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MLA Dataset Analyser solution 19 March 2008 Daniel Britton – Business analyst

MLA Dataset Analyser solution 19 March 2008 Daniel Britton – Business analyst. Who are MLA?. Museum, Libraries and Archives Council. Non-Departmental Public Body (NDPB), sponsored by the Department for Culture, Media and Sport (DCMS).

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MLA Dataset Analyser solution 19 March 2008 Daniel Britton – Business analyst

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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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