This chapter introduces the general goals of IM and connects them to information sources on buildings in order to determine the fundamental principles of IM with BIM.

The need for information management

With the information explosion we have been experiencing, it is hardly surprising that IM seems to have become a self-evident technical necessity. Handling the astounding amounts of information produced and disseminated every day requires more robust and efficient approaches than ever. Nevertheless, IM is considered mosty as a means to an end, usually performance in a project or enterprise: with effective IM, one can improve the chances of higher performance. Consequently, IM usually forms a key component of overall management.

This is widely acknowledged in building design management. Even before the digital era, the evident dependence of AECO on information coming from various sources and regarding various but interconnected aspects of a building had led to agreement that information and the way it is handled can be critical for communication and decision making. DM often focuses on information completeness, relevance, clarity, accuracy, quality, value, timeliness etc., so as to enable greater productivity, improve risk management, reduce errors and generally raise efficiency and reliability. The dependence on information is such that some even go so far as to suggest that DM is really fundamentally about IM: managing information flows so that stakeholders receive the right information at the right time.^[1]

In practical terms, however, there was little clarity concerning what should be managed and how. DM sources often simply affirm that information is important and should be treated with care. What makes information usable, valuable, relevant etc. is assumed to be known tacitly. Information is fundamentally correctly defined as data in usable form. Predictably, however, it is also equated to the thousands of drawings and other documents produced during the lifecycle of a building. If the right document is present, then it is assumed that stakeholders also possess the right information and are directly capable of judging the veracity, completeness, coherence etc. of the information they receive or need. However, equating information with documents not only places a heavy burden on users, it also prolongs attachment to analogue practices in the digital era.

It is arguably typical of AECO and DM that, in the face of operational and especially technical complexity, they invest heavily in human resources. This goes beyond the interpretation of documents in order to extract information; it also includes the invention of new roles that assume a mix of new and old tasks and responsibilities. So, in addition to project and process managers, one encounters not only information managers but also BIM managers, CAD managers, BIM coordinators and CAD coordinators, working together in complex, overlapping hierarchies. These new roles are usually justified by the need for support concerning new technologies, which may be yet unfamiliar to the usual participants in an AECO project. At the same time, however, they increase complexity and reduce transparency by adding more intermediaries in the already multi-layered structure of AECO. They moreover increase the distance between AECO stakeholders and new technologies, frequently limiting learning opportunities for the stakeholders.

New roles, either temporary or permanent, may be inevitable with technological innovation. In the early days of motorcars, for example, chauffeurs were more widely employed to drive them than today, while webmasters have become necessary by the invention and popularity of the World Wide Web and remain so for the foreseeable future, despite growing web literacy among general users. However, such new roles should be part of a sound and thorough plan of approach rather than an easy alternative to a good approach. The plan should determine what is needed and why, taking into account the increasing familiarity and even proficiency of many users with various technologies, to a degree that they require little day-to-day support. In our case, one may expect that AECO professionals will eventually become quite capable not only of using BIM directly but also of coordinating their BIM activities, with little need for technical intermediaries. After all, that was the case with analogue drawings in the past. To achieve this, AECO needs practical familiarization with the new technologies but above all clear comprehension of what these technologies do with information. Based on that, one can develop a sound IM approach that takes into account both domain needs and the capacities of digital technologies, determine changes in the tasks, responsibilities and procedures of existing AECO roles, and develop profiles for any additional roles.

Information sources

Inclusiveness

IM^[2] has a broad scope and, as a result, is quite inclusive. It pays no attention to issues of representation and accepts as information sources all kinds of documents, applications, services and schemes. This is due to three reasons. Firstly, IM covers many application areas and must therefore be adaptable to practices encountered in any of them. Secondly, in many areas there is a mix of analogue and digital information, as well as various channels, for example financial client transactions with a shop using cash and debit or credit cards, either physically or via a web shop. IM provides means for bringing such disparate material together into more coherent forms, ensuring that no outdated or inappropriate information is used and preventing that information is missing, inaccessible or deleted by error. These means include correlation with context (e.g. time series displays relative to other data), classification and condensation (aggregation, totalling, filtering and summarization). Thirdly, IM has a tenuous relation to computerization, often relying on it but also appearing weary of putting too much emphasis on technology to the detriment of information and organization.

The inclusiveness of IM with respect to information sources means that it may end up not only tolerating the redundancy of analogue and digital versions of the same information but also supporting outdated practices and conventions, even prolonging their life through superficial digitization. It may also reduce IM to mere document management, i.e. making sure that the necessary documents are retained and kept available. This seems like an easy way out of most domain problems. As the content and the expanse of the Internet suggest, there may be enough computer power and capacity to store and retrieve any document produced in a project or enterprise – in our case, throughout the whole lifecycle of a building (although one should question whether this also applies to all buildings in the world). On the other hand, however, both the information explosion in the digital era and big data approaches suggest the opposite: we already need more intelligent solutions than brute force. At this moment, we may think we still have control over the huge amounts of information in production and circulation but the IoT could change that soon, as smart things start communicating with each other with great intensity. For AECO this can be quite critical, since buildings are among the prime candidates for accommodating a wide range of sensors and actuators, e.g. for controlling energy consumption.

Structured, semi-structured and unstructured information

It is important for IM that BIM marks a transition not only to symbolic representation but also to holistic, structured information solutions for AECO. With regard to structure, there are three main categories:

• Unstructured data are the subject of big data approaches: sensor measurements, social media messages and other data without a uniform, standardized format. Finding relevant information in unstructured data is quite demanding because queries have to take into account a wide range of locations where meaningful data may reside and a wide variety of storage forms (including natural language and images).
• Semi-structured data are a favourite of IM: information sources with a loosely defined structure and flexible use. Analogue drawings are a typical example: one knows what is expected in e.g. a section but there are several alternative notations and few if any prohibitions concerning what may be depicted and how. IM thrives on semi-structured sources, adding metadata, extracting and condensing, so as to summarize relevant information into a structured overview.
• Structured data are found in sources where one knows precisely what is expected and where. Databases are prime examples of structured information sources. In a relational database, one knows that each table describes a particular class of entities, that each record in a table describes a single entity and that each field describes a particular property of these entities in the same, predefined way. Finding the right data in a structured source is therefore straightforward and less challenging for IM.

In contrast to analogue drawings, BIM is clearly structured, along the lines of a database. Each symbol belongs to a particular type and has specific properties. This structure is one of the driving forces behind BIM, in particular with respect to its capacity to integrate and process building information coherently. Given the effort put into developing structured models in BIM, it makes little sense to abandon the advantages they promise. More specifically, a parsimonious approach to IM with BIM should:

• Avoid having other primary information sources next to BIM: any building information should be integrated in BIM and all related data linked to it. Currently, there is general agreement that the price of a component, e.g. a washbasin, should be a property of the corresponding symbol. However, the same should apply to packaging information for this component, including the dimensions of the box in which the washbasin is brought to the building site, as this is useful for logistic purposes. Trying to retrieve this information from the manufacturer’s catalogue is significantly less efficient than integrating the relevant data among the symbol properties. The same applies to a photograph of some part of the building during construction or use: this too should be connected to BIM as a link between the digital file of the photograph and relevant symbols in the model (Figure 1) or even mapped as a decal on the symbols (Figure 2).
• Desist from promoting BIM output to the level of a primary source: any view of a model, from a floor plan to a cost calculation, can be exported as a separate document (PDF, spreadsheet etc.). This may have its uses but one should not treat such exports as sources separate from the model. Any query about the building, including the history of such output, should start from the model. Using IM to ensure consistency between exports and the model is meaningless. This applies even to legally significant documents like contracts because these too can be expressed as views of the model (i.e. textual frames around data exported from the model).

From the above, a wider information environment emerges around the model, populated largely by files linked to the model, preferably to specific symbols. IM can assist with the organization of this environment, even allowing queries to be answered on the basis of such satellite documents, but the successful deployment of BIM depends on transparent links between these queries and documents and the model itself: any query should ultimately lead to primary data and their history in the model.

It is perhaps ironic that while the world is focusing on big, unstructured data, AECO should insist on structured data. One explanation is latency: AECO has been late with the development of structured information solutions because it continued to use analogue, semi-structured practices in digital facsimiles. As a consequence, AECO has yet to find the limits of structured data, although this may happen soon, when the IoT becomes better integrated in building design and management.

The emphasis on the structured nature of BIM also flies in the face of IM and its inclusiveness. In this respect, one should keep in mind that IM is a means, not an end, and that its adaptability has historical causes. It is not compulsory to retain redundant information sources next to BIM, simply because IM can handle redundancy and complexity. If the structured content of BIM suffices, then IM for AECO simply becomes easier and parsimonious.

Information management goals

Information flow

The first of the two main goals of IM is to regulate information flows. This is usually achieved by specifying precise processing steps and stages, which ensure that information is produced and disseminated on time and to the right people, until it is finally archived (or disposed of). In terms of the semantic information theory proposed in this book, this involves identifying and tracking information instances throughout a process, covering both the production and modification of data. In IM there is an emphasis on the sources and stores of information: the containers and carriers from where information is drawn, rests or is archived. BIM combines all these into a single information environment, shifting attention to the symbols, their properties and relations, where the data of information instances are found.

Managing information flow involves:

• What: the information required for or returned by each specific task in a process
• Who: the actors or stakeholders who produce or receive the information in a task
• How: the processing of information instances
• When: the timing of information instances

What is about information instances and symbols, as discussed in the previous section. However, despite the integration potential of BIM, which makes most information internal, some data may reside outside of models, e.g. weather data required for a thermal simulation. Connectivity to external sources is also part of IM.

For both internal and external information, it is critical to distinguish between authorship and custodianship: the actors who produce some information are not necessarily the same stakeholders who safeguard this information in a project, let alone during the lifecycle of a building. A typical example is briefing information: this is usually compiled in the initiative stage by a specialist on the basis of client and user input, as well as professional knowledge. In the development stage, custodianship may pass on to a project manager who utilizes it to evaluate the design, possibly adapting the brief on the basis of insights from the design. Then in the use stage, it becomes a background to facility and property management, before it develops into a direct or indirect source for a new brief, e.g. for the refurbishment of the building. Making clear in all stages who is the custodian of this information is of paramount importance in an integrated environment like BIM, where overlaps and grey areas are easy to develop.

How information flows are regulated relates to the syntagmatic dimension of a model: the sequence of actions through which symbols, their properties and relations are processed. The information instances produced by these actions generally correspond to the sequence of tasks in the process but are also subject to extrinsic constraints, often from the software: the presence of bounding walls is necessary for defining a space in most BIM editors, although in many design processes one starts with the spatial design rather than with construction. IM needs to take such conflicts into account and differentiate between the two sequences.

A useful device for translating tasks into information actions is the tripartite scheme Input-Processing-Output (I‑P‑O), which underlies any form of information processing: for any task, some actors deliver information as input; this input is then processed by some other (or even the same) actors. These return as output some other information, which usually becomes input for the next task. IM has to ensure that the right input is delivered to the right actors and that the right output is collected. By considering each decision task with respect to I‑P‑O, one can identify missing information in the input and arrange for its delivery.

The syntagmatic dimension obviously also relates to when: the moments when information instances become available. These moments usually form a coherent time schedule. The time schedule captures the process of actions and transactions, linking each to specific information instances. Here again one should differentiate between the sequence of tasks, which tends to be adequately covered by a project schedule, and the sequence of information actions, which may require additional refinement.

Information flow in BIM

We are used to viewing the early part of a design process as something almost magical: someone somehow puts a few lines on a scrap of paper and suddenly we have a basis for imagining what the building will look like. The same applies to BIM: one starts entering symbols in a model and the design is there for all to see and process. Building information flows seem to emerge out of nothing but this is far from true. The designers who make the first sketches or decide on the first elements in a model operate on the basis of general knowledge of their disciplines, specific knowledge of the kind of building they are designing and specific project information, including location characteristics and briefs. In other words, building representations are the product of cognitive processes that combine both tacit and overt information.

It is also widely assumed that the amount of information in a design process grows from very little in early design to substantial amounts by the end, when a building is fully specified. This actually refers to the specificity of conventional building representations, e.g. the drawing scales used in different design stages. In fact, even before the first sketch is made, there usually is considerable information available on the building. Some of it predates the project, e.g. planning regulations and building codes that determine much of the form of a building and key features of its elements, such as the pitch of the roof and the dimensions of stairs. Other information belongs to the project, e.g. the brief that states accommodation requirements for the activities to be housed in the building, the budget that constrains cost or site-related principles like the continuation of vistas or circulation networks in the neighbourhood through the building site. Early building representations may conform to such specifications but most information remains in other documents or in the mind of the designers. For example, in many cases, one starts drawing or modelling a design with a site plan onto which building elements and spaces are placed but the site plan rarely includes planning regulations.

In managing building information, one should ensure that this information becomes explicit and is connected to subsequent tasks. In BIM, this amounts to augmenting the basic model setup (site plan, floor height and grids) with constraints from planning regulations (e.g. in the form of the permissible building envelope), use information from the brief and constraints on the kind of building elements that are admissible in the model (e.g. with respect to the fire rating of the building). Integration of such information amounts to feedforward: measurement and control of the information system before disturbances occur. Feedforward is generally more efficient and effective than feedback, e.g. checking if all building elements meet the fire safety requirements after they have been entered in the model.

It has also been suggested that early design decisions have a bigger impact on the outcome of a design process than later decisions. Having to decide on the basis of little overt information makes such decisions difficult and precarious. This conventional wisdom concerning early decisions may be misleading. Admittedly, early design decisions tend to concern basic features and aspects, from overall form to load-bearing structure, which determine much of the building and so have a disproportionate influence on cost and performance. However, such decisions are not exclusive to early design: the type of load-bearing structure can change late in the process, e.g. in relation to cost considerations or the need for larger spans. Such a late change can be more expensive because it also necessitates careful control of all interfacing between load-bearing and other elements in the design. From an IM perspective, what matters is to make all relevant information explicit in BIM, so as to know which data serve as input for a task (processing) and register the output of the task. Explicitness of information allows one to map decision making in a process and to understand the significance of any decision, regardless of process stage.

Information quality

The second main goal of IM is to safeguard or improve information quality.^[3] Quality matters to IM in two respects. Firstly, concerning information utility: knowing that the information produced and disseminated in a process meets the requirements of its users. Secondly, concerning information value: information with a higher quality needs to be preserved and propagated with higher priority. IM measures quality pragmatically, in terms of relevance, i.e. fitness for purpose: how well the information supports the tasks of its users. In addition to pragmatic information quality, IM is also keen on inherent information quality: how well the information reflects the real-world entities it represents.

In both senses, information quality is determined within each application environment. IM offers a tactical, operational and technical framework but does not provide answers to domain questions. These answers have to be supplied by the application environment in order for IM to know which information to preserve, disseminate or prioritize. It should be noted that IM is not passive with regard to information quality. It can also improve it both at meta-levels (e.g. by systematically applying tags) and with respect to content (e.g. through condensation).

Information quality concerns the paradigmatic dimension: the symbols of a representation and their relations. As this dimension tends to be quite structured in symbolic representations, one can go beyond the pragmatic level of IM and utilize the underlying semantic level to understand better how information quality is determined.

The first advantage of utilizing the semantic level lies in the definition of acceptable data as being well-formed and meaningful. This determines the fundamental quality of data: their acceptability within each representation. A coffee stain cannot be part of a building representation but neither can a line segment be part of a model in BIM: it has to be a symbol that has the appearance of a line segment (i.e. uses the line segment as implementation mechanism), e.g. a room separation line in Revit, the most abstract of bounding elements. By the same token, a colour is not acceptable as a description of the material of a wall and a floor cannot be host to a door (except for a trapdoor). In conclusion, any data that do not fit the specifications of a symbol, a property or a relation cannot be well-formed or meaningful in BIM. Therefore, they have low quality, which requires attention. If quality cannot be improved, these data should be ignored as noise.

Data that pass the fundamental semantic test must then be evaluated concerning relevance for the particular building or project and its tasks. To judge relevance, one needs additional criteria, e.g. concerning specificity: it is unlikely that a model comprising generic building elements is satisfactory for a task like the acoustic analysis of a classroom because the property values of generic elements tend to be too vague regarding factors that influence acoustic performance.

The semantic level also helps to determine information value beyond utility: prioritizing which information should be preserved and propagated relates to semantic type. As derivative data can be produced from primary data when needed, they do not have to be prioritized – in many cases, they do not have to be preserved at all. Operational data and metadata tend to change little and infrequently in BIM, so these too have a lower priority relative to primary data. Finally, anti-data have a high priority, both because they necessitate interpretation and action, and because such action often aims at producing missing primary data.

Parsimonious IM concerning information value in a symbolic representation like BIM can be summarized as follows:

• Preservation and completion of primary data
• Establishing transparent and efficient procedures for producing derivative data when needed
• Identification and interpretation of anti-data, including specification of relevant actions
• Preservation of stable operational and metadata

The priority of primary data apparently conflicts with IM improvement of information quality through condensation, i.e. operations that return pragmatically superior derivative data and metadata. Such operations belong to the second point above: if the primary data serve as input for certain procedures, then these procedures have to be established as a dynamic view or similar output in BIM. If users need to know the floor areas of spaces, one should not just give them the space dimensions and let them work out the calculations but supply instead transparent calculations, ordered and clustered in a meaningful way. This does not mean that the results of these calculations should be preserved next to the space dimensions from which they derive.

Moving from the semantic to the pragmatic level, veracity is a key criterion of quality: fitness for purpose obviously requires that the information is true. In addition to user feedback, veracity can be established on the basis of additional data, e.g. laser scanning to verify that a model represents faithfully, accurately and precisely the geometry of a particular building.

Before relevance or veracity, however, one should evaluate the structural characteristics of primary information: a model that is not complete, coherent and consistent is a poor basis for any use. Completeness in a building representation means that all parts and aspects are present, i.e. that there are no missing symbols for building elements or spaces in a model. BIM software uses deficiency detection to identify missing symbols. Missing aspects refer to symbol properties or relations: the definition of symbols should include all that is necessary to describe their structure, composition, behaviour and performance.

Completeness is about the presence of all puzzle pieces; coherence is about how well these pieces fit together to produce a seamless overall picture. In a building representation this primarily concerns the interfacing of elements, including possible conflicts in space or time. Clash detection in BIM aims at identifying such conflicts, particularly in space. Relations between symbols are of obvious significance for coherence, so these should be made explicit and manageable. In BIM, there are examples of this in the way some symbols attach themselves to others, e.g. co-terminating walls to each other, spaces to their bounding walls and floors, windows and doors to hosting walls. Parameterization can extend such relations further into a network that automatically ensures coherence.

Finally, consistency is about all parts and aspects being represented in the same or compatible ways. In a symbolic representation, this refers to the properties and relations of symbols. If these are described in the same units and present in all relevant symbol types, then consistency is also guaranteed in information use. Colour, for example, should be a property of the outer layer of all building elements. In all cases, the colour should be derived from the materials of this layer. This means that any paint applied to an element should be explicit as material with properties that include colour. Moreover, any colour data attached to this material layer should follow a standard like the RAL or Pantone colour matching systems. Allowing users to enter any textual description of colour does not promote consistency.

It is important to evaluate completeness, coherence and consistency only after clarifying the semantic types in a representation. This allows one to concentrate on the data that really matter, in particular primary and anti-data, and the procedures that produce derivative data. This allows higher focus in IM and reduces the amount of data to be processed.