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All studies show that, with globalization, large companies are faced with an increasing number of decisions and that these decisions are more complex and involve greater quantities of data. In today’s world, clearly one must have access to strategic and operational intelligence throughout the entire company and be able to take actions that are appropriate for the dynamics of the business environment. In this context, a company cannot expect to rank among the leaders in its industry by simply relying on a few indicators and pre-formatted planning boards. It is especially unwise to listen to the advice of those who know nothing of decision-making, who think that they can meet the needs of major global enterprises with query tools, a few OLAP hypercubes and standard ERP planning boards.
The intelligence necessary to support the activities of a major global enterprise requires a decision-making infrastructure that is available 24 hours a day, seven days a week, 52 weeks a year, with detailed data repositories, quasi-real time updates of certain data, traditional management decision-making applications (e.g. indicators and planning boards), operational decision-making assistance, in-depth analysis capabilities, free access to widespread data, process management resources and DataLab.
The intelligence deployed must assist with the day-to-day problems that a company encounters, as well as more occasional difficulties. Issues involving strategy, operational approach and management arise on a daily basis, and sometimes decisions made to address one of these issues will immediately interfere with a decision in another area. Consequently, it is necessary to design a decision-making system that can take into account the needs mentioned above while facilitating various uses of data.
One real-world example of the intelligence necessary to support an active company was the launch of a new DSL television service by a telecommunications operator. The objective was to have 200,000 subscribers to this new service in one year. Considering that it would be necessary to make adaptations according to local infrastructures, a gradual launch was the most appropriate approach.
The first intelligence contribution made for this one-year launch plan was to facilitate the establishment of a general launch schedule that took into consideration the potential number of customers who might be interested in this new offer, the existing availability of cable TV service in some areas and certain technical limitations. As a result, the areas were divided into 15 groups. The second intelligence contribution was to monitor daily sales in the traditional manner. In the first month, sales were at approximately 25 per day, and then gradually reached 50, eventually hitting 100 at the end of the second month, and climbing to 1000 in the third month following an initial advertising campaign. From then on, sales varied between 700 and 1200 for five months, then between 1700 and 3500 through the end of the year as the service gradually became available in different areas, as advertising campaigns were conducted, and as the service packages and rates were adapted. In the end, the objective was reached, with 207,000 subscribers obtained.
The third intelligence contribution was to analyze the influence of launching this new service on subscriptions to other services, which led to refining combination service packages (such as the dual package: DSL & television, or the triple package: DSL, television and telephone) with related financial advantages that might have a negative impact in terms of margin. These impacts were monitored specifically. The fourth intelligence contribution consisted of analyzing customers’ use of their services in terms of quantity and time period (particularly for television and video on demand), as the services were launched by geographical area, in order to adapt the service to customer interest, to improve the marketing message, and to schedule network developments around foreseeable peak consumption times. Of course, we cannot forget an additional intelligence contribution, which was the usual management monitoring, with its series of indicators and planning boards for conducting the administrative routines of the various company functions.
What this example illustrates is the variety of uses of historic data that support a company action taken by various participants involved in deploying a service, selling it, advertising it and managing it. Imagine the difficulties there would have been in inter-departmental dialogue for the same launch undertaken by an organization in which each functional department had its own private data that was more or less consistent (in terms of definitions, periodicity and updates) with the data maintained by the other departments. Operational situations such as the ones described in this example are very frequent in major global enterprises, which, like the company described in this example, operate on multiple continents, in dozens of countries, and cannot rely simply on a few OLAP hypercubes and standard ERP planning boards.
Most of Teradata’s clients are global enterprises, and those who, like the Gartner IT review, are familiar with Teradata’s services, know why. For more information on the subject of intelligence to support active businesses, please visit us at: http://www.teradata.com/Active-Enterprise-Data-Warehouse/#tabbable=0&tab1=0&tab2=0&tab3=0