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Part I (Information Management Today): Roughly 50 years ago, Russell Ackoff wrote about the importance, or lack of importance, of information that management needs in “Ackoff’s Management Information System” (see attachment). After reading the article, you will analyze how each assumption applies or does not apply to our society today. Treat each assumption separately in your analysis. As part of your response, provide a specific example for each assumption.
Part II (Information Management Revisited): After reading the Ackoff article, you will revise each of 5 assumptions to reflect a global, digital society. You will provide a summary (at least four sentences) that describe each revised assumption in detail and how they reflect our global, digital society.
Part III (Framework for Information Management): You will review the CPA Horizons Report (see attachment). After reviewing the report, you will describe how the CPA Horizons Report will help accountants to provide their managers with the right information at the right time in the right format. You will identify and describe three key skills that are not listed in the report that will allow accountants to produce the right information for their managers and their users. One of the skills you propose must be one that addresses the advances in technology. Finally, you will explain how your degree from UMGC and this class will help you to ‘embrace the future’ as noted in the report.
Page Length: Your response should not exceed eight pages (double-spaced) or four pages (single-spaced). Note that cover page, reference page, and appendix, if provided are excluded from the page count. References: You will need to include at least four literary references (one reference must be from the readings in our class) and at least two in-text citations to support your paper. These references must be related directly to the topics covered in the paper. References & citations must be properly formatted as noted below. Headings: You will need to use headings (short, brief, and centered) to separate each area of your paper. Your headings should have an appropriate title such as Information Management Today instead of Part I. Margins & Font Sizes: Use standard margins (minimum .5″; maximum 1.5″) and standard font size (minimum 10 point; maximum 12 point) in your paper. Writing Style: APA is the preferred writing style, but you can choose any appropriate writing style (e.g. MLA), except that all references are to be formatted via APA. Please consult the UMUC Effective Writing Center (http://www.umuc.edu/writingcenter/index.cfm) for assistance regarding the choice of styles and formatting of references via APA.
Part I (Information Management Today): Roughly 50 years ago, Russell Ackoff wrote about the importance, or lack of importance, of information that management needs in “Ackoff’s Management Information
Management Misinformation Systems Author(syf 5 X V V H O O / $ F N R I f Source: Management Science, Vol. 14, No. 4, Application Series (Dec., 1967yf S S % % 6 Published by: INFORMS Stable URL: http://www.jstor.org/stable/2628680 Accessed: 03-07-2018 01:02 UTC JSTOR is a not-for-profit service that helps scholars, researchers, and students discover, use, and build upon a wide range of content in a trusted digital archive. We use information technology and tools to increase productivity and facilitate new forms of scholarship. For more information about JSTOR, please contact [email protected] Your use of the JSTOR archive indicates your acceptance of the Terms & Conditions of Use, available at http://about.jstor.org/terms INFORMS is collaborating with JSTOR to digitize, preserve and extend access to Management Science This content downloaded from 126.96.36.199 on Tue, 03 Jul 2018 01:02:59 UTC All use subject to http://about.jstor.org/terms MANAGEMENT SCIENCE Vol. 14, No. 4, December, 1967 Printed in U.S.A. MANAGEMENT MISINFORMATION SYSTEMS * RUSSELL L. ACKOFF University of Pennsylvania Five assumptions commonly made by designers of management information systems are identified. It is argued that these are not justified in many (if not mostyf F D V H V D Q G K H Q F H O H D G W R P D M R U G H I L F L H Q F L H V L Q W K H U H V X O W L Q J V V W H P V . These assumptions are: (1yf W K H F U L W L F D O G H I L F L H Q F X Q G H U Z K L F K P R V W P D Q D J H U s operate is the lack of relevant information, (2yf W K H P D Q D J H U Q H H G V W K H L Q I R U – mation he wants, (3yf L I D P D Q D J H U K D V W K H L Q I R U P D W L R Q K H Q H H G V K L V G H F L V L R n making will improve, (4yf E H W W H U F R P P X Q L F D W L R Q E H W Z H H Q P D Q D J H U V L P S U R Y H s organizational performance, and (5yf D P D Q D J H U G R H V Q R W K D Y H W R X Q G H U V W D Q d how his information system works, only how to use it. To overcome these assumptions and the deficiencies which result from them, a management information system should be imbedded in a management control system. A procedure for designing such a system is proposed and an example is given of the type of control system which it produces. The growing preoccupation of operations researchers and management scien- tists with Management Information Systems (MIS’syf L V D S S D U H Q W , Q I D F W I R r some the design of such systems has almost become synonymous with operations research or management science. Enthusiasm for such systems is understand- able: it involves the researcher in a romantic relationship with the most glamorous instrument of our time, the computer. Such enthusiasm is understandable but, nevertheless, some of the excesses to which it has led are not excusable. Contrary to the impression produced by the growing literature, few com- puterized management information systems have been put into operation. Of those I’ve seen that have been implemented, most have not matched expectations and some have been outright failures. I believe that these near- and far-misses could have been avoided if certain false (and usually implicityf D V V X P S W L R Q V R n which many such systems have been erected had not been made. There seem to be five common and erroneous assumptions underlying the design of most MIS’s, each of which I will consider. After doing so I will outline an MIS design procedure which avoids these assumptions. Give Them More Most MIS’s are designed on the assumption that the critical deficiency under which most managers operate is the lack of relevant information. I do not deny that most managers lack a good deal of information that they should have, but I do deny that this is the most important informational deficiency from which they suffer. It seems to me that they suffer more from an over abundance of irrelevant information. * Received June 1967. B-147 This content downloaded from 188.8.131.52 on Tue, 03 Jul 2018 01:02:59 UTC All use subject to http://about.jstor.org/terms B-148 RUSSELL L. ACKOFF This is not a play on words. The consequences of changing the emphasis of an MIS from supplying relevant information to eliminating irrelevant information is considerable. If one is preoccupied with supplying relevant information, attention is almost exclusively given to the generation, storage, and retrieval of information: hence emphasis is placed on constructing data banks, coding, indexing, updating files, access languages, and so on. The ideal which has emerged from this orientation is an infinite pool of data into which a manager can reach to pull out any information he wants. If, on the other hand, one sees the manager’s information problem primarily, but not exclusively, as one that arises out of an overabundance of irrelevant information, most of which was not asked for, then the two most important functions of an information system become filtration (or evaluationyf D Q G F R Q G H Q V D W L R Q 7 K H O L W H U D W X U H R Q 0 , 6 V V H O G R P U H I H U V W R W K H V e functions let alone considers how to carry them out. My experience indicates that most managers receive much more data (if not informationyf W K D Q W K H F D Q S R V V L E O D E V R U E H Y H Q L I W K H V S H Q G D O O R I W K H L U W L P e trying to do so. Hence they already suffer from an information overload. They must spend a great deal of time separating the relevant form the irrelevant and searching for the kernels in the relevant documents. For example, I have found that I receive an average of forty-three hours of unsolicited reading material each week. The solicited material is usually half again this amount. I have seen a daily stock status report that consists of approximately six hundred pages of computer print-out. The report is circulated daily across man- agers’ desks. I’ve also seen requests for major capital expenditures that come in book size, several of which are distributed to managers each week. It is not uncommon for many managers to receive an average of one journal a day or more. One could go on and on. Unless the information overload to which managers are subjected is reduced, any additional information made available by an MIS cannot be expected to be used effectively. Even relevant documents have too much redundancy. Most documents can be considerably condensed without loss of content. My point here is best made, perhaps, by describing briefly an experiment that a few of my colleagues and I conducted on the OR literature several years ago. By using a panel of well-known experts we identified four OR articles that all members of the panel considered to be “above average,” and four articles that were considered to be “below average.” The authors of the eight articles were asked to prepare “objective” examinations (duration thirty minutesyf S O X V D Q V Z H U V I R U J U D G X D W H V W X G H Q W V Z K o were to be assigned the articles for reading. (The authors were not informed about the experiment.yf 7 K H Q V H Y H U D O H [ S H U L H Q F H G Z U L W H U V Z H U H D V N H G W R U H G X F H H D F h article to 2 and 3 of its original length only by eliminating words. They also prepared a brief abstract of each article. Those who did the condensing did not see the examinations to be given to the students. A group of graduate students who had not previously read the articles were then selected. Each one was given four articles randomly selected, each of which This content downloaded from 184.108.40.206 on Tue, 03 Jul 2018 01:02:59 UTC All use subject to http://about.jstor.org/terms MANAGEMENT MISINFORMATION SYSTEMS B-149 was in one of its four versions: 100yb b, 33 yb R U D E V W U D F W ( D F K Y H U V L R Q R f each article was read by two students. All were given the same examinations. The average scores on the examinations were then compared. For the above-average articles there was no significant difference between average test scores for the 100yb b, and 33 yb Y H U V L R Q V E X W W K H U H Z D V a significant decrease in average test scores for those who had read only the abstract. For the below-average articles there was no difference in average test scores among those who had read the 100 yb b, and 33 yb Y H U V L R Q V E X W W K H U e was a significant increase in average test scores of those who had read only the abstract. The sample used was obviously too small for general conclusions but the results strongly indicate the extent to which even good writing can be condensed without loss of information. I refrain from drawing the obvious conclusion about bad writing. It seems clear that condensation as well as filtration, performed mechanically or otherwise, should be an essential part of an MIS, and that such a system should be capable of handling much, if not all, of the unsolicited as well as solicited information that a manager receives. The Manager Needs the Information That He Wants Most MIS designers “determine” what information is needed by asking managers what information they would like to have. This is based on the as- sumption that managers know what information they need and want it. For a manager to know what information he needs he must be aware of each type of decision he should make (as well as doesyf D Q G K H P X V W K D Y H D Q D G H T X D W e model of each. These conditions are seldom satisfied. Most managers have some conception of at least some of the types of decisions they must make. Their conceptions, however, are likely to be deficient in a very critical way, a way that follows from an important principle of scientific economy: the less we under- stand a phenomenon, the more variables we require to explain it. Hence, the manager who does not understand the phenomenon he controls plays it “safe” and, with respect to information, wants “everything.” The MIS designer, who has even less understanding of the relevant phenomenon than the manager, tries to provide even more than everything. He thereby increases what is already an overload of irrelevant information. For example, market researchers in a major oil company once asked their marketing managers what variables they thought were relevant in estimating the sales volume of future service stations. Almost seventy variables were identified. The market researchers then added about half again this many variables and performed a large multiple linear regression analysis of sales of existing stations against these variables and found about thirty-five to be statis- tically significant. A forecasting equation was based on this analysis. An OR team subsequently constructed a model based on only one of these variables, traffic flow, which predicted sales better than the thirty-five variable regression equation. The team went on to explain sales at service stations in terms of the This content downloaded from 220.127.116.11 on Tue, 03 Jul 2018 01:02:59 UTC All use subject to http://about.jstor.org/terms B-150 RUSSELL L. ACKOFF customers’ perception of the amount of time lost by stoppong for service. The relevance of all but a few of the variables used by the market researchers could be explained by their effect on such perception. The moral is simple: one cannot specify what information is required for decision making until an explanatory model of the decision process and the system involved has been constructed and tested. Information systems are subsystems of control systems. They cannot be designed adequately without taking control in account. Furthermore, whatever else regression analyses can yield, they cannot yield understanding and explanation of phenomena. They describe and, at best, predict. Give a Manager the Information He Needs and His Decision Making Will Improve It is frequently assumed that if a manager is provided with the information he needs, he will then have no problem in using it effectively. The history of OR stands to the contrary. For example, give most managers an initial tableau of a typical “real” mathematical programming, sequencing, or network problem and see how close they come to an optimal solution. If their experience and judgment have any value they may not do badly, but they will seldom do very well. In most management problems there are too many possibilities to expect experience, judgement, or intuition to provide good guesses, even with perfect information. Furthermore, when several probabilities are involved in a problem the un- guided mind of even a manager has difficulty in aggregating them in a valid way. We all know many simple problems in probability in which untutored intuition usually does very badly (e.g., What are the correct odds that 2 of 25 people selected at random will have their birthdays on the same day of the year?yf . For example, very few of the results obtained by queuing theory, when arrivals and service are probabilistic, are obvious to managers; nor are the results of risk analysis where the managers’ own subjective estimates of probabilities are used. The moral: it is necessary to determine how well managers can use needed information. When, because of the complexity of the decision process, they can’t use it well, they should be provided with either decision rules or perform- ance feed-back so that they can identify and learn from their mistakes. More on this point later. More Communication Means Better Performance One characteristic of most MIS’s which I have seen is that they provide managers with better current information about what other managers and their departments and divisions are doing. Underlying this provision is the belief that better interdepartmental communication enables managers to coordinate their decisions more effectively and hence improves the organization’s overall performance. Not only is this not necessarily so, but it seldom is so. One would hardly expect two competing companies to become more cooperative because This content downloaded from 18.104.22.168 on Tue, 03 Jul 2018 01:02:59 UTC All use subject to http://about.jstor.org/terms MANAGEMENT MISINFORMATION SYSTEMS B-151 the information each acquires about the other is improved. This analogy is not as far fetched as one might first suppose. For example, consider the following very much simplified version of a situation I once ran into. The simplification of the case does not affect any of its essential characteristics. A department store has two “line” operations: buying and selling. Each function is performed by a separate department. The Purchasing Department primarily controls one variable: how much of each item is bought. The Merchan- dising Department controls the price at which it is sold. Typically, the measure of performance applied to the Purchasing Department was the turnover rate of inventory. The measure applied to the Merchandising Department was gross sales; this department sought to maximize the number of items sold times their price. Now by examining a single item let us consider what happens in this system. The merchandising manager, using his knowledge of competition and con- sumption, set a price which he judged would maximize gross sales. In doing so he utilized price-demand curves for each type of item. For each price the curves show the expected sales and values on an upper and lower confidence band as well. (See Figure 1.yf : K H Q L Q V W U X F W L Q J W K H 3 X U F K D V L Q J ‘ H S D U W P H Q W K R Z P D Q y items to make available, the merchandising manager quite naturally used the value on the upper confidence curve. This minimized the chances of his running short which, if it occurred, would hurt his performance. It also maximized the chances of being over-stocked but this was not his concern, only the purchasing manager’s. Say, therefore, that the merchandising manager initially selected price P1 and requested that amount Q, be made available by the Purchasing Department. In this company the purchasing manager also had access to the price-demand curves. He knew the merchandising manager always ordered optimistically. Z o I E Q2 –u- 03 0 1 I E~SS4IM IC Pt P2 P3 PRICE FIGURE, 1. Price-demand curve This content downloaded from 22.214.171.124 on Tue, 03 Jul 2018 01:02:59 UTC All use subject to http://about.jstor.org/terms B-152 RUSSELL L. ACKOFF Therefore, using the same curve he read over from Qi to the upper limit and down to the expected value from which he obtained Q2, the quantity he actually intended to make available. He did not intend to pay for the merchandising manager’s optimism. If merchandising ran out of stock, it was not his worry. Now the merchandising manager was informed about what the purchasing manager had done so he adjusted his price to P2. The purchasing manager in turn was told that the merchandising manager had made this readjustment so he planned to make only Q3 available. If this process-made possible only by perfect communication between departments-had been allowed to continue, nothing would have been bought and nothing would have been sold. This out- come was avoided by prohibiting communication between the two departments and forcing each to guess what the other was doing. I have obviously caricatured the situation in order to make the point clear: when organizational units have inappropriate measures of performance which put them in conflict with each other, as is often the case, communication be- tween them may hurt organizational performance, not help it. Organizational structure and performance measurement must be taken into account before opening the flood gates and permitting the free flow of information between parts of the organization. (A more rigorous discussion of organizational structure and the relationship of communication to it can be found in .yf A Manager Does Not Have to Understand How an Information System Works, Only How to Use It Most MIS designers seek to make their systems as innocuous and unobtrusive as possible to managers lest they become frightened. The designers try to provide managers with very easy access to the system and assure them that they need to know nothing more about it. The designers usually succeed in keeping man- agers ignorant in this regard. This leaves managers unable to evaluate the MIS as a whole. It often makes them afraid to even try to do so lest they display their ignorance publicly. In failing to evaluate their MIS, managers delegate much of the control of the organization to the system’s designers and operators who may have many virtues, but managerial competence is seldom among them. Let me cite a case in point. A Chairman of a Board of a medium-size company asked for help on the following problem. One of his larger (decentralizedyf G L Y L – sions had installed a computerized production-inventory control and manu- facturing-manager information system about a year earlier. It had acquired about $2,000,000 worth of equipment to do so. The Board Chairman had just received a request from the Division for permission to replace the original equipment with newly announced equipment which would cost several times the original amount. An extensive “justification” for so doing was provided with the request. The Chairman wanted to know whether the request was really justified. He admitted to complete incompetence in this connection. A meeting was arranged at the Division at which I was subjected to an ex- tended and detailed briefing. The system was large but relatively simple. At the heart of it was a reorder point for each item and a maximum allowable This content downloaded from 126.96.36.199 on Tue, 03 Jul 2018 01:02:59 UTC All use subject to http://about.jstor.org/terms MANAGEMENT MISINFORMATION SYSTEMS B-153 stock level. Reorder quantities took lead-time as well as the allowable maximum into account. The computer kept track of stock, ordered items when required and generated numerous reports on both the state of the system it controlled and its own “actions.” When the briefing was over I was asked if I had any questions. I did. First I asked if, when the system had been installed, there had been many parts whose stock level exceeded the maximum amount possible under the new system. I was told there were many. I asked for a list of about thirty and for some graph paper. Both were provided. With the help of the system designer and volumes of old daily reports I began to plot the stock level of the first listed item over time. When this item reached the maximum “allowable” stock level it had been reordered. The system designer was surprised and said that by sheer “luck” I had found one of the few errors made by the system. Continued plotting showed that because of repeated premature reordering the item had never gone much below the maximum stock level. Clearly the program was confusing the maximum allowable stock level and the reorder point. This turned out to be the case in more than half of the items on the list. Next I asked if they had many paired parts, ones that were only used with each other; for example, matched nuts and bolts. They had many. A list was pro- duced and we began checking the previous day’s withdrawals. For more than half of the pairs the differences in the numbers recorded as withdrawn were very large. No explanation was provided. Before the day was out it was possible to show by some quick and dirty calculations that the new computerized system was costing the company almost $150,000 per month more than the hand system which it had replaced, most of this in excess inventories. The recommendation was that the system be redesigned as quickly as pos- sible and that the new equipment not be authorized for the time being. The questions asked of the system had been obvious and simple ones. Man- agers should have been able to ask them but-and this is the point-they felt themselves incompetent to do so. They would not have allowed a handoperated system to get so far out of their control. No MIS should ever be installed unless the managers for whom it is intended are trained to evaluate and hence control it rather than be controlled by it. A Suggested Procedure for Designing an MIS The erroneous assumptions I have tried to reveal in the preceding discussion can, I believe, be avoided by an appropriate design procedure. One is briefly outlined here. 1. Analysis Of The Decision System Each (or at least each importantyf W S H R I P D Q D J H U L D O G H F L V L R Q U H T X L U H G E W K e organization under study should be identified and the relationships between them should be determined and flow-charted. Note that this is not necessarily the same thing as determining what decisions are made. For example, in one com- This content downloaded from 188.8.131.52 on Tue, 03 Jul 2018 01:02:59 UTC All use subject to http://about.jstor.org/terms B-154 RUSSELL L. ACKOFF pany I found that make-or-buy decisions concerning parts were made only at the time when a part was introduced into stock and was never subsequently reviewed. For some items this decision had gone unreviewed for as many as twenty years. Obviously, such decisions should be made more often; in some cases, every time an order is placed in order to take account of current shop loading, underused shifts, delivery times from suppliers, and so on. Decision-flow analyses are usually self-justifying. They often reveal important decisions that are being made by default (e.g., the make-buy decision referred to aboveyf D Q G W K H G L V F O R V H L Q W H U G H S H Q G H Q W G H F L V L R Q V W K D W D U H E H L Q J P D G H L Q – dependently. Decision-flow charts frequently suggest changes in managerial responsibility, organizational structure, and measure of performance which can correct the types of deficiencies cited. Decision analyses can be conducted with varying degrees of detail, that is, they may be anywhere from coarse to fine grained. How much detail one should become involved with depends on the amount of time and resources that are available for the analysis. Although practical considerations frequently restrict initial analyses to a particular organizational function, it is preferable to perform a coarse analysis of all of an organization’s managerial functions rather than a fine analysis of one or a subset of functions. It is easier to introduce finer in- formation into an integrated information system than it is to combine fine sub- systems into one integrated system. 2. An Analysis Of Information Requirements Managerial decisions can be classified into three types: (ayf ‘ H F L V L R Q V I R U Z K L F K D G H T X D W H P R G H O V D U H D Y D L O D E O H R U F D Q E H F R Q V W U X F W H d and from which optimal (or near optimalyf V R O X W L R Q V F D Q E H G H U L Y H G , Q V X F h cases the decision process itself should be incorporated into the information system thereby converting it (at least partiallyyf W R D F R Q W U R O V V W H P $ G H F L V L R n model identifies what information is required and hence what information is relevant. (byf ‘ H F L V L R Q V I R U Z K L F K D G H T X D W H P R G H O V F D Q E H F R Q V W U X F W H G E X W I U R P Z K L F h optimal solutions cannot be extracted. Here some kind of heuristic or search procedure should be provided even if it consists of no more than computerized trial and error. A simulation of the model will, as a minimum, permit comparison of proposed alternative solutions. Here too the model specifies what information is required. (cyf ‘ H F L V L R Q V I R U Z K L F K D G H T X D W H P R G H O V F D Q Q R W E H F R Q V W U X F W H G 5 H V H D U F K L s required here to determine what information is relevant. If decision making cannot be delayed for the completion of such research or the decision’s effect is not large enough to justify the cost of research, then judgment must be used to “guess” what information is relevant. It may be possible to make explicit the implicit model used by the decision maker and treat it as a model of type (byf . In each of these three types of situation it is necessary to provide feedback by comparing actual decision outcomes with those predicted by the model or decision maker. Each decision that is made, along with its predicted outcome, This content downloaded from 184.108.40.206 on Tue, 03 Jul 2018 01:02:59 UTC All use subject to http://about.jstor.org/terms MANAGEMENT MISINFORMATION SYSTEMS B-155 should be an essential input to a management control system. I shall return to this point below. S. Aggregation Of Decisions Decisions with the same or largely overlapping informational requirements should be grouped together as a single manager’s task. This will reduce the information a manager requires to do his job and is likely to increase his under- standing of it. This may require a reorganization of the system. Even if such a reorganization cannot be implemented completely what can be done is likely to improve performance significantly and reduce the information loaded on man- agers. 4. Design Of Information Processing Now the procedure for collecting, storing, retrieving, and treating information can be designed. Since there is a voluminous literature on this subject I shall leave it at this except for one point. Such a system must not only be able to answer questions addressed to it; it should also be able to answer questions that have not been asked by reporting any deviations from expectations. An extensive exception-reporting system is required. 5. Design Of Control Of The Control System It must be assumed that the system that is being designed will be deficient in many and significant ways. Therefore it is necessary to identify the ways in which it may be deficient, to design procedures for detecting its deficiencies, and for correcting the system so as to remove or reduce them. Hence the system should be designed to be flexible and adaptive. This is little more than a platitude, but it has a not-so-obvious implication. No completely computerized system can be as flexible and adaptive as can a man-machine system. This is illustrated by a concluding example of a system that is being developed and is partially in operation. (See Figure 2.yf The company involved has its market divided into approximately two hundred marketing areas. A model for each has been constructed as is “in” the computer. On the basis of competivive intelligence supplied to the service marketing manager by marketing researchers and information specialists he and his staff make policy decisions for each area each month. Their tentative decisions are fed into the computer which yields a forecast of expected performance. Changes are made until the expectations match what is desired. In this way they arrive at “final” decisions. At the end of the month the computer compares the actual performance of each area with what was predicted. If a deviation exceeds what could be expected by chance, the company’s OR Group then seeks the reason for the deviation, performing as much research as is required to find it. If the cause is found to be permanent the computerized model is adjusted appropriately. The result is an adaptive man-machine system whose precision and generality is continuously increasing with use. Finally it should be noted that in carrying out the design steps enumerated This content downloaded from 220.127.116.11 on Tue, 03 Jul 2018 01:02:59 UTC All use subject to http://about.jstor.org/terms B-156 RUSSELL L. ACKOFF PROPOSED POLICIES MARKETING EVALUATED PROPOSALS MARKET AREA MEMORY a MODELS COM PARATOR MANAGEMENT SELECTED POLICY (COMPUTERyf 3 5 ( ‘ , & 7 ( ‘ & 2 0 3 8 7 ( 5 f OUTCOME 2 Ui INFORMATION F INQUIRIES Z -j2 <~~~~~~~~~~~ o~~~~~~~~~~~~~~~~~~~ UJ oz DATA D FIELD _MOTHERA SOURCES MARKET DATA OPERATIONS DEVIANT AREAS INFORMATION INFORMATION RESEARCH SYSTEM GROUP M ~~~~~~DATA oz 1 FIELD DATA ACTUAL PERFORMANCE MARKET SELLINGARS FORCEARS FIGURE 2. Simplified diagram of a market-area control system above, three groups should collaborate: information systems specialists, oper- ations researchers, and managers. The participation of managers in the design of a system that is to serve them, assures their ability to evaluate its performance by comparing its output with what was predicted. Managers who are not willing to invest some of their time in this process are not likely to use a management control system well, and their system, in turn, is likely to abuse them. Reference 1. SENGUPTA, S. S., AND ACKOFF, R. L., “Systems Theory from an Operations Research Point of View,” IEEE Transactions on Systems Science and Cybernetics, Vol. 1 (Nov. 1965yf S S . This content downloaded from 18.104.22.168 on Tue, 03 Jul 2018 01:02:59 UTC All use subject to http://about.jstor.org/terms
Part I (Information Management Today): Roughly 50 years ago, Russell Ackoff wrote about the importance, or lack of importance, of information that management needs in “Ackoff’s Management Information
Part I (Information Management Today): Roughly 50 years ago, Russell Ackoff wrote about the importance, or lack of importance, of information that management needs in “Ackoff’s Management Information
2019 Angela Carrington Vice President IBM Global Business Services Ira Gebler Partner IBM Global Business Services Financial Management for The Future: How Government Can Evolve to Meet the Demands of a Digital World 2 CO-CONTRIBUTORS TO THIS REPORT INCLUDE: • Phil Howe • Karin O’Leary with additional support from: • Mark Fisk • Susan Kann • Mike Libutti • Howard Osborne • Jason Prow • Claude Yusti 3 TABLE OF CONTENTS Introduction ……………………………………………………………… ….. 4 Shared Services …………………………………………………………….. 6 Robotic Process Automation …………………………………………… 10 Blockchain ……………………………………………………………… ….. 12 Artificial Intelligence ……………………………………………………… 15 Conclusion ……………………………………………………………… ….. 17 4 ANGELA CARRINGTON IRA GEBLER The Federal Financial Management community has made significant progress in improving the cost, quality, and performance of its systems. Initiatives directed and sponsored by Office of Management and Budget (OMB), General Services Administration (GSA), and the Treasury Department have provided the incentives and guidance necessary to build upon that progress. However, many agencies continue to face chal – lenges meeting certain standards for accounting and reporting, and continue to use outdated finan – cial systems that minimally support their financial performance and accountability. Moreover, some agencies still use legacy financial systems that feed their core Enterprise Resource Planning (ERP) system. Many of these systems are old, outdated, and costly to maintain. Additional efforts to improve financial systems through upgrades or replacement of legacy technology in an effective, efficient, and transparent manner will help agen – cies realize the full value of their investment in ERP systems. In 2010, the IBM Center for The Business of Government published What We Know Now: A Look into Lessons Learned Implementing Federal Financial Systems Projects . That special report presented ten principles designed to provide insight into how to best deploy financial manage – ment systems, with a focus on optimizing resources and information. The principles were based upon lessons learned from multiple financial management system deployments throughout the public sector and remain relevant today: • Engage stakeholders; • Simplify processes; • Plan acquisitions; • Tighten scope; • Commit resources; INTRODUCTION IMPROVING FEDERAL FINANCIAL SYSTEMS: PROGRESS MADE, MORE TO DO • Manage proactively; • Work together; • Guide change; • Conduct reviews; and • Test thoroughly. 5 ANGELA CARRINGTON IRA GEBLER Since that publication, new opportunities and technologies have rapidly appeared. Cloud computing and shared services are becoming common – place in the public sector. ERP vendors are beginning to encourage clients to move to the cloud by adding higher-end capabilities and announcing end-dates for on-premises systems support. In addition, new technologies and capabilities, including robotic process automation (RPA), blockchain, and artificial intelligence (AI), promise to enable significant gains in pro – ductivity. The automation of RPA, the trust and security enabled by block – chain, and the cost savings provided by shared services can all deliver significant business value. We hope that this special report, Financial Management for The Future: How Government Can Evolve to Meet the Demands of a Digital World, will help government leaders and stakeholders capitalize on the promise of new technologies and business practices to increase the value of govern – ment financial systems. Angela Carrington Vice President IBM Global Business Services [email protected] Ira Gebler Partner IBM Global Business Services [email protected] 6 FINANCIAL MANAGEMENT FOR THE FUTURE: HOW GOVERNMENT CAN EVOLVE TO MEET THE DEMANDS OF A DIGITAL WORLD This special report identifies and describes new opportunities that Chief Financial Officers (CFOs), Chief Information Officers (CIOs), and their colleagues and stakeholders can leverage to better position their agencies for: • Shared services; • Robotic process automation; • Blockchain; and • Artificial intelligence. SHARED SERVICES Federal shared services efforts have advanced for several decades. These efforts moved forward significantly with the release of OMB’s March 2013 Memorandum M-13-08, Improving Financial Systems through Shared Ser vices, and the designation of certain Federal agencies as approved shared service providers for Financial Management across the Federal government. In October 2015, OMB and GSA announced the first govern – ment-wide operating model for shared services, including the estab – lishment of the Shared Services Governance Board (SSGB) and the Unified Shared Services Management (USSM) office (now part of the GSA Office of Shared Solutions and Performance Improvement OSSPI). This model was intended to facilitate the delivery of high-quality shared services to improve performance and efficiency throughout the government, with the SSGB driving strategic direction and the USSM responsible for execution. Ongoing initiatives and progress OMB’s May 2016 Memorandum M-16-11, Improving Financial Systems through Shared Ser vices, institutionalized many ongoing initiatives and progress, including: • Establishing the Federal Integrated Business Framework (FIBF), which serves as a model that enables the Federal government to better coordinate and document common business needs across agencies, and to focus on outcomes, data, processes, and performance. The FIBF is the essential first step towards standards that will drive economies of scale, simplify processes, and leverage the government’s buying power. More information about FIBF initiatives may be found here: https://www.ussm.gov/ fibf/. • Introducing an investment review process for Financial Manage – ment (FM), Human Resources (HR), and acquisitions utilizing shared services. • Collaborating with the Office of Federal Procurement Policy to 7 develop a governmentwide management and acquisition strategy for shared solutions. • Promoting increased accountability for shared service delivery by managing the new ProviderStat process, a data-driven and ongoing review process used to assess cost, quality, and perfor – mance metrics and ultimately drive agency budget development. • Publishing an implementation playbook of best practices and lessons learned from across government. • Aligning investment reviews to the Federal budget cycle. • Fostering greater collaboration among Federal agencies to prepare a demand analysis for FM and HR, allowing shared service providers to plan for increased demand. • Identifying requirements, assessment criteria, and a designation process for allowing new entrants into the supply marketplace. To date, many agencies have successfully moved their financial sys – tems to shared services providers. However, some implementations have faced challenges, and several agencies have even moved to other providers or brought their systems back in-house. The push to shared services continues Despite such challenges, the 2017 Executive Order on Cybersecurity (https://www.whitehouse.gov/presidential-actions/presidential-execu – tive-order-strengthening-cybersecurity-federal-networks-critical-infra – structure) and the 2018 Modernizing Government Technology Act (Public Law No. 115-91, Subtitle G, Secs 1076-1078) both reiter – ated the push for Federal agencies to adopt the use of shared ser – vices. In December 2018, OMB refreshed its shared service strategy outlined in the most recent President’s Management Agenda (https:// www.whitehouse.gov/omb/management/pma) . Along with the strategy update, agencies are now encouraged to become more involved in the development of standards in order to drive future shared services capabilities and solutions. Sharing quality services is identified as a cross-agency priority (CAP) goal, targeting those areas where multiple agencies must collaborate to effect change and report progress in a 8manner the public can easily track. The sharing quality services CAP goal states, “The federal government will establish a strategic govern- ment-wide framework for improving the effectiveness and efficiency of administrative services by 2020, leading to continual improvements in performance and operational cost savings of 20 percent annually at scale—or an estimated $2 billion over the next 10 years.” On April 26, 2019, OMB issued a new directive ordering significant realignment in shared services, M-19-26 Centralized Mission Support Capabilities for the Federal Government (https://www.white- house.gov/wp-content/uploads/2019/04/M-19-16.pdf). The memo designated the following agencies as Quality Services Management Offices (QSMOs) to lead broad categories of shared services: GSA (HR), the Department of Health and Human Service (HHS) (Grants Management), the Department of Homeland Security (DHS) (Cybersecurity), and Treasury (FM). Each QSMO agency has a senior agency point of contact (SAPOC), drives standards, and develops a five-year plan. In the future, other agencies will not be permitted to pursue stand-alone modernizations in those areas without approval from the QSMO . Federal agencies can accelerate progress by building on prior govern- ment efforts while also taking bold new steps. Bold steps include accelerating implementation of shared services within and across departments, where economies of scale can be leveraged to perform common financial management and IT activities. Moreover, Federal CIOs and CFOs where agencies or divisions share a common ERP system have an opportunity to re-shape their workforce assumptions; as shared services providers have demonstrated the ability to drive savings for the U.S. taxpayer, Federal workforces can focus more effort on mission support and analytic tasks. Also in the HR arena, the government’s modernization progress in shared services has been built on decades of work to define governance structures and busi – ness needs and to establish a marketplace and rules of competition. Through these and similar efforts, agencies can transform the status quo of legacy IT systems that are expensive to maintain and nearly impossible to modernize. A major challenge to accelerating the “as a service” model is change management. Several factors have contributed to antiquated agency financial systems—from project funding and scale, to having leader – ship aligned and willing to drive the charge. Yet the comfort of exist – ing business processes and customization often limits agencies in modernizing their financial systems. This makes continuous stake- holder engagement critical to guiding the change. As agencies embrace the “as-a-service” model and/or consolidate routine or stan- dard operations to a small number of organizations, savings can be redirected to core mission areas. Moreover, the path to an “as-a-ser – vice” model might include an agency moving its technology and peo – ple into a central services office, and planning for further modernizations and transformation. While this delivers proven effi- ciencies and savings, long-term benefits will emerge from establishing a future state financial services vision, and proactively leading people and managing solutions to achieve that vision. Another key consider – ation for successful change management is making sure the right resources are committed and the scope of the effort is well defined. 9 Financial Management Line of Business establishes standards The government has made significant progress in FM through the Financial Management Line of Business (FM LoB) by establishing standards in the following areas: • Federal business lifecycles, service areas, functions, and activi – ties, which serve as the basis for a common understanding of what services agencies need and what the solutions should offer. • Business capabilities, which are the outcome-based business needs mapped to federal government authoritative references, forms, and data standards. • Business use cases, which offer a set of agency “stories” that document the key activities, inputs, outputs, and other LoB intersections to describe how the government operates. In addition, the FM LoB is continuing FM Federal Integrated Business Framework (FIBF) efforts in the following areas: • Standard data elements, which identify the minimum data fields required to support the inputs and outputs noted in the use cases and capabilities. • Performance metrics to define how the government measures successful delivery of outcomes based on timeliness, efficiency, and accuracy targets. These standards and tools make action imperative in considering FM shared services. Agencies can leverage processes used to create the FIBF business requirements and functional areas, in order to assess the capabilities of providers to deliver services required by customer agencies (“what” is delivered), not on the software the provider offers (the “how”). This gives agencies the ability to define services needed, and offers providers flexibility to differentiate their offerings while complying with established standards. This standardized model of technology and business process support constitutes one of the most important factors for successful implementation of shared services, and will help the government accelerate its transformation. Moreover, by emphasizing the “as a service” approach, agencies can buy dynamic cloud-based services and not static IT systems. Cloud strategies are a key consideration for agencies to consider when assessing not only the demands but also the opportunities of a digital world. A well-defined cloud strategy has proven to be more successful when jointly developed by all department leaders working, collaborat – ing, and engaging together. To assist with the process of moving to “as-a-service” solutions, agen – cies should consider utilizing not just FMLoB assets like templates or process models, but also staff who have successfully built out a LoB and demonstrated real progress (e.g., number of agencies migrated) over the long term. 10 ROBOTIC PROCESS AUTOMATION RPA is a popular topic among today’s government agencies, and for good reason—the digital worker can deliver real business value via improved productivity, compliance, and accuracy, while reducing the cost of public service. In August 2018, OMB encouraged agencies to introduce new technologies, like RPA, to reduce repetitive administra – tive tasks in Memorandum M-18-23, Shifting from Low-Value to High-Value Work (https://www.whitehouse.gov/wp-content/ uploads/2018/08/M-18-23.pdf ). RPA processes rules-based, structured data through a user interface of robotic software—supporting for instance, repetitive data entry functions and ERP downloads and uploads. While its benefits are real, RPA software generally executes steps for expected, default sce – narios or process flows, and does not always handle exceptions, make decisions, and adapt. RPA provides the foundation on which to build the digital employee. When integrated with cognitive capabilities like advanced analytics and artificial intelligence, advanced RPA implementations can enable intelligent automation with the potential to enhance digital workforce productivity. This intelligent automation evolves the digital workforce from a simple, process-driven team of task executers, to an orches – trated team capable of decision-making, evaluating, and self-healing to continuously improve. Examples of RPA in action • Reducing backlogs in the validation and reconciliation process. A defense agency identified and removed manual backlogs in the validation and reconciliation process for its annual cost analysis update. With an eye on discovery, transparency, and improve – ment, the agency determined that 80 percent of the process steps could be automated. An RPA prototype was developed to reconcile and validate data to improve cleanliness, reduce complexity and manual review time, and increase audit readiness through the creation of audit files and data governance. This 11 automation can be developed in less than a week, reconciles and validates 12 of 20 data provider files, follows 40 different business rules, supports multiple data formats, and completes a previous 30-60 minute manual process in less than one minute. The automation represents the first step for users interacting with the data in an SAP analytics system, and is expected to expand into additional functional and mission-oriented ERP actions. • Automating manual testing process. Another defense agency implemented a pilot program to automate manual testing process. Digital assistants were created to work alongside humans in end-to-end ERP and business intelligence testing and data validation. The digital assistants reduce individual test cycle times from 30-60 minutes to 2-3 minutes, while increasing test coverage, test quality, and positioning humans to perform critical job functions such as defect resolution, active debugging, and more advanced testing. The solution combines RPA with tradi – tional automation to execute test cases, including data entry and collection, analysis and reporting, and error identification for each case, laying the foundation for future ERP automation. How to get started? Processes best suited for RPA are high-volume, repetitive tasks that may involve multiple legacy systems and manual processes. They may also require a large number of staff, and have to address inaccuracies due to the rekeying of data. For example: • Extracting data from a source system or document and key – ing the data into a spreadsheet or second system; • Cumbersome processes requiring data capture from multiple sources (e.g., reconciliations and comparisons of financial data); • Matching data between two systems; and • Repetitive clerical processes (e.g., invoice matching or report generation and distribution). Implementing an RPA solution effectively necessitates a focus on several key considerations: • Begin with well-defined and fairly simple use cases; • Build upon early success and advance toward intelligent automation and systems cognitive; • Where the RPA platform and software bot provides the “hands” to increase productivity of a digital workforce, as described below a well-placed cognitive tool supplements the workforce’s “brain”; and • Features might include natural language processing and machine learning systems that train bots as they encounter new situations, and data analytics to diagnose issues and make recommendations. 12 BLOCKCHAIN As agencies continue to rely upon a combination of ERP solutions and legacy financial systems, they often encounter heavy customiza – tion to account for nuanced business processes and offer non-stan – dard (often proprietary) data distributions or transactions. End-to-end financial transactions involve a multitude of department and enter – prise level systems and multiple organizations. This complex business application ecosystem can lead to data silos, data duplication, mis – matched data, “vendor lock,” or incomplete transaction data. Similarly, an inability of organizations to record all financial transac – tions and supporting documentation—and to provide comprehensive traceability of transactions through planning, budgeting, and execu – tion—can hamper efforts towards achieving a clean audit. The advent of blockchain technology has helped commercial firms in the financial and other sectors address such challenges, and can reap similar ben – efits for government agencies. What is blockchain? Blockchain is a capability that allows for a centralized ledger that can track financial transactions end to end, enabling the level of traceabil – ity that agencies have long sought to achieve a clean audit opinion. Blockchain offers: Removal of data silos via a shared ledger —Blockchain provides a central ledger that can sit in between existing systems to allow for a single view into enterprise financial data, and the sharing of that data to inform important decisions. Assets can be tracked easily and the shared ledger becomes a single source of truth for financial transactions. Secure data —Blockchain supports data encryption and permission settings for participants, to ensure appropriate visibility and that transactions are secure, authenticated, and verifiable. Provenance of assets —Blockchain offers complete provenance details of each recorded asset to track what happened and when. All assets are secured to the blockchain ledger. Immutability —Nobody can change written data on the blockchain, not even a system administrator. This leads to trust in transactions and avoidance of fraud and abuse. How can blockchain reduce complexities and create integrity and traceability into the CFO’s financial management process? Though blockchain has the ability to maintain its own set of business logic and business rules as part of a “smart contract,” the most appropriate 13 Enterprise Systems ERP & Legacy Systems Smart Contract Blockchain Auditors implementation may be to leverage blockchain capability as a “shadow chain.” The shadow chain can sit over the top of existing systems and record transactions during each step in the financial pro – cess, according to the business rules defined in the blockchain smart contract; this creates an immutable record that can be reviewed through permissioned access. Such an implementation will take advantage of the investment already made to build out the complex logic of the FM process, and eliminate the need for governing bodies to further standardize data across the myriad of organizations, sys – tems, and transactions involved—focusing instead on the rules that define what data each player provides at each step in the process for ledger recording. The blockchain can connect with existing ERPs and legacy systems through use of APIs (Application Programming Interfaces), and these systems can pass records to be stored in the chain. With each piece of the end-to-end transaction stored in the block – chain, each organization can view each piece through secure access—a level of visibility not currently available. Agencies can leverage smart contracts that execute on the shared ledger based on a set of agreed upon business rules to drive consistency across the 14 network; the same rules apply for everyone using data on the shadow chain. Further, auditors have a place to view each transaction and a way to document traceability. Finally, the value of blockchain can pro – vide a trusted, more accurate, more actionable data set to feed AI, RPA, analytics, and other emerging technologies, value greater than than of a blockchain itself. Use cases Opportunities for blockchain in the Federal government are gaining traction. One agency is using blockchain to transmit and store data collected along borders and from airports and ports, while another is exploring how blockchain can be used to exchange medical informa – tion more securely and efficiently. How to get started? These resources can help agencies get started: • ACT-IAC Blockchain Primer: Enabling Blockchain Innovation in the U.S. Federal Government: https://www. actiac.org/act-iac-white-paper-enabling-blockchain-innova – tion-us-federal-government • ACT-IAC Blockchain Playbook: https://blockchain-work – ing-group.github.io/blockchain-playbook/intro • The Impact of Blockchain for Government: Insights on Identity, Payments, and Supply Chain: http://www.businessofgovernment.org/report/ impact-blockchain-government-insights-identity-pay – ments-and-supply-chain#overlay-context=blog/ how-can-blockchain-technology-help-government-drive-eco – nomic-activity-1 • Blockchain best practices guide The Founder’s Handbook, Your Guide to Getting Started with Blockchain Edition 2.0 discusses how to identify the business problem, build your ecosystem, business model design, gover – nance, and legal considerations. https://www-01. ibm.com/common/ssi/cgi-bin/ssialias?html – fid=28014128USEN . Key considerations To determine whether a use case is a good fit for blockchain, ask these questions: • Is a business network involved? • Is consensus used to validate transactions? • Is an audit trail, or provenance, required? 15 • Must the record of transactions be immutable, or tamper proof? • Should dispute resolution be final? If the answer is yes to the first question and to at least one other, then the case would benefit from blockchain technology. ARTIFICIAL INTELLIGENCE Cognitive capabilities involving AI will come to the Federal ERP mar – ketplace, but likely at a slower pace than in the commercial market. The information security requirements and process required for imple – mentation by federal agencies, particularly in the defense and intelli – gence space, will mean that adoption of widespread usage must be accompanied by a careful focus on compliance. For example, agen – cies must follow the Federal Risk and Authorization Management Program (FedRAMP) which enables secure cloud computing for the federal government. In addition to FedRAMP authorizations, U.S. Department of Defense (DoD) cloud solutions must achieve a provi – sional authorization from the Defense Information Systems Agency at impact level 5 (controlled, unclassified information) or 6 (for class ified information), as defined in DoD’s Cloud Computing Security Requirements Guide (SRG), Version 1, Release 3 (March 6, 2017). In October 2018, OMB released the Federal Data Strategy (https:// www.performance.gov/CAP/CAP_goal_2.html ) that serves as a foun – dation for how agencies can use AI, which has since been supple – mented by an AI Executive Order (https://www.whitehouse.gov/ presidential-actions/executive-order-maintaining-american-leader – ship-artificial-intelligence ). The Strategy identifies practices, princi – ples, and action steps designed to inform agency actions on an on-going basis, and the EO lays out a roadmap for agency implemen – tation and standards development. Example of AI in action The U.S. Transportation Security Administration’s Office of Acquisition Program Management experimented with a proof of concept of a Cognitive Object Detection Assistant (CODA). CODA demonstrated 16 early machine learning successes for X-ray baggage screening in the checkpoint environment, and the ability to improve threat object detection in comparison to traditional detection methods. CODA helped to augment the X-ray image for the operator by highlighting threats and prohibited items, identifying threat type, and providing a confidence score. CODA technology demonstrated a 99 percent detec – tion rate for handguns. Adoption of advanced capabilities will likely continue to occur first on the edges for the Federal financial ERP systems, where natural lan – guage processing may be implemented for help desk support (and blockchain use cases for asset management and supply chain). AI-enabled chat-bots have been successfully deployed on numerous ERP implementations to drive customer satisfaction, (e.g., by immedi – ate response to common questions) and reduce costs (e.g., off-shift support can be provided exclusively through AI). An application of AI in ERP can help agencies understand large amounts of historical, unstructured information, in order to identify patterns and improve performance. For example, a large civilian Federal agency is looking to improve its acquisition processes and redundancies in departmentwide contracting by putting emerging technologies behind data on the $24 billion it spends on goods and services each year through its “Buy Smarter ” initiative. This agency recently analyzed $23 billion in prior year purchase records from its procurement system. AI machine learning algorithms enabled more discrete category management and the identification of about $2 bil – lion a year in potential cost avoidance from consolidated purchases, reduction of vendors, reduction of models purchased, and similar steps. This analysis was not natively available in the procurement systems and too time consuming to be done manually. How to get started? These resources can help agencies get started: • Federal Data Strategy: Leveraging Data as a Strategic Asset, https://strategy.data.gov/ • Delivering Artificial Intelligence in Government: Challenges and Opportunities, http://www.businessofgovernment. org/sites/default/files/Delivering%20Artificial%20 Intelligence%20in%20Government.pdf • How Artificial Intelligence Can Transform Agencies, https://www.nextgov.com/ideas/2019/01/how-ar – tificial-intelligence-can-transform-govern – ment/154462/ 17 CONCLUSION Additional efforts are required to improve agency ERPs through upgrades, replacement of legacy financial systems, cloud strategies, and the adoption of cognitive technologies. The goal to operate an effective, efficient, and transparent CFO organization is ever changing as technology advances faster than most agencies can adapt, although many of the concepts discussed in the Center ’s 2010 paper remain relevant and apply to areas discussed in this special report. While most Federal ERPs have been relegated to the back office and CFO functions, the future of ERPs enabled with cognitive technolo – gies will allow migration to mission-oriented functions—adding value to the ERP investment. A key to success will involve agency under – standing of how to plan for and acquire new technologies and ser – vices. The following steps can help agencies move forward. Recommended Next Steps • Learn more about innovative solutions to help agencies become more efficient and transparent in Federal Financial Management by visiting the Bureau of the Fiscal Service’s Office of Financial Innovation & Transformation (FIT) at https://www.fiscal.treasury.gov/fit . • Attend training on these emerging technologies, such as free topic offerings at Massive Open Online Courses at www.MOOC.org . • Identify pain points in business processes, and consider a use case for one or more of these emerging technologies. • Pilot the use of an emerging technology for a business process pain point, and share the results of a pilot experience through FIT FM Innovation Program for information sharing purposes and lessons learned. About the IBM Center for The Business of Government Through research stipends and events, the IBM Center for The Business of Government stimulates research and facilitates discussion of new approaches to improving the effectiveness of government at the federal, state, local, and international levels. About IBM Global Business Services With consultants and professional staff in more than 160 countries globally, IBM Global Business Services is the world’s largest consulting services organization. IBM Global Business Services provides clients with business process and industry expertise, a deep understanding of technology solutions that address specific industry issues, and the ability to design, build, and run those solutions in a way that delivers bottom- line value. To learn more visit ibm.com. For more information: Daniel J. 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