Implementation of Data Warehouse and Reporting in Fokus Bank

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1 Implementation of Data Warehouse and Reporting in Fokus Bank Håvar Wold Niklas Data AS/Coopers & Lybrand Consulting ANS Introduction: In this paper, the focus will be set on the SAS System and its strength in the field of DataWarehousing and the exploitation of the information which can be found inside it. The case that will be presented, comes from the cooperation between the Norwegian Fokus Bank and the Oslo office of the consulting firm Niklas Data Group. This presentation will discuss different challenges that we met during our work. This includes both a brief overlook of technical aspects, deviation between the 'should be' and the 'as is' situation of all the different applications we had to collect data from, and how to model the DataWarehouse in such a way that the bank actually could gain something when exploiting it. Two systems built on top of the DataWarehouse will be presented with pictures of some of the screens and some short comments. A more thorough demonstration will be given during the presentation, if you miss it and still would like to see it, please get in touch with Niklas Data and we will help the best we can. The paper reflects on which factors we have found to be the critical success factors, and gives an overlook on time spent and resourses used in these two projects. An important line of wisdom is (as SAS Institute says): 'Think big - start small'. Background: Niklas Data started the cooperation with Fokus Bank in April After some more or less slow years, Fokus Bank was now on the way up again and ready to invest money in new information systems. As a pilot project they chose to look into the field of DataWarehousing. Based on internal knowledge of the SAS System and good reviewes in the press for SAS Institute s solution for DataWarehousing, Fokus Bank decided to use this system for the task, and Niklas Data, together with SAS Institute, were engaged in the project. So far the cooperation has resulted in two projects, and new ones are coming up. The existing DataWarehouse in Fokus Bank has been implemented in two stages. First a part concerning the account plan in the bank, then later an extention which consists of information gathered from all the different backoffice systems in the bank. As part of both these projects, one of the goals has been to make Fokus Bank exploit the information in new and informative ways.

2 On top of the first DataWarehouse, a reporting tool implemented with SAS Eis Motore was built. As most businesses today, Fokus Bank had a paperbased management information and decision support system. These reports where subject to a time consuming production and distribution process. The scope of the first project was to find a more dynamic solution to this reporting than the existing paper reports, and also to give the bank faster access to this information. The second project extends the DataWarehouse to contain information from all the backoffice systems in Fokus Bank as well. At this point it makes it possible to do some automatic risk reporting to the government. It is the first step towards a financial risk management system that at sight will give the bank the chance to manage this risk in a flexible and rewarding way. Niklas Data Group: Niklas Data was founded in Sweden in 1987, and is an independent consultant company specialised in developing business solutions based on the SAS Software. Our vision has been the same for these 10 years; To be the preferred Provider of Business Solutions based on - Functional Competence - Methodologies and - The SAS System Niklas Data's headquarters are in Amsterdam and we have offices in Sweden, Finland, Denmark and Norway. Today we employ more than 100 specialised SAS consultants. In 1994 we became a certified "SAS Quality Partner", and since 1995 we have been charter members of SAS Institute's "Rapid Warehousing Program". Niklas Data works with Clients across all Industries, and with all elements within the SAS System.

3 Fokus Bank DataWarehouse; Part 1. (Featuring 'OLAP-Exploitation') In the first project, Fokus Bank wanted their financial board reports generated automatically, stored away periodically in a historical DataWarehouse and with possibilities for online analytical processing (OLAP). In short, OLAP makes it possible to perform multidimensional viewing and analysis. More about this later. 1 Månedsregnskap FORSIDE Innhold Hovedtrekk 2 Valuta og pengemarked FINANS- RAPPORT KONSERN FORSIDE Innhold FORSIDE Innhold Hovedtrekk 3 Tap- og kredittrisiko Kvartalsvis FORSIDE Innhold Hovedtrekk 4 Diverse rapporter FORSIDE Innhold Hovedtrekk Figure 1: The entire financial report to the board Of the entire financial report, we chose to start with the section concerned with monthly internal accounting reports. This was done according to the principles of the Rapid Warehousing Methodology supported by SAS Institute and Niklas Data.

4 Figure 2: The techical environment for the project In this first project, the information gathering was relatively straight forward. We had just one information system to attend to. Fokus Bank has outsourced their transaction systems to NOVIT which, among other tasks, takes on this kind of work for many of the Norwegian banks. This secured that the project could relay on accurate and correct data. The first part of the solution is an automatic gathering of information from Novit to the internal Windows NT server in Fokus Bank The second part of the job is to structure the information according to the logical data model developed in cooperation with Fokus Bank. The model had to be made in such a way, that OLAP-reporting could be done. Some of 'the OLAP Rules' are: { Multi-dimensional conceptual view { Unrestricted cross-dimensional operation { Flexible reporting { Unlimited dimensions and aggregation levels 'Dimensions' is in other words important. The logical datamodel is developed with this in mind. It is important that this logical model is built with the endusers and his or her needs in mind. In this project we identified three different dimensions: { Organization { Time period { Accounts

5 Time Organisation Year District Fokus Bank Month Departement Branch Account Result after tax Accounts Figure 3: Logical data model As seen in figure 3, each dimension consists of different aggregation levels. Every combination of these three dimensions are possible during the analysis of the information. Therefore, building 'the right' logical datamodel and structuring the information according to this model, is important in fullfilling the OLAP rules. Every aggregation level of each dimension can be combined with one another. This gives the enduser a great flexibility if the application on top is designed to take advantage of the structure. Such a solution will not create any restrictions whatsoever during the analysis process. (This is of course within the limits set by the model.) In addition, an automatic loading of the DataWarehouse must also be implemented. This calls for either a scheduler adding new information to the DataWarehouse on a regular basis, or as we chose in this project, the possibility to start a new loading with 'a click'. This was done because we could not rely on the information being correct for a spesific day during the month. This varied from the 1st to the 5th in a month, and a solution based on a scheduler was never an alternative. OLAP-Exploitation DEMO The next part will give a taste of the OLAP-reporting solution we built on top of the DataWarehouse.

6 Figure 4: The main menu The main menu consists of an expanding report menu, an administration button for those users defined with that kind of responsibilities, and a deviations pathfinder initially showing information for every main unit in the bank. Figure 5: The deviation pathfinder When activating this pathfinder, the users can choose between different analysis on different levels in the organization. All the different deviations are calculated in front, making it easy to identify figures that differ from what the budget was. Instead of having to search through the whole report, it is possible to identify and analyse interesting situations on the fly.

7 The different reports are all built up in the same manner according to KISS. ("Keep it simple stupid!") Most of the users of this application were not used to a screen based work approach. One of the points with this application was to show Fokus Bank some of the possibilities and to get the users involved and enthusiastic about the project. The solution can without much extra effort be made more sophisticated. (This will be done in the initial phases of the followup project soon to be started.) Some screens from this application are shown below. Multi-dimensional report Multi-dimensional graph Multi-dimensional graph Administration menu User menu User administration (access and security)

8 Metadata menu Metadata administration Menu for loading of DataWarehouse Timestamp of information in DataWarehouse (To get an impression of this solution and its possibilities, see the presentation or feel free to get in touch with Niklas Data.) Time spent and resources used Only two consultants from Niklas Data were involved in this project from beginning to end. We were involved in every step of the process from information gathering in Fokus Bank to implementation of the final solution. One of these consultants also had responsibility for the project management. The project was carried out over a periode of about 90 days. Over 90% of this time was spent in the implementation of the DataWarehouse. From that point on, the design and programming of the OLAP-reporting system on top of the DataWarehouse kept one person busy for about 3 weeks. The following products from SAS Institute were used in the project: { SAS Base { SAS/CONNECT { SAS/AF { SAS/SCL { SAS/EIS Motore

9 Fokus Bank DataWarehouse; Part 2: (Featuring CAD-reporting) According to the EU-regulations Fokus Bank has to comply with the Capital Adequacy Directive (CAD). A complete system had to be built and put in production no later then January Niklas Data was assigned to this project in September 96. The goals were to fulfill the EU-directive, as well as to establish a new part of the DataWarehouse containing information for later use in the financial risk management of the bank. Figure 6.: The technical environment for the project The first challenge in this project was to get information out of the different backoffice systems. Unlike the first part of the DataWarehouse project, we now had to access several different systems to get all the information we needed. The vendors of these systems gave us a hard time, denying to give out any information at all about their different databases without getting paid. As a result, we had to define reports on our own, saving them as textfiles or Excel-files and then at last reading the information into the SAS System. A second challenge was to secure consistency between the information from the different backoffice systems. Attributes like 'Sold' does not necessary imply the same status in two different transaction-systems. (Is stock sold when a deal is made, when you have given your stock away or when the opposite side actually has paid...) The third point worth mentioning is data quality. There is usually a gap between the 'as is' and the 'should be' situation in most systems of this kind. The standard definitions of registration are not always followed. A DataWarehouse depends on the information from the basic systems is correct. Garbage in - garbage out. This is especially important when Excel or other 'non-standard', user defined products are used as a backoffice system.

10 The experience to draw from this is, that the endusers of the basic systems have to take part in the specification of the data. If not, design with a 'should be' description is doomed to fail and cause a lot of extra work during a later stage in the project. Time Year Quarter Month Week Day Instrument Drawer Drawer Currency... Continent Country Equity... Debt... Currency Product Figure 7.: Logical data model (light version) The total DW, including the latest financial information, was now established. Like the first project, the information is structured according to future business needs defined by Fokus Bank. (See to figure 7.) SAS Base Quality check Generate CAD-data SAS Access Excel CAD Report xyz 100 wvy 292 sas 999 SAS DDE / Excel Macro Figure 8.: Export of CAD-data to Excel and production of CAD-report

11 The second part of the project was specification and automation of the production of CADreports to the government. The reporting tool is made on top of the DataWarehouse. During this job, the quality of the data is verified before the reports can be produced. Because the integration between the SAS System and Excel is so tight, we chose to use Excel Macros to put numbers from SAS Dataset into an empty Excel-report, then spreading these numbers out in templates defined by the government. This solution saved us a lot of time. The work consists of four parts: a.) Extraction of relevant information from the DataWarehouse b.) Structure the data according to the defined reports c.) Export the generated numbers to Excel d.) Distribute the numbers into pre-defined Excel templates Loading the DataWarehouse - producing CAD-report DEMO The user interface surrounding the proccess for verification of information, loading of the DataWarehouse and finally producing the CAD-report is made as simple as possible Collect info. Secure quality Updating missing info. Getting external info See the result Generate CADdata Control CADdata Export CAD-data Produce CADreport Figure 9: The main menu The main menu consists of two parts; part one deals with loading the DataWarehouse, part two produces the CAD-report. The two jobs are completely separated, but in this first pilot project there was now need to keep them apart. The final result of the second part is ready-to-deliver CAD-reports to the government. See figure 10 for an example.

12 CAD-report 17 CAD-report 18 Figure 10: Produced CAD-report - examples (To get an impression of this solution and its possibilities, see the presentation or feel free to get in touch with Niklas Data.) Time spent and resources used During the projects we worked closely with personnel from Fokus Bank. That is one of the main success factors in a project of this type where the lack of time is so apparent. This engagement was partly due to the engagement from the banks management, another factor we found to be critical for the success of the project. The deadline for the project was not negotiable, because any delays would mean that Fokus Bank had to pay a day to day fine. Without the cooperation and devotion from internal resources this project would not have turned out the way it did. The total use of resources from Niklas Data on this second project was 2 consultants full-time 90 days, with the help of three part-time workers each working about 3 weeks. Fokus Bank assigned one person full-time to help and coordinate the internal work in Fokus Bank the last month and a half. The following products from SAS Institute were used in the project: { SAS Base { SAS/ACCESS { SAS/AF { SAS/SCL

13 Where to go from here... Both of the projects still have many possibilities for further development. The first project, OLAP reporting, will all the financial reports to the Board. In addition to this, the application and relevant information will be spread throughout the whole organization. This has two reasons, one is better control of activities also in smaller units in the organization, the other the possibility to electronically test and correct the reported numbers from every part of the organization. Today this is done manually in a paperbased system, requiring 7 days of work. After this project Fokus Bank assumes that the finale confirmed numbers can be ready after 1-2 days, giving the Board a faster way to detect gaps between budget and actual numbers. The second project has even more exciting perspectives. The established financial part of the DataWarehouse can be used in financial risk management one way or the other. This, in comparison to the CAD-report, can give Fokus Bank a competitive advantage if used with knowledge and skill.with falling margins for retail banks, investment banking and therefore risk control is becoming more important. More and more banks see, that risk control is increasingly becoming the banks core business. What have we learned from these projects... Think big - START SMALL Without loosing your visions, start with only a manageable part of your mission critical information needs. Projects like these should be iterative, incremental projects, with every single one of them giving the organization measurable results. In such a way benefits gained will spill over from one project to the next, and the project owner can justify his/her investment continuously. Secure high level corporate sponsorship The project must be supported by the management. In that way, it is easier to get help from personnel in the rest of the organization who actually have to make an effort in making the project successful. 90% of the time is used in building the DataWarehouse When the DataWarehouse is established, building applications on top of it will in most cases not be very time consuming. That is, of course, if the tools are effective. The SAS System supports all of the phases involved in a DataWarehouse project, making the process seamless and effective. Never underestimate the gap between the as is and the should be operational data Do not trust the documentation, if any, of the operational data in the different basic systems. It is most likely that factors like typing errors, people not following standard definitions, no common understanding of definition attributes etc. etc. will make it necessary to do a job up in front, securing that the garbage in - garbage out situation will be avoided.

14 Building a logical datamodel according to the users business needs It is worth some extra time analysing the present, and future business needs, before the logical data model is considered finished. (Important subject and business areas must be identified.) It is not possible to get it all the first time around, but it saves a lot of work later if done properly the first time. The moment the user get a solution to play with, he or she will almost certainly see new exciting ways to exploit the information in the DataWarehouse. If the logical data model is designed skillfully, most of the solution generated ideas can be implemented without extending the DataWarehouse. Use the Rapid Warehousing Methodology The key success factor, covering all the above topics, is to have a good methodology and following it. This secures that all the checkpoints on the way are fulfilled and that a measurable result can be achieved in 90 days. (The key measures of success must be agreed upon and documented in the early phases of the project.) Acknowlegements SAS, SAS Base, SAS/ACCESS,SAS/CONNECT, SAS/AF, SAS/SCL and SAS/EIS are registrated trademarks of SAS Institute Inc., Cary, North Carolina. indicates USA registration. Contact Information Niklas Data AS Trollåsveien 4 Postboks 597 N-1411 Kolbotn NORWAY Phone: Fax: