Datamazed – analysing library data flows, manipulations and redundancies

ELAG 2015
By Lukas Koster, Library Systems Coordinator at the Library of the University of Amsterdam

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It’s more about transformations than manipulations. Tried to build a dataflow repository for efficiencies and blueprint for improvement.Initial problem is that system environment is complex. Lots of things happening to maintain this environment. Data is all over the place. Labyrinths are easy, but a maze is much more complex and that’s what our systems look like. Worth spending time to develop new environments because currently it is all very fragmented. The data is hostage and we need to free it.

Goal of the project: describe the nature and content of all internal and external datastores and workflows between internal and external systems in terms of object types and data formats, thereby identifying overlap, redundancy and bottleneck that stand in the way of efficient data and service management.

Methodology used is enterprise architecture. Distinguish between business (what), enterprise (how) and technology. Looked at other similar experiences and knew of BIBNET Flemish Public Library Network and their Architecture Study, focusing on the big picture rather than dataflows.

DFD = dataflow diagramming is a fairly easy model. Also used tools such as data modelling, visualisation etc.  Chose Business System Modelling, a relatively open tool with a number of export/import and a lot of documentation and reports. 

Dataflow repository describes all elements, including the systems they use etc. Their Visual Paradigm Project Model is subdivided into meaningful folders that can also be used to generate reports. They also have made a data dictionary for all object types, data elements and so on.

Business layer top level
Business layer level 2
Business layer level 3: data management
Application layer: data exchange

Dataflows can be defined by type (they had 5). In all data flows there’s an element of selection on what you do and with what. It has to be documented to help for decisions and so that you know what to expect and what happens (especially if you’re going to change systems.) Same for transformations – has to be transparent. 

Data redundancy is also an important issue and can be caused for various reasons. The unique solution: linked data!

Mostly benefits of all of this is not only having a good overview of available data, dataflow dependencies and efficiencies but also experimenting with linked data. It may be the beginning of something else, such as data consolidation exchange. Descriptions of how things are more automated should also be recorded.