- Email: [email protected]
Applied management information systems: Business analysis
566
Dossier Applied management business analysis
information
systems:
John R. Beaumont and Chris D. Beaumont This is the second in a series of applied management information potential. It presents an overview context of the strategic positiomng proposed in the first article of the
three articles presenting thoughts on systems, current practice and future of methods of business analysis, in the for management information systems series.l
An underlying tenet of the overview presented here is that the power of analysis is not appreciated; much effort is given to database management, yet analysis, documentation and presentation are neglected. Briefly, ‘analysis’ should not be confined to mathematical and statistical modelling; in the present context, analysis should be viewed as encapsulating all the components of a management information system-data acquisition, data collation, modelling, and information presentation-within the context of tactical and strategic business planning (see Figure 1). The success and relevance of business analyses depend primarily on communication between analysts, who produce the studies, and management, who use the results. Failures to provide meaningful and actionable analyses are often more attributable to poor lines of communication between the analysts and management than to any technical shortcomings. Management must com-
prehend the limitations of the results, the inputs and the underlying data assumptions; the isolation of analysts in, say, an internal service unit can be a basic constraint on management’s use of pertinent business analyses. Analysts and systems designers need to recognize that there is a hierarchy of information needs related to different management levels and functional areas in an organization. For example, the customer billing data are often tied up with the accounting function, and unavailable to the company’s marketing function. The overwhelming pattern is for an increasing amount of detail in what appears to be endless dimensions. In most companies the process of building up the level of information required has hardly commenced. Moreover, there will be a growing distinction between information which goes to line management and information which goes to the board of directors, who will focus on a different and narrower set of issues. Greater incidence of personal microcomputers should facilitate more direct management involvement, especially if a networking capability is available. It should, however, be appreciated that personal analysis can be a barrier to communization. In the context of business forecasting, for example, significant advances have been made in the micro-
John R. Beaumont is ICL Professor of Applied Management Information Systems at the University of Stirling, Stirling FK9 4LA, UK; Chris D. Beaumont is a managing consultant in Strategy and Marketing with Coopers and Lybrand, Plumtree Court, London EC4 4AQ,, UK, and Visiting Lecturer in Decision Sciences at The London Business School, Sussex Place, Regent’s Park, London NW1 4SA, UK.
FUTURES
Cktobbr1987
Dossier 567
Data acquisition
Data collation Management information system
information presentation
Modelling Figure 1. Components
of a management
information
particularly environment, computer with regard to graphical presentation facilities;2 little attention, however, has been given to the practical, value-added requirement of managerial judgment in the forecasting process with regard to this specific application area3 and the discussion below on simulation modelling, decision support systems and expert systems. While the four components of a management information system are interrelated (see Figure l), for convenience they are discussed below in separate sections. In the final section, some comments are made about future directions in business analysis. Data acquisition It has already been argued in the first article in this series4 that further data collection without sound structuring is likely to be both wasteful and unhelpful. As technologies develop both for data capture (eg electronic point of sales (EPoS) and bar coding and scanning) and for data storage (eg CD ROMs and optical disks), unless data collection and storage is structured and flexible, decision making will be hindered by overload. Can retail management really make direct use of billions of data elements covering each individual product transaction? In terms of data acquisition, an ambivalent situation can be recognized. In some circumstances, particularly with regard to government data, some useful data are not released. As any management information system can only be as good as the data incorporated, a number
FUTURES
October 1907
system
of issues related to data acquisition merit attention, including data summarization, data release, and data acquisition methods. Analysis can assist in a number of ways, particularly through an expioration and summarization of underlying patterns. How often have computerbased advances meant that the simple things, such as data description, are neglected?!j One adverse effect of recent software developments has often been a relative neglect of simple descriptive statistics, which can offer invaluable insights into patterns and trend@ inferential issues of model specification, parameter estimation and hypothesis testing should be viewed as stages away. Classical inferential statistics satisfy the deductive nature of hypothesis development in the natural sciences, but are often unhelpful for the analysis of messy data with underdeveloped theories of, consumer behaviour. Recently, say, under the umbrella of exploratory data analysis,’ greater attention is being given to visual identification and description of the major characteristics of data using prior knowledge and experience. Any management information system must combine internal and external data. Secondary data sources can be cost-effective and complementary to company data. However, a lot of useful and relevant data, p~ticularly for marketing analysis studies, are not released by government as official statistics, in order to maintain apparent confidentiality. The demand for such data is demonstrated by the fact that commercial companies have developed
‘clones’ to satisfy expressed demand from management, by attempting to reproduce some data that are held already within government. While it is essential to maintain con~dentiality, there is no justification for hiding unnecessarily behind such apparent restrictions. Fortunately, the UK Department of Trade and Industry is coordinating activities under a tradeable information initiative; this initiative can be viewed as a natural extension of its information handling activities. Given the importance of the 1984 UK Data Protection Act, micromodels could offer an apposite vehicle for the release of apparently confidential information in a usable form. Specifically, if model parameters could be made available, individuals’ confidentiality would be maintained, but there would be scope for the required, more detailed, policy impact analysis to be undertaken at the appropriate level. With IT (information technology) generally, it has been the “I”, or the technologies of computing, telecommunications and systems, that has been the focus of attention, rather than the ‘I’.8 Too much emphasis has been placed on new technology rather than the uses of information provided by the technology. Greater education as to the potential is necessary. Commodity Information, however, has become an important, tradeable and valuable commodity that can be delivered in a number of ways. One important and rapidly growing component of the tradeable information sector is the provision of online information, accessing databases that cover many subjects (including legal databases, financial services databases, geodemographic databases and the various databases available on Prestel, the public viewdata service operated by British Telecom). Sophisticated software is employed to structure the databases and to provide users with access to the
desired items of information. In general, however, there is an unfortunate rigidity in the enquiry formats, and attention is being given to the development of enhanced search procedures (see, for instance, the UK AIvey-funded research on intelligent knowledge-based systems (IKBS)). As the tradeable information sector expands, competing companies will exist that offer different databases purporting to satisfy the same needs. Management must have confidence in the integrity of the databases; data integrity must include accuracy, up-to-dateness and coverage. Fundamentally, it is suggested that it should be validated independently.g Data acquisition is a costly exercise, particularly as any good management information system must be kept up-todate with developing relevant and consistent historical data series. Indeed, in practice, the cost problems of maintenance are often greater than those of system establishment. For example, central to the proposal to replace local tax in the UK by the community charge or poll tax, intended to ensure greater local government accountability, is a computer-based register; it will be a geographical information system founded on all addresses that must incorporate daily migration of households and/or individuals over 18 years old. A cogent argument can be made for more attention to be given to methods of data acquisition. While reference has been made to the evolving technologies of data capture, it is believed that analytical methods remain useful. Brief reference here is made to sampling and (synthetic) data creation. Recent theoretical work on sample design strategies, for example, is indicative of some fundamental and practical results with regard to collecting and analysing large data sets. Stuart proposes an alternative multipurpose sample design using relatively few strata with large numbers of first-stage units to be selected within each stratum, rather than using a large
FUTURES
October 1987
Dossier
number of strata with few first-stage units selected within each stratum.10 Techniques are now available by which new (synthetic) data can be simulated that are consistent with available data. For instance, for market analysis in the UK, mi~rosimu~ation methods are being applied to update households and their socioeconomic and demographic characteristics for small areas using the latest (1981) UK census of population as the foundation.” Beyond this forecasting role, a particular attraction of such analysis is the opportunity for management explicitly to integrate characteristics of policy to consider the implications of alternative decisions. Data collation The essence of m~agement information systems is the handling and integration of different data sets. The fundamental issue is the cross-referencing and integration of data sets that are founded on different bases and time periods. Generally, to facilitate business control the trend is for management to require an increasing amount of disaggregated information. To satisfy these needs, careful attention must be given to the level at which data are collected and stored. A further, recently comprehended reason for paying careful attention to structuring data collation is that order exists in chaos. Apparently random data, which would be dismissed usually as noise or inadequacies in our data, can now be described within a conceptual framework offered by non-linear systems dynamics. Genuine stochastic behaviour can result from small deterministic systems, and therefore as our understanding of these systems develops, our ability to transform raw data into useful relevant and information should increase. For management, however, there is the fundamental implication of unpredictability (even if more information is generated). Rather than be
FUTURES October 1987
569
negative, the structuring of random changes using geometrical and statistical concepts can be powerfully applied, especially in the areas of risk analysis and structural stability. Modelling Although techniques have been shown to have importance in the stages of data acquisition and collation, there is a vast array of methods for statistical and mathematical modelling that have been developed and should be incorporated in a management information system. Software is now available in some form for all potential methods. Unfortunately, the technique emphasis undermines the direct usefulness of its applications. Modelling should be focused on commercial dynamics and output should be m~agement-orientated. While specific methods would be an integral part of particular systems, in this discussion, rather than focus on the details of various methods, it is apposite to identify important types of approach: 0 l o
Statistical (forecasting, regression, classification and sampling). Mathematical (optimization, simulation). Financial (budgeting, investment appraisal and risk analysis).
At the outset it is stressed that attention is not restricted to quantitative methods; too much emphasis on analytic management is a source of some of today’s problems. I2 For illustrative purposes, we shall consider elements of these approaches, centring our discussion on the management context; there is a plethora of suitable methodological textbooks available to describe different techniques. Attention should be sctfution-orientated, focusing on problem solving rather than fitting a problem to a method. Whimately, effectiveness does not depend on methodological criteria or sophistication, but on an ability to provide useful and meaningful insight to assist management in
their decision making. Analysis can only be justified if it leads in a more costeffective manner to the same decision by focusing discussion, or more critically to a different and better decision. An important question is: are the data available? (Unfortunately, this question is sometimes neglected in methodological texts.) As a general tenet, further enhancements to many of the available methods of business analysis cannot be justified until quality of available input data is improved. For example, Goldstein has commented generally on how the poor quality of data often undermines the application of respectable methods,t3 and Beaumont has argued that problems with spatial interaction gravity modelling describing shopping trips for retail location analysis arose because of the lack of suitable data on consumers’ flow patterns, rather than the nature of the model per se. I4 For management, the fundamental advantage of a causal approach is that it is founded on an attempt to understand how a system operates. In regression analysis, a causal reiationship is specified to explain variations in a dependent/ response variable by a set of independent/explanatory variables. For example, variations in store turnover are explained by different factors, such as size of catchment area, customer demographics, store size, merchandise mix, real prices and income, competitive situation, etc. Similarly, most companies are concerned with the effectiveness of their advertising, and attempts to understand changes in market share and product awareness have been linked to such factors as levels of advertising, and relative price distribumedia, tion. Fundamentally, formal modelling should not be viewed as a panacea; this latter example, for instance, is only apposite products and for certain markets. If a comprehensive understanding can be achieved, there is a greater likelihood of developing a useful management system, particularly if its limitations and
potential pitfalls are known. However, much of the available data are of a low order, categorical type, which are unsuitable for standard regression analysis (conventionally requiring non-parametric statistics). Over the last 15 years, categorical ring analysis has advanced, providing a close linkage with econometrics and using the integrated structure offered by the general linear model. In categorical data analysis we can recognize a family of statistical models that are differentiated by whether the response variable is continuous or categorical and by whether the explanatory variables are continuous, mixed or categorical.
Classification Classification is the fundamental way to provide coherence and consistency in a description of the complexities present in the real world. While there is no such thing as a single classilication, either for a particular data set or a set of phenomena of interest, it is a basic approach to structuring and summarizing data, grouping entities on the basis of their similarities and/or differences across a range of phenomena of interest, which can be especially useful for management. Classification schemes, such as the Standard Identification Classification (SIC) codes and geodemographic discriminators such as ACORN (A Classification of Residential Neighbourhoods) provide a means for crossreferencing data as well as being important market segmentation tools in their own right. Importantly in the present context, classification schemes provide an ordered structure for management information systems. Classification offers a consistent and meaningful base to cross-reference data, especially the integration of internal company data with external market data. While optimal solutions, mathematicaliy derived from models, do not such model-based occur in reality,
FUTURES October 1987
approaches have proved useful, because they can show management: 0
0
the trade-offs and conflicts between alternative objectives and assumptions; and the implications of selecting suboptimal options for reasons not incorporated in an analysis.
what is the current For example, efficiency of our distribution network in terms of an optimum one? Reference was made above to the usefulness and relevance of simulation models to permit management to complete ‘what if . . .’ studies (eg, an assessment of the implications of different service delivery methods on customer flows). Simulation models can also have immense pedagogic value for management in a training situation if both a realistic business environment is created and role playing, strategy formulation and decision making are highlighted. l5 Implicit in much decision making is an assumption that existing relationships and factors will continue in the future. In a business environment of rapid technological developments, intensive competition and increasing deregulation, however, structural changes are likely. Such effects are important to management because they represent periods of relatively great opportunity or risk. The rapid penetration of personal computers was cited as an important strategic issue for management information systems in the first article in this series.16 An associated growth has been spreadsheet software (after the availability of Visi Calc in 1979), and the later development of ‘integrated’ software (with Lotus l-2-3 as the world market leader). Although the scope of, say, Lotus l-2-3 is beyond financial modelling, the main applications are budgeting and planning (with the regular use of graphics capabilities for visual presentation).” In terms of business analysis, for completeness, further reference should
FUTURES October 1987
be made to decision support and expert systems. Expert systems are at the other end of the continuum from simple decision support systems, distinguished by whether all information is integral to the database, or whether some information is provided by management through a user interface. Such systems will not reduce or management’s replace decision making role (and they could have an important role in management training and development). For management purposes, it is possible for certain problems, such as production-inventory analyses, to be reduced to programmed logic; in contrast, for other problems, it should be remembered that knowledge about human behaviour and associated responses is much less formalized, if not unknown. Such systems can also be important for a company’s relations with customers, with the efficiency and effectiveness of service provision ensuring maintenance and development of market share. For instance, ICI’s Counsellor system gives advice to farmers on plant disease control over a viewdata network. Unfortunately, the power of a management information system depends on its usefulness and relevance in management decision making. As a consequence, the man-machine interface is of paramount significance. Careful thought to management needs when considering presentation, both tables and graphics, is essential. Information should be presented in a manner consistent with management’s requirements, rather than the modelling regime. The benefits of the statistical modelling approach, for example, are its explicit quantification of uncertainty; as such, analysis should be concentrated on monitoring fluctuations with the stochastic environment via, for instance, exception reporting. Bell’s visual interactive modelling facilitates this process by utilizing the traffic light analogy (green = no action; amber = monitor; red = alert, action). l8 Key performance indicators have not received the attention they deserve. Any
measurement must be set against the company’s objectives and must be aligned towards the management of different business units. It is fundaimportant that any permentally formance evaluation of an individual business unit should be measured over time against its own targets, rather than compared with other business units’ performances. Management statistics and performance indicators can aid good management and improve accountability, as well as offering a means for making.appropriate comparisons. There is the danger that attention is concentrated on the measurable, often at the exclusion of qualitative results, which can give the illusion of comprehensibility where comprehensibility is untenable. Importantly, performance indicators do not themselves provide solutions to m~agement’s strategic planning problems. Concluding
thoughts
There is no disputing the fact that management need more (or more approinformation, priate) more quickly, cheaply and accurately. It is necessary to enhance the flow of information to management to permit them to control and plan their business profitably. Unfortunately, management today requirements are not satisfied effectively. The issues raised are of a practical rather than a research and development orientation. The value of existing technology is proven. A basic prerequisite for management information systems is a greater emphasis on the use of analyses. Management can use information, technology and methods to increase the rate and effectiveness of strategic change; they are among their most powerful tools. If anything, IT increases uncertainty by facilitating more competition. However, IT and quantitative methods do present tremendous opportunities for those companies that apply them effectively. The challenge is for both management and technicians to provide a work-
ing environment for the opportunities to be realized. This requires strategies for management information systems to be an integral component of business strategy itself; this perspective is to be developed in the final article in this series. Until recently, while the expansion in computer power enabled management information systems to process vast quantities of data, they were inflexible to management’s needs. The evolution of decision support systems is beginning to overcome such shortcomings. More with the integration of generally, computers, communications and office systems, computers have started to have a balanced and integrated place to assist management. If the required planning is not forthcoming, existing choices could disappear and expensive and unnecessary mistakes may result. There is no predetermined system, but planning should be driven by management needs, rather than technology and data. Management need to know what information they need, what information they can get, and what information they can use effectively. In this information technology age, while there is enormous potential to assist management, it would be misguided to thereby hindering a be complacent, realization of the benefits. Notes and references
1. John
R.
Beaumont and Chris D. “Applied management ~nfo~ation systems: thoughts on current practice and future potential”, Futures, 19, (4), August 1987, pages
Beaumont,
442-445.
For a review, see C. D. Beaumont, E. Mahmoud and V. E. McGee, “Microforecasting catalogue”, Journal of Forecasting, 3, (4), 1985, pages 260-269. Chris D. Beaumont, “Forecasting with micros: a cautionary tale”, Futures, ICY, (l), February 1986, pages 84-91. 0fi cil, reference 1. For instance, the process of reading a table is described in A. S. C. Ehrenberg, “Reading a table”, Journal of the
FUTURES October 1987
Dossier 573
6.
7.
8.
9,
10.
11.
Royal Statistical Socieo, C, 35, (3), 1986, pages 237-244. See, for example, C. Chattield, “The initial examination of data”, Journal of the Royal Statistical Society, A, 148, (3), 1985, pages 214-253. J. W. Tukey, Exploratory Data Analysis (Reading, MA, USA, Addison-Wesley, 1977). See J. R. Beaumont, “The power of information”, mimeo, 1987, and the detailed discussion of ISDN in Beaumont and Beaumont, op tit, reference 1. See, for example, proposals for an ‘audit’ by accountants in J. R. Beaumont, “Accountancy in the IT age: information validation”, Certified Accountant, November 1987. shifts in sampling A. Stuart, “Location with unequal probabilities”, Journal of the Royal Statistical Sociev, A, 149, (4), pages 349-365. See, for example, M. Clarke, “Demoand household graphic processes dynamics: amicrosimulationapproach”, in R. Woods and P. Rees (eds), Population Structures and Models (London, Allen and Unwin, 1986).
FUTURES October 1997
12.
13.
14.
15.
16. 17.
See, for example, the discussion of “paralysis through analysis” in T. Peters and R. Waterman, In Search of Excellence (New York, Harper Row, 1982). “Present position and M. S. Goldstein, potential developments: some personal views”, Journal of the Royal Statistical Society, A, 147, (2), pages 260-267. J. R. Beaumont, “Retail location some management perspecanalysis: International Joumal of Retailing, tives”, 3, (2), 1987. See J. R. Beaumont and C. D. Beaumont, “C and L Telecoms game”, 1986 (available from Coopers and Lybrand Associates, Plumtree Court, London EC4A 4HT, UK) for a successful international application in telecommunications management. Op tit, reference 1. For a comprehensive discussion see M. Jackson, Creative Modeiling with Lotus
l-2-3 (London, Wiley, 1985). 18. P. Bell, “Visual interactive modelling”, part of a short course on Business Forecasting, London Business School, June 1987.
Dossier Applied management business analysis
information
systems:
John R. Beaumont and Chris D. Beaumont This is the second in a series of applied management information potential. It presents an overview context of the strategic positiomng proposed in the first article of the
three articles presenting thoughts on systems, current practice and future of methods of business analysis, in the for management information systems series.l
An underlying tenet of the overview presented here is that the power of analysis is not appreciated; much effort is given to database management, yet analysis, documentation and presentation are neglected. Briefly, ‘analysis’ should not be confined to mathematical and statistical modelling; in the present context, analysis should be viewed as encapsulating all the components of a management information system-data acquisition, data collation, modelling, and information presentation-within the context of tactical and strategic business planning (see Figure 1). The success and relevance of business analyses depend primarily on communication between analysts, who produce the studies, and management, who use the results. Failures to provide meaningful and actionable analyses are often more attributable to poor lines of communication between the analysts and management than to any technical shortcomings. Management must com-
prehend the limitations of the results, the inputs and the underlying data assumptions; the isolation of analysts in, say, an internal service unit can be a basic constraint on management’s use of pertinent business analyses. Analysts and systems designers need to recognize that there is a hierarchy of information needs related to different management levels and functional areas in an organization. For example, the customer billing data are often tied up with the accounting function, and unavailable to the company’s marketing function. The overwhelming pattern is for an increasing amount of detail in what appears to be endless dimensions. In most companies the process of building up the level of information required has hardly commenced. Moreover, there will be a growing distinction between information which goes to line management and information which goes to the board of directors, who will focus on a different and narrower set of issues. Greater incidence of personal microcomputers should facilitate more direct management involvement, especially if a networking capability is available. It should, however, be appreciated that personal analysis can be a barrier to communization. In the context of business forecasting, for example, significant advances have been made in the micro-
John R. Beaumont is ICL Professor of Applied Management Information Systems at the University of Stirling, Stirling FK9 4LA, UK; Chris D. Beaumont is a managing consultant in Strategy and Marketing with Coopers and Lybrand, Plumtree Court, London EC4 4AQ,, UK, and Visiting Lecturer in Decision Sciences at The London Business School, Sussex Place, Regent’s Park, London NW1 4SA, UK.
FUTURES
Cktobbr1987
Dossier 567
Data acquisition
Data collation Management information system
information presentation
Modelling Figure 1. Components
of a management
information
particularly environment, computer with regard to graphical presentation facilities;2 little attention, however, has been given to the practical, value-added requirement of managerial judgment in the forecasting process with regard to this specific application area3 and the discussion below on simulation modelling, decision support systems and expert systems. While the four components of a management information system are interrelated (see Figure l), for convenience they are discussed below in separate sections. In the final section, some comments are made about future directions in business analysis. Data acquisition It has already been argued in the first article in this series4 that further data collection without sound structuring is likely to be both wasteful and unhelpful. As technologies develop both for data capture (eg electronic point of sales (EPoS) and bar coding and scanning) and for data storage (eg CD ROMs and optical disks), unless data collection and storage is structured and flexible, decision making will be hindered by overload. Can retail management really make direct use of billions of data elements covering each individual product transaction? In terms of data acquisition, an ambivalent situation can be recognized. In some circumstances, particularly with regard to government data, some useful data are not released. As any management information system can only be as good as the data incorporated, a number
FUTURES
October 1907
system
of issues related to data acquisition merit attention, including data summarization, data release, and data acquisition methods. Analysis can assist in a number of ways, particularly through an expioration and summarization of underlying patterns. How often have computerbased advances meant that the simple things, such as data description, are neglected?!j One adverse effect of recent software developments has often been a relative neglect of simple descriptive statistics, which can offer invaluable insights into patterns and trend@ inferential issues of model specification, parameter estimation and hypothesis testing should be viewed as stages away. Classical inferential statistics satisfy the deductive nature of hypothesis development in the natural sciences, but are often unhelpful for the analysis of messy data with underdeveloped theories of, consumer behaviour. Recently, say, under the umbrella of exploratory data analysis,’ greater attention is being given to visual identification and description of the major characteristics of data using prior knowledge and experience. Any management information system must combine internal and external data. Secondary data sources can be cost-effective and complementary to company data. However, a lot of useful and relevant data, p~ticularly for marketing analysis studies, are not released by government as official statistics, in order to maintain apparent confidentiality. The demand for such data is demonstrated by the fact that commercial companies have developed
‘clones’ to satisfy expressed demand from management, by attempting to reproduce some data that are held already within government. While it is essential to maintain con~dentiality, there is no justification for hiding unnecessarily behind such apparent restrictions. Fortunately, the UK Department of Trade and Industry is coordinating activities under a tradeable information initiative; this initiative can be viewed as a natural extension of its information handling activities. Given the importance of the 1984 UK Data Protection Act, micromodels could offer an apposite vehicle for the release of apparently confidential information in a usable form. Specifically, if model parameters could be made available, individuals’ confidentiality would be maintained, but there would be scope for the required, more detailed, policy impact analysis to be undertaken at the appropriate level. With IT (information technology) generally, it has been the “I”, or the technologies of computing, telecommunications and systems, that has been the focus of attention, rather than the ‘I’.8 Too much emphasis has been placed on new technology rather than the uses of information provided by the technology. Greater education as to the potential is necessary. Commodity Information, however, has become an important, tradeable and valuable commodity that can be delivered in a number of ways. One important and rapidly growing component of the tradeable information sector is the provision of online information, accessing databases that cover many subjects (including legal databases, financial services databases, geodemographic databases and the various databases available on Prestel, the public viewdata service operated by British Telecom). Sophisticated software is employed to structure the databases and to provide users with access to the
desired items of information. In general, however, there is an unfortunate rigidity in the enquiry formats, and attention is being given to the development of enhanced search procedures (see, for instance, the UK AIvey-funded research on intelligent knowledge-based systems (IKBS)). As the tradeable information sector expands, competing companies will exist that offer different databases purporting to satisfy the same needs. Management must have confidence in the integrity of the databases; data integrity must include accuracy, up-to-dateness and coverage. Fundamentally, it is suggested that it should be validated independently.g Data acquisition is a costly exercise, particularly as any good management information system must be kept up-todate with developing relevant and consistent historical data series. Indeed, in practice, the cost problems of maintenance are often greater than those of system establishment. For example, central to the proposal to replace local tax in the UK by the community charge or poll tax, intended to ensure greater local government accountability, is a computer-based register; it will be a geographical information system founded on all addresses that must incorporate daily migration of households and/or individuals over 18 years old. A cogent argument can be made for more attention to be given to methods of data acquisition. While reference has been made to the evolving technologies of data capture, it is believed that analytical methods remain useful. Brief reference here is made to sampling and (synthetic) data creation. Recent theoretical work on sample design strategies, for example, is indicative of some fundamental and practical results with regard to collecting and analysing large data sets. Stuart proposes an alternative multipurpose sample design using relatively few strata with large numbers of first-stage units to be selected within each stratum, rather than using a large
FUTURES
October 1987
Dossier
number of strata with few first-stage units selected within each stratum.10 Techniques are now available by which new (synthetic) data can be simulated that are consistent with available data. For instance, for market analysis in the UK, mi~rosimu~ation methods are being applied to update households and their socioeconomic and demographic characteristics for small areas using the latest (1981) UK census of population as the foundation.” Beyond this forecasting role, a particular attraction of such analysis is the opportunity for management explicitly to integrate characteristics of policy to consider the implications of alternative decisions. Data collation The essence of m~agement information systems is the handling and integration of different data sets. The fundamental issue is the cross-referencing and integration of data sets that are founded on different bases and time periods. Generally, to facilitate business control the trend is for management to require an increasing amount of disaggregated information. To satisfy these needs, careful attention must be given to the level at which data are collected and stored. A further, recently comprehended reason for paying careful attention to structuring data collation is that order exists in chaos. Apparently random data, which would be dismissed usually as noise or inadequacies in our data, can now be described within a conceptual framework offered by non-linear systems dynamics. Genuine stochastic behaviour can result from small deterministic systems, and therefore as our understanding of these systems develops, our ability to transform raw data into useful relevant and information should increase. For management, however, there is the fundamental implication of unpredictability (even if more information is generated). Rather than be
FUTURES October 1987
569
negative, the structuring of random changes using geometrical and statistical concepts can be powerfully applied, especially in the areas of risk analysis and structural stability. Modelling Although techniques have been shown to have importance in the stages of data acquisition and collation, there is a vast array of methods for statistical and mathematical modelling that have been developed and should be incorporated in a management information system. Software is now available in some form for all potential methods. Unfortunately, the technique emphasis undermines the direct usefulness of its applications. Modelling should be focused on commercial dynamics and output should be m~agement-orientated. While specific methods would be an integral part of particular systems, in this discussion, rather than focus on the details of various methods, it is apposite to identify important types of approach: 0 l o
Statistical (forecasting, regression, classification and sampling). Mathematical (optimization, simulation). Financial (budgeting, investment appraisal and risk analysis).
At the outset it is stressed that attention is not restricted to quantitative methods; too much emphasis on analytic management is a source of some of today’s problems. I2 For illustrative purposes, we shall consider elements of these approaches, centring our discussion on the management context; there is a plethora of suitable methodological textbooks available to describe different techniques. Attention should be sctfution-orientated, focusing on problem solving rather than fitting a problem to a method. Whimately, effectiveness does not depend on methodological criteria or sophistication, but on an ability to provide useful and meaningful insight to assist management in
their decision making. Analysis can only be justified if it leads in a more costeffective manner to the same decision by focusing discussion, or more critically to a different and better decision. An important question is: are the data available? (Unfortunately, this question is sometimes neglected in methodological texts.) As a general tenet, further enhancements to many of the available methods of business analysis cannot be justified until quality of available input data is improved. For example, Goldstein has commented generally on how the poor quality of data often undermines the application of respectable methods,t3 and Beaumont has argued that problems with spatial interaction gravity modelling describing shopping trips for retail location analysis arose because of the lack of suitable data on consumers’ flow patterns, rather than the nature of the model per se. I4 For management, the fundamental advantage of a causal approach is that it is founded on an attempt to understand how a system operates. In regression analysis, a causal reiationship is specified to explain variations in a dependent/ response variable by a set of independent/explanatory variables. For example, variations in store turnover are explained by different factors, such as size of catchment area, customer demographics, store size, merchandise mix, real prices and income, competitive situation, etc. Similarly, most companies are concerned with the effectiveness of their advertising, and attempts to understand changes in market share and product awareness have been linked to such factors as levels of advertising, and relative price distribumedia, tion. Fundamentally, formal modelling should not be viewed as a panacea; this latter example, for instance, is only apposite products and for certain markets. If a comprehensive understanding can be achieved, there is a greater likelihood of developing a useful management system, particularly if its limitations and
potential pitfalls are known. However, much of the available data are of a low order, categorical type, which are unsuitable for standard regression analysis (conventionally requiring non-parametric statistics). Over the last 15 years, categorical ring analysis has advanced, providing a close linkage with econometrics and using the integrated structure offered by the general linear model. In categorical data analysis we can recognize a family of statistical models that are differentiated by whether the response variable is continuous or categorical and by whether the explanatory variables are continuous, mixed or categorical.
Classification Classification is the fundamental way to provide coherence and consistency in a description of the complexities present in the real world. While there is no such thing as a single classilication, either for a particular data set or a set of phenomena of interest, it is a basic approach to structuring and summarizing data, grouping entities on the basis of their similarities and/or differences across a range of phenomena of interest, which can be especially useful for management. Classification schemes, such as the Standard Identification Classification (SIC) codes and geodemographic discriminators such as ACORN (A Classification of Residential Neighbourhoods) provide a means for crossreferencing data as well as being important market segmentation tools in their own right. Importantly in the present context, classification schemes provide an ordered structure for management information systems. Classification offers a consistent and meaningful base to cross-reference data, especially the integration of internal company data with external market data. While optimal solutions, mathematicaliy derived from models, do not such model-based occur in reality,
FUTURES October 1987
approaches have proved useful, because they can show management: 0
0
the trade-offs and conflicts between alternative objectives and assumptions; and the implications of selecting suboptimal options for reasons not incorporated in an analysis.
what is the current For example, efficiency of our distribution network in terms of an optimum one? Reference was made above to the usefulness and relevance of simulation models to permit management to complete ‘what if . . .’ studies (eg, an assessment of the implications of different service delivery methods on customer flows). Simulation models can also have immense pedagogic value for management in a training situation if both a realistic business environment is created and role playing, strategy formulation and decision making are highlighted. l5 Implicit in much decision making is an assumption that existing relationships and factors will continue in the future. In a business environment of rapid technological developments, intensive competition and increasing deregulation, however, structural changes are likely. Such effects are important to management because they represent periods of relatively great opportunity or risk. The rapid penetration of personal computers was cited as an important strategic issue for management information systems in the first article in this series.16 An associated growth has been spreadsheet software (after the availability of Visi Calc in 1979), and the later development of ‘integrated’ software (with Lotus l-2-3 as the world market leader). Although the scope of, say, Lotus l-2-3 is beyond financial modelling, the main applications are budgeting and planning (with the regular use of graphics capabilities for visual presentation).” In terms of business analysis, for completeness, further reference should
FUTURES October 1987
be made to decision support and expert systems. Expert systems are at the other end of the continuum from simple decision support systems, distinguished by whether all information is integral to the database, or whether some information is provided by management through a user interface. Such systems will not reduce or management’s replace decision making role (and they could have an important role in management training and development). For management purposes, it is possible for certain problems, such as production-inventory analyses, to be reduced to programmed logic; in contrast, for other problems, it should be remembered that knowledge about human behaviour and associated responses is much less formalized, if not unknown. Such systems can also be important for a company’s relations with customers, with the efficiency and effectiveness of service provision ensuring maintenance and development of market share. For instance, ICI’s Counsellor system gives advice to farmers on plant disease control over a viewdata network. Unfortunately, the power of a management information system depends on its usefulness and relevance in management decision making. As a consequence, the man-machine interface is of paramount significance. Careful thought to management needs when considering presentation, both tables and graphics, is essential. Information should be presented in a manner consistent with management’s requirements, rather than the modelling regime. The benefits of the statistical modelling approach, for example, are its explicit quantification of uncertainty; as such, analysis should be concentrated on monitoring fluctuations with the stochastic environment via, for instance, exception reporting. Bell’s visual interactive modelling facilitates this process by utilizing the traffic light analogy (green = no action; amber = monitor; red = alert, action). l8 Key performance indicators have not received the attention they deserve. Any
measurement must be set against the company’s objectives and must be aligned towards the management of different business units. It is fundaimportant that any permentally formance evaluation of an individual business unit should be measured over time against its own targets, rather than compared with other business units’ performances. Management statistics and performance indicators can aid good management and improve accountability, as well as offering a means for making.appropriate comparisons. There is the danger that attention is concentrated on the measurable, often at the exclusion of qualitative results, which can give the illusion of comprehensibility where comprehensibility is untenable. Importantly, performance indicators do not themselves provide solutions to m~agement’s strategic planning problems. Concluding
thoughts
There is no disputing the fact that management need more (or more approinformation, priate) more quickly, cheaply and accurately. It is necessary to enhance the flow of information to management to permit them to control and plan their business profitably. Unfortunately, management today requirements are not satisfied effectively. The issues raised are of a practical rather than a research and development orientation. The value of existing technology is proven. A basic prerequisite for management information systems is a greater emphasis on the use of analyses. Management can use information, technology and methods to increase the rate and effectiveness of strategic change; they are among their most powerful tools. If anything, IT increases uncertainty by facilitating more competition. However, IT and quantitative methods do present tremendous opportunities for those companies that apply them effectively. The challenge is for both management and technicians to provide a work-
ing environment for the opportunities to be realized. This requires strategies for management information systems to be an integral component of business strategy itself; this perspective is to be developed in the final article in this series. Until recently, while the expansion in computer power enabled management information systems to process vast quantities of data, they were inflexible to management’s needs. The evolution of decision support systems is beginning to overcome such shortcomings. More with the integration of generally, computers, communications and office systems, computers have started to have a balanced and integrated place to assist management. If the required planning is not forthcoming, existing choices could disappear and expensive and unnecessary mistakes may result. There is no predetermined system, but planning should be driven by management needs, rather than technology and data. Management need to know what information they need, what information they can get, and what information they can use effectively. In this information technology age, while there is enormous potential to assist management, it would be misguided to thereby hindering a be complacent, realization of the benefits. Notes and references
1. John
R.
Beaumont and Chris D. “Applied management ~nfo~ation systems: thoughts on current practice and future potential”, Futures, 19, (4), August 1987, pages
Beaumont,
442-445.
For a review, see C. D. Beaumont, E. Mahmoud and V. E. McGee, “Microforecasting catalogue”, Journal of Forecasting, 3, (4), 1985, pages 260-269. Chris D. Beaumont, “Forecasting with micros: a cautionary tale”, Futures, ICY, (l), February 1986, pages 84-91. 0fi cil, reference 1. For instance, the process of reading a table is described in A. S. C. Ehrenberg, “Reading a table”, Journal of the
FUTURES October 1987
Dossier 573
6.
7.
8.
9,
10.
11.
Royal Statistical Socieo, C, 35, (3), 1986, pages 237-244. See, for example, C. Chattield, “The initial examination of data”, Journal of the Royal Statistical Society, A, 148, (3), 1985, pages 214-253. J. W. Tukey, Exploratory Data Analysis (Reading, MA, USA, Addison-Wesley, 1977). See J. R. Beaumont, “The power of information”, mimeo, 1987, and the detailed discussion of ISDN in Beaumont and Beaumont, op tit, reference 1. See, for example, proposals for an ‘audit’ by accountants in J. R. Beaumont, “Accountancy in the IT age: information validation”, Certified Accountant, November 1987. shifts in sampling A. Stuart, “Location with unequal probabilities”, Journal of the Royal Statistical Sociev, A, 149, (4), pages 349-365. See, for example, M. Clarke, “Demoand household graphic processes dynamics: amicrosimulationapproach”, in R. Woods and P. Rees (eds), Population Structures and Models (London, Allen and Unwin, 1986).
FUTURES October 1997
12.
13.
14.
15.
16. 17.
See, for example, the discussion of “paralysis through analysis” in T. Peters and R. Waterman, In Search of Excellence (New York, Harper Row, 1982). “Present position and M. S. Goldstein, potential developments: some personal views”, Journal of the Royal Statistical Society, A, 147, (2), pages 260-267. J. R. Beaumont, “Retail location some management perspecanalysis: International Joumal of Retailing, tives”, 3, (2), 1987. See J. R. Beaumont and C. D. Beaumont, “C and L Telecoms game”, 1986 (available from Coopers and Lybrand Associates, Plumtree Court, London EC4A 4HT, UK) for a successful international application in telecommunications management. Op tit, reference 1. For a comprehensive discussion see M. Jackson, Creative Modeiling with Lotus
l-2-3 (London, Wiley, 1985). 18. P. Bell, “Visual interactive modelling”, part of a short course on Business Forecasting, London Business School, June 1987.