DSS What We Have Learned
Government agencies have a clear advantage over many commercial organizations when considering data warehouse initiatives. The early adopters of DSSs in the corporate world have identified and conquered many of the challenges associated with information management. As government agencies embark on data warehousing initiatives, there is a great opportunity to make this transition smooth and swift and leap to the cutting-edge technology and techniques currently employed. To aid in this process, it is useful to review some of the lessons learned on the data warehousing battlefield.
Lesson #1: Demand all required answers. Don't let the limitations of a product change the way you organize your business.
First, evaluate the information value of the "ideal" data warehouse. Data warehouse architects must identify all the questions that could possibly be asked of the application to help them achieve the mission of the organization. Never cut any corners. Let imaginations soar and make certain the requirements analysis is exhaustive, even if it is clear that the data warehouse that would be required cannot realistically be built. It is important to make sure that the present incarnation of the data warehouse is not inconsistent with future goals. Draw up the ideal data warehouse and retain it for future consideration.
Second, when building and deploying the real-world data warehouse (as opposed to the "ideal" warehouse), be conservative about the questions that will be lost as hard choices are made. Losing the ability to ask some questions will be inevitable, as the data may simply not be available. Some of the questions might just be too expensive to answer, as the cost of cleansing and reconciling the data may not be sufficiently offset by the benefit of the additional reports. Be forewarned, however, that some of the questions can be lost due to limited capabilities of a decision support tool or OLAP (on-line analytical processing) product. Occasionally, data warehouse architects make the mistake of "wrapping the business around the product" instead of "wrapping the product around the business."
Third, choose a data warehousing and OLAP solution that reflects your declaration of independence of data warehouse design from product capability. Such solutions ensure that the latent informational value of the data warehouse is preserved and reflected in the schema, or OLAP multidimensional model, and in the final presentation to the end user. A data warehousing effort will be compromised unless the decision support solution supports broad warehouse schemas, great data depth and breadth, as well as rich analytical capabilities.
Lesson #2: Users must receive answers when, where and how they would like them.
A latter day data warehousing historian, writing in the July 2010 issue of DM Review, might conclude that the one generalization to be made about DSS end users of the 20th century is that no generalization can be made. The historian may wisely observe that DSS end users spanned the globe and accessed common data warehouses from far-off lands. End users held allegiance to different operating systems and possessed wildly disparate computer literacy skills. Many users had access to a LAN/WAN while others were holed up in a motel room with nothing more than a phone line. Some had a need for reports of great complexity while others were delighted with a high-level business summary. In addition, some had the authorization to view all levels of information while others lacked security clearance for anything more than browsing the cafeteria hours. Lastly, some 20th century analysts had unlimited time to craft questions to which they needed answers, while others preferred to be alerted only when specific situations occurred. And, what's more, many did not even have a computer.
This variety of end-user profiles suggests that it is not sufficient for decision support providers to merely provide access to the data warehouse. Providers must also enable the distribution of this value through multiple interfaces to end users with different infrastructures, skills and needs. The decision support solution must provide equal access for all.
A DSS architecture should allow for data warehouse access via the desktop and the Web, as well as provide the possibility of customization via an API. Additionally, the architecture must enable OLAP results to be broadcast to computers and consumer electronic devices such as pagers, phones and fax machines directly from the data warehouse. A Web DSS interface should include features compatible with its desktop counterpart. An OLAP information broadcast environment should enable sophisticated triggers to fire off results as needed to all the latest communications devices. Lastly, this power should be fully configurable within each environment.
Lesson #3: Access must be secure.
The need for secure access to data is perhaps more important in the government sector than anywhere else. It is imperative that the OLAP solution offers the flexibility to configure the data access capabilities of the product to match the access privileges of the organization. In addition, the OLAP solution must ensure that security access is not breached at any level of the DSS architecture.
Lesson #4: Surveillance must be continuous and notification swift.
Our old friend, the data warehouse historian, ruefully examining the silicon rubble of the decision support systems of the late nineties, might suggest that only those systems that remained nimble and harnessed emerging technology to meet evolving end-user needs were able to survive. Data warehouses grew rapidly in size, the number of required reports escalated and the community of end users clamoring for access grew exponentially. The key to survival lay in vastly expanding the overall frequency of report execution by running them in the background, with triggers specified, before sending only those results that truly merited attention to end users via e-mail, pager, fax or voice mail. Surveillance had to be continuous and notification swift if one was to survive in the fiercely competitive landscape of the nineties.
This evolutionary concept is what we know today to be information broadcasting, a technology that makes it possible to send highly personalized decision support messages to hundreds of thousands of recipients via e-mail, fax, pager and mobile phone. Using exception conditions and recurring schedules as triggers, information broadcasting maximizes the value of data warehouses by automating the delivery of critical information to end users in an efficient and unobtrusive fashion.
Information broadcasting, the latest trend in decision support, has the potential to massively expand the audience for information obtained from a data warehouse. Government agencies may soon follow their commercial counterparts by transforming their warehouses into revenue-generating assets, forever altering the operating principles of federal organizations as they exist today.
In the final analysis, our historian friend might wryly observe that any data warehouse built in the 20th century was doomed to be a succession of compromises. But he would add that the very best among them, those that won, were ones in which the designer made compromises or tradeoffs with a conscious effort to always maximize one crucial variable: information value.
If special attention is paid to this underlying truth, a data warehouse can successfully form the bedrock of an agency's information delivery infrastructure. Government organizations can be well on their way to implementing a data warehouse and DSS that will be the cornerstone of decision-making processes from sea to shining sea. And, who knows, one such organization may soon find itself written into the annals of data warehousing history.
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