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.

ELAG 2015 - Stockholm - Opening presentations

Opening note – Gunilla Herdenberg, KB National Librarian

Older collections and prints are stored in two buildings 40m underground. Legal deposit from 1661. 1979 audio-visual material was added (films, moving image etc.) and last year digital material. Want to unlock data to facilitate use and reuse.

Faith, hope and codification – Janis Kremlin, Senior librarian for academic affairs

Libraries dream of comprehensiveness. Best memo stick is [picture of] tree in a garden because the purpose of a garden is to create a whole world. We are seeking permanency. The new keyword is codification. Libraries are always concerned about objects. We’re missing the way we are communicating, faced with the challenge of losing memory, that’s why codification is so important. It concentrates on little objects, we’re not talking of collections any more but codes, like a garden that can create the entire world. At the end of the day it has to be relevant.

Keynote: Sometimes I feel sorry forthe data – Magnus Oman, Daniel Gillard

What big data really is is scalability, that’s the main point. At least for the techies. For the memory people it’s the fact it’s so big we can’t possibly work with it! Silly…

We throw away so much data and don’t have to. There’s a whole load we can know now, we have the data. But we’re not using it. For example we could use data from people’s movements for public transport planning. But there are moral issues even with anonymity and that’s why it’s not being exploited.

Another example is about how computers understand language. Taking data from Wikipedia for example, which is really not that much, we can analyse the proximity of words or similarity and make connections. This is a mechanistic view of course but is an example. Analytics is what can make sense or get things wrong…

One question was asked about data on text rather than numbers and can we do that? Answer is that it’s not that easy and reliable, even if some companies say they can do it but the algorithms are not straightforward.