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  • Analysing library data flows for efficient innovation

    Posted on November 27th, 2014 Lukas Koster 1 comment

    In my work at the Library of the University of Amsterdam I am currently taking a step forward by actually taking a step back from a number of forefront activities in discovery, linked open data and integrated research information towards a more hidden, but also more fundamental enterprise in the area of data infrastructure and information architecture. All for a good cause, for in the end a good data infrastructure is essential for delivering high quality services in discovery, linked open data and integrated research information.
    In my role as library systems coordinator I have become more and more frustrated with the huge amounts of time and effort spent on moving data from one system to another and shoehorning one record format into the next, only to fulfill the necessary everyday services of the university library. Not only is it not possible to invest this time and effort productively in innovative developments, but this fragmented system and data infrastructure is also completely unsuitable for fundamental innovation. Moreover, information provided by current end user services is fragmented as well. Systems are holding data hostage. I have mentioned this problem before in a SWIB presentation. The issue was also recently touched upon in an OCLC Hanging Together blog post: “Synchronizing metadata among different databases” .

    Fragmented data (SWIB12)

    Fragmented data (SWIB12)

    In order to avoid confusion in advance: when using the term “data” here, I am explicitly not referring to research data or any other specific type of data. I am using the term in a general sense, including what is known in the library world as “metadata”. In fact this is in line with the usage of the term “data” in information analysis and system design practice, where data modelling is one of the main activities. Research datasets as such are to be treated as content types like books, articles, audio and people.

    It is my firm opinion that libraries have to focus on making their data infrastructure more efficient if they want to keep up with the ever changing needs of their audience and invest in sustainable service development. For a more detailed analysis of this opinion see my post “(Discover AND deliver) OR else – The future of the academic library as a data services hub”. There are a number of different options to tackle this challenge, such as starting completely from scratch, which would require huge investments in resources for a long time, or implementing some kind of additional intermediary data warehouse layer while leaving the current data source systems and workflows in place. But for all options to be feasible and realistic, a thorough analysis of a library’s current information infrastructure is required. This is exactly what the new Dataflow Inventory project is about.

    The project is being carried out within the context of the short term Action Plans of the Digital Services Division of the Library of the University of Amsterdam, and specifically the “Development and improvement of information architecture and dataflows” program. The goal of the project is to describe the nature and content of all internal and external datastores and dataflows between internal and external systems in terms of object types (such as books, articles, datasets, etc.) and data formats, thereby identifying overlap, redundancy and bottlenecks that stand in the way of efficient data and service management. We will be looking at dataflows in both front and back end services for all main areas of the University Library: bibliographic, heritage and research information. Results will be a logical map of the library data landscape and recommendations for possible follow up improvements. Ideally it will be the first step in the Cleaning-Reconciling-Enriching-Publishing data chain as described by Seth van Hooland and Ruben Verborgh in their book “Linked Data for Libraries, Archives and Museums”.

    The first phase of this project is to decide how to describe and record the information infrastructure in such a form that the data map can be presented to various audiences in a number of ways, and at the same time can be reused in other contexts on the long run, for instance designing new services. For this we need a methodology and a tool.

    At the university library we do not have any thorough experience with describing an information infrastructure on an enterprise level, so in this case we had to start with a clean slate. I am not at all sure that we came up with the right approach in the end. I hope this post will trigger some useful feedback from institutions with relevant experience.

    Since the initial and primary goal of this project is to describe the existing infrastructure instead of a desired new situation, the first methodological area to investigate appears to be Enterprise Architecture (interesting to see that Wikipedia states “This article appears to contain a large number of buzzwords“). Because it is always better to learn from other people’s experiences than to reinvent all four wheels, we went looking for similar projects in the library, archive and museum universe. This proved to be rather problematic. There was only one project we could find that addresses a similar objective, and I happened to know one of the project team members. The Belgian “Digital library system’s architecture study” (English language report here)” was carried out for the Flemish Public Library network Bibnet, by Rosemie Callewaert among others. Rosemie was so kind to talk to me and explain the project objectives, approaches, methods and tools used. For me, two outcomes of this talk stand out: the main methodology used in the project is Archimate, which is an Enterprise Architecture methodology, and the approach is completely counter to our own approach: starting from the functional perspective as opposed to our overview of the actual implemented infrastructure. This last point meant we were still looking at a predominantly clean slate.
    Archimate also turned out to be the method of choice used by the University of Amsterdam central enterprise architecture group, whom we also contacted. It became clear that in order to use Archimate efficiently, it is necessary to spend a considerable amount of time on mastering the methodology. We looked for some accessible introductory information to get started. However the official Open Group Archimate website is not as accessible as desired in more than one way. We managed to find some documentation anyway, for instance the direct linkt to the Archimate specification and the free document “Archimate made practical”. After studying this material we found that Archimate is a comprehensive methodology for describing business, application and technical infrastructure components, but we also came to the conclusion that for our current short term project presentation goals we needed something that could be implemented fairly soon. We will keep Archimate in mind for the intermediate future. If anybody is interested, there is a good free open source modelling tool available, Archi. Other Enterprise Architecture methodologies like Business Process Modelling focus more on workflows than on existing data infrastructures. Turning to system design methods like UML (Unified Modelling Language) we see similar drawbacks.

    An obvious alternative technique to consider is Dataflow Diagramming (DFD) (what’s in a name?), part of the Structured Design and Structured Analysis methodology, which I had used in previous jobs as systems designer and developer. Although DFD’s are normally used for describing functional requirements on a conceptual level, with some tweaking they can also be used for describing actual system and data infrastructures, similar to the Archimate Application and Infrastructure layers. The advantage of the DFD technique is that it is quite simple. Four elements are used to describe the flow of information (dataflows) between external entities, processes and datastores. The content of dataflows and datastores can be specified in more detail using a data dictionary. The resulting diagrams are relatively easy to comprehend. We decided to start with using DFD’s in the project. All we had left to do was find a good and not too expensive tool for it.

    Basic DFD structure

    Basic DFD structure

    There are basically two types of tools for describing business processes and infrastructures: drawing tools, focusing on creating diagrams, and repository based modelling tools, focused on reusing the described elements. The best known drawing tool must be MicroSoft Visio, because it is part of their widely used Office Suite. There are a number of other commercial and free tools, among which the free Google Drive extension Draw.io. Although most drawing tools cover a wide range of methods and techniques, they don’t usually support reuse of elements with consistent characteristics in other diagrams. Also, diagrams are just drawings, they can’t be used to generate data definition scripts or basic software modules or reverse engineering or flexible reporting. Repository based tools can do all these things. Reuse, reporting, generating, reverse engineering and import and export features are exactly the features we need. We also wanted a tool that supports a number of other methods and techniques for employing in other areas of modelling, design and development. There are some interesting free or open source tools, like OpenModelSphere (which supports UML, ERD Data modelling and DFD), and a range of commercial tools. To cut a long story short we selected the commercial design and management tool Visual-Paradigm because it supports a large number of methodologies with an extensive feature set in a number of editions for reasonable fees. An additional advantage is the online shared teamwork repository.

    After acquiring the tool we had to configure it the way we wanted to use it. We decided to try and align the available DFD model elements to the Archimate elements so it would in time be possible to move to Archimate if that would prove to be a better method for future goals. Archimate has Business Service and Business Process elements on the conceptual business level, and Application Component (a “system”), Application Function (a “module”) and Application Service (a “function”) elements on the implementation level.

    Basic Archimate Structure

    Basic Archimate Structure

    In our project we will mainly focus on the application layer, but with relations to the business layer. Fortunately, the DFD method supports a hierarchical process structure by means of the decomposition mechanism, so the two hierarchical structures Business Service – Business Process and Application Component – Application Function – Application Service can be modeled using DFD. There is an additional direct logical link between a Business Process and the Application Service that implements it. By adding the “stereotypes” feature from the UML toolset to the DFD method in Visual Paradigm, we can effectively distinguish between the five process types (for instance by colour and attributes) in the DFD.

    Archimate DFD alignment

    Archimate DFD alignment

    So in our case, a DFD process with a “system” stereotype represents a top level Business Service (“Catalogue”, “Discover”, etc.) and a “process” process within “Cataloguing” represents an activity like “Describe item”, “Remove item”, etc. On the application level a “system” DFD process (Application Component) represents an actual system, like Aleph or Primo, a “module” (Application Function) a subsystem like Aleph CAT or Primo Harvesting, and a “function” (Application Service) an actual software function like “Create item record”.
    A DFD datastore is used to describe the physical permanent and temporary files or databases used for storing data. In Archimate terms this would probably correspond with a type of “Artifact” in the Technical Infrastructure layer, but that might be subject for interpretation.
    Finally an actual dataflow describes the data elements that are transferred between external entities and processes, between processes, and between processes and datastores, in both directions. In DFD, the data elements are defined in the data dictionary in the form of terms in a specific syntax that also supports optionality, selection and iteration, for instance:

    • book = title + (subtitle) + {author} + publisher + date
    • author = name + birthdate + (death date)

    etc.
    In Archimate there is a difference in flows in the Business and Application layers. In the Business layer a flow can be specified by a Business Object, which indicates the object types that we want to describe, like “book”, “person”, “dataset”, “holding”, etc. The Business Object is realised as one or more Data Objects in the Application Layer, thereby describing actual data records representing the objects transferred between Application Services and Artifacts. In DFD there is no difference between a business and a dataflow. In our project we particularly want to describe business objects in dataflows and datastores to be able to identify overlap and redundancies. But besides that we are also interested in differences in data structure used for similar business objects. So we do have to distinguish between business and data objects in the DFD model. In Visual-Paradigm this can be done in a number of ways. It is possible to add elements from other methodologies to a DFD with links between dataflows or datastores and the added external elements. Data structures like this can also be described in Entity Relationship Diagrams, UML Class Diagrams or even RDF Ontologies.
    We haven’t decided on this issue yet. For the time being we will employ the Visual Paradigm Glossary tool to implement business and data object specifications using Data Dictionary terms. A specific business object (“book”) will be linked to a number of different dataflows and datastores, but the actual data objects for that one business object can be different, both in content and in format, depending on the individual dataflows and datastores. For instance a “book” Business Object can be represented in one datastore as an extensive MARC record, and in another as a simple Dublin Core record.

    Example bibliographic dataflows

    Example bibliographic dataflows

    After having determined method, tool and configuration, the next step is to start gathering information about all relevant systems, datastores and dataflows and describing this in Visual Paradigm. This will be done by invoking our own internal Digital Services Division expertise, reviewing applicable documentation, and most importantly interviewing internal and external domain experts and stakeholders.
    Hopefully the resulting data map will provide so much insight that it will lead to real efficiency improvements and really innovative services.

     

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