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Enhancing MLA Data Access and Analysis: A New Portal for Museums, Libraries, and Archives

This project outlines the development of an analysis platform for the Museums, Libraries, and Archives (MLA) sector, aiming to provide accessible and comprehensive data access. The MLA seeks to eliminate inconsistencies from previously isolated datasets by creating a portal that aggregates public, institutional, workforce, and financial survey data. Utilizing advanced OLAP technology, the platform enables the generation of complex reports and visualizations. The initiative supports evidence-based decision-making through efficient data analysis, enhancing collaboration across the cultural sector in England.

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Enhancing MLA Data Access and Analysis: A New Portal for Museums, Libraries, and Archives

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  1. MLA Dataset Analyser solution19 March 2008Daniel Britton – Business analyst

  2. Who are MLA? • Museum, Libraries and Archives Council. • Non-Departmental Public Body (NDPB), sponsored by the Department for Culture, Media and Sport (DCMS). • MLA partnership - deliver strategic leadership in England and in each of its regions and collaborate with partners across the UK. • Strategic body • Work with and for the museums, archives and libraries sector • Aim for collaboration between sectors

  3. Background to the project • MLA desire to be an evidence-informed group. • MLA required a new analysis platform • MLAP staff require easy access to analyse multiple years worth of data from various sources. • Data was previously held in separate systems and formats – inaccessible and inconsistent. • Objective: create an accessible analysis portal for the dissemination of reports: • Key performance indicators and targets • Understanding public participation • Presence and operational details of MLA in regions

  4. Datasets • Large amount of datasets – unconnected for the most part. • Data includes: • Public survey data – visitor profile and opinions • Institution survey data – performance and trends • Workforce data – employee profile • Financial data – financial surveys of institutions • Aggregated statistics • Granularity – differs dependent on dataset. • UK • Country • Region • LA • Institution

  5. Technology – SV4 • Cubes: • Multi-dimensional – x, y, z… • Multiple measures • What can we determine? • Number of fruit sold/purchased per store per month, per…

  6. Technology – OLAP - Mondrian • Java-based OLAP server • 4-tied architecture: • Storage layer – RDBMS • Star layer – maintains an aggregate cache • Dimensional layer – parses MDX queries • Presentation layer – e.g. JPivot • Advantages: • Fast at processing large quantities of data • Complex reports created with relative ease – via MDX

  7. Dataset configuration • Multiple cubes per dataset • Easy to examine a subset of data • Improves speed of analysis • Aggregated and Pre-aggregated data • Region levels – some data aggregated, some pre-aggregated (fudged). • Combining cubes • Separate datasets combined on common criteria, e.g. Region, LA, etc.

  8. Advantages: SV4 • Speed – cubes allow complex reports to be created very quickly • Flexibility – no limits to the number of dimensions/criteria to analyse • UI – insert colours, arrows • Analyse trends • Highlight data

  9. Design - 3 stage report creation Name report, access level and category Select cube, configure via OLAP tool, apply filters Describe report and insert footnotes

  10. Configuring the OLAP tool

  11. Reports produced • Tables • Graphs • Export to excel • Export to PDF

  12. Advanced report creation - MDX

  13. Additional features • Data control • Download raw data • Dataset upload – future-proof, upload additional years • Footnotes • Security • Four user access levels • Administrator, MLA partnership staff, Registered public, Anonymous • Complete control of access to entire datasets or individual reports. • Integration • Seamless security and UI integration • User verification between sub-domains

  14. Possibility to integrate features from other projects • GIS mapping

  15. Any questions?

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