Soft Systems Methodology

History

Soft Systems Methodology (SSM for short) was developed by Peter Checkland and colleagues at the University of Lancaster. It is based upon systems theory, which provides an antidote to conventional, 'reductionist' scientific enquiry - with its tendency to 'reduce' phenomena into smaller and smaller components in order to study and understand them. Systems theory attempts to study the whole picture; the relation of component parts to each other, and to the wider picture - it is 'holistic.' Biology and environmental science use its principles widely, as do other disciplines including systems analysis. SSM is not, contrary to popular supposition, an information systems design methodology - it is rather a general problem solving tool. Brian Wilson, a colleague of Checkland's at Lancaster, has adapted the methodology for business information analysis, and various attempts (Avison's 'Multiview,' for instance) have been made to incorporate it into systems design work.

What do we mean by 'system?'

We use the word 'system' quite a lot in everyday language ('computer system,' 'the educational system', 'systematic;'); we even talk about 'the system' - a  vague, sinister officialdom. Three uses of the word must be distinguished:

1.         a way of doing things, an organisation of resources and procedures.

2.         a computer, or information system

3.         (a specialised SSM use) - a conceptual organisation of resources and procedures defined according to systems theory - more about this later.

It will be a useful discipline to check that you understand which of these three senses of the word is being used every time the word occurs in this handout.

Why 'soft?'

System thinking has come to be characterised as either 'hard' or 'soft.' There are fundamental differences between a man-made ('designed physical' system), such as a nuclear reactor, and an organisational system - a 'human activity' system. Where mechanical components are involved, their behaviour can usually be predicted with reasonable accuracy - these are 'hard' systems; where human beings are involved this is not necessarily the case. Because human behaviour is unpredictable, organisational and management problems are seldom clear cut and well-defined; they are normally complex, with many indeterminable variables - 'soft' systems. At first glance, information systems would seem to be 'hard' - designed physical - systems, but experience shows that they seldom add value unless they are closely married to their organisational context, and the people who use them. There are therefore many softer issues which are important in information system planning, design, and implementation. 'Soft' has another, more specialist meaning - depending on the type of person you are, and your training and experience, you may understand 'systems' as tangible things which are really present in the world. You may, however, understand systems ideas as a series of intellectual constructs that we use to help us deal with the enormous complexity of the real world. This is an interesting, but un-resolvable argument; SSM tends strongly to the latter position.

Overview

SSM helps formulate and structure thinking about problems in complex, human situations. Its core is the construction of conceptual models (based on the understanding of human activity systems outlined above) and the comparison of those models with the real world. This process can greatly clarify those multi-faceted problems with many conflicting potential solutions, or no obvious way forward. Conceptual models are not representations of the real world, like a data-flow diagram - they are constructs which embody potential real world systems, but, more importantly, follow rigorously the systems principles already discussed, and their own well-defined internal logic. SSM is not, therefore, about analysing systems found in the world, but about applying systems principles to structure thinking about things that happen in the world - a difficult, but crucial distinction to grasp. It is most usefully carried out by people involved in the problem situation, with expert help available to guide and facilitate.

Data Warehousing

Abbreviated DW, a collection of data designed to support management decision making. Data warehouses contain a wide variety of data that present a coherent picture of business conditions at a single point in time.

“A Decision Support DB that is maintained separately from the organisation's operational Databases”.

What is a Data Warehouse?

A data warehouse is something you do, not something you buy. A successful data warehouse does not have an end. Regardless of the methodology, warehousing environments must be built incrementally through projects that are managed under the umbrella of a data-warehousing program. That program will be sponsored and supported at the Department of Information Technology.

Most of the benefits of the data warehouse will not be realized in the first delivery. The first project will be the foundation for the next, which will in turn form the foundation for the next. Data warehousing at the enterprise level is a long-term strategy, not a short-term fix. Its cost and value should be evaluated across a time span sufficient to provide us with a realistic picture of its cost-to-value ratio.

A DW is:

·        Subject-oriented: The warehouse is organized around the major subjects of the enterprise (e.g. customers, products, and sales) rather than the major application areas (e.g. customer invoicing, stock control, and product sales).

·        Integrated: The data warehouse integrates corporate application-oriented data from different source systems, which often includes data that is inconsistent. The integrated data source must be made consistent to present a unified view of the data to the users.

·        Time-variant: Data in the warehouse is only accurate and valid at some point in time or over some time interval. also shown in the extended time that the data is held, the implicit or explicit association of time with all data, and the fact that the data represents a series of snapshots.Warehouse data represent the time, and queries are based on time range.

·        Non-volatile: Data in the warehouse is not updated in real-time but is refreshed from operational systems on a regular basis. New data is always added as a supplement to the database, rather than a replacement.

Collection of data that is used primarily in organizational decision making.

Development of a data warehouse includes development of systems to extract data from operating systems plus installation of a warehouse database system that provides managers flexible access to the data.

The term data warehousing generally refers to the combination of many different databases across an entire enterprise. Contrast with data mart.

Benefits of DW:

·        Potential high returns on investment

·        Competitive advantage

·        Increased productivity of corporate decision-makers

Relational algebra

Introduction

Relational algebra received little attention until the publication of E.F. Codd's relational model of data in 1970. Codd proposed such algebra as a basis for database query languages.

Relational algebra is essentially equivalent in expressive power to relational calculus (and thus first-order logic); this result is known as Codd's theorem.

To overcome difficulties, Codd restricted the operands of relational algebra to finite relations only and also proposed restricted support for negation (NOT) and disjunction (OR). Analogous restrictions are found in many other logic-based computer languages.

Codd defined the term relational completeness to refer to a language that is complete with respect to first-order predicate calculus apart from the restrictions he proposed. In practice the restrictions have no adverse effect on the applicability of his relational algebra for database purposes.

Primitive operations

As in any algebra, some operators are primitive than the others, being definable in terms of the primitive ones, are derived. It is useful if the choice of primitive operators parallels the usual choice of primitive logical operators. Although it is well known that the usual choice in logic of AND, OR and NOT is somewhat arbitrary, Codd made a similar arbitrary choice for his algebra.

The five primitive operators of Codd's algebra are;

1.    Selection,

2.    Projection,

3.    Cartesian product (also called the cross product or cross join),

4.    The set union,

5.    The set difference

These five operators are fundamental in the sense that none of them can be omitted without losing expressive power. Many other operators have been defined in terms of these five.