Analysis in DHIS 2. Principles of DHIS 2 analysis  Flexible data model enables dynamic reporting (input != output)  Customize with report design tools.

Slides:



Advertisements
Similar presentations
DIGIDOC A web based tool to Manage Documents. System Overview DigiDoc is a web-based customizable, integrated solution for Business Process Management.
Advertisements

Analyses for all areas of your business Analysis Suite by Taurus Software Analysis Suite by Taurus Software.
Business Intelligence (BI) PerformancePoint in SharePoint 2010 Sayed Ali – SharePoint Administrator.
James Serra – Data Warehouse/BI/MDM Architect
Key Considerations for Report Generation & Customization Richard Wzorek Director, Production IT Confidential © Almac Group 2012.
16 months…. The Visibility Information Exchange Web System is a database system and set of online tools originally designed to support the Regional Haze.
Lab3 CPIT 440 Data Mining and Warehouse.
CSE6011 Warehouse Models & Operators  Data Models  relations  stars & snowflakes  cubes  Operators  slice & dice  roll-up, drill down  pivoting.
Modeling and Evaluation. Modeling Information system model –User perspective of data elements and functions –Use case scenarios or diagrams Entity model.
Enterprise Business Processes and Reporting (IS 6214) MBS MIMAS 3 rd Feb 2010 Fergal Carton Business Information Systems.
Distributed Data Analysis & Dissemination System (D-DADS) Prepared by Stefan Falke Rudolf Husar Bret Schichtel June 2000.
Database Management Systems, 2 nd Edition. R. Ramakrishnan and J. Gehrke1 Decision Support Chapter 23.
“This presentation is for informational purposes only and may not be incorporated into a contract or agreement.”
DATA WAREHOUSING IN SQL SERVER 2005/2008 BUSINESS INTELLIGENCE.
ICAP Tanzania’s Experiences Implementing an Aggregate Database: District Health Information System (DHIS) Joshua Chale, Data Manager July 28, 2010.
Bringing BI to SharePoint. DSP (Decision Support Panel)  DSP Portal Edition provides an easy-to-use framework that links information stored in data warehouses.
1 The following presentation is from the Oracle Webcast “What’s New in P6 EPPM Release 8.1.” As a partner, you may not use the Oracle Power Point template,
Data Warehouse Overview September 28, 2012 presented by Terry Bilskie.
ActivityInfo Introduction. ActivityInfo? ActivityInfo is an online tool for monitoring humanitarian projects to help humanitarian organizations to: collect.
OLAP Theory-English version On-Line Analytical processing (Business Intelligence) [Ing.J.Skorkovský,CSc.] Department of corporate economy.
5/26/2016DataSet™ Presentation 1 Front Cover 2008 DataSet™ An Advanced Business Intelligence Solution.
Microsoft Business Intelligence Environment Overview.
Business Intelligence Zamaneh Jahed. What is Business Intelligence? Business Intelligence (BI) is a broad category of applications and technologies for.
© 2008 IBM Corporation ® IBM Cognos Business Viewpoint Miguel Garcia - Solutions Architect.
Enterprise Reporting Solution
Presented By: Muhammad Rizvi Raghuram Vempali Surekha Vemuri.
1 Data Warehouses BUAD/American University Data Warehouses.
Slide 1. © 2012 Invensys. All Rights Reserved. The names, logos, and taglines identifying the products and services of Invensys are proprietary marks.
The Data Warehouse “A data warehouse is a subject-oriented, integrated, time-variant, and nonvolatile collection of “all” an organisation’s data in support.
Data Warehousing.
Touchstone Automation’s DART ™ (Data Analysis and Reporting Tool)
Financial OLAP Tool. “Y.A.E. Financial Consulting Ltd.” Established in 1989 the company has been engaged in Finance, Economics, I.T. and Accounting Consulting.
SHIFALI CHOUBEY GISE LAB IITB Decision Support System For Farmers.
What is the Office of Central Reporting? One central office, responsible to produce data reports and analytics for the entire organization Why? Keeps.
Chapter 5 DATA WAREHOUSING Study Sections 5.2, 5.3, 5.5, Pages: & Snowflake schema.
Building Dashboards SharePoint and Business Intelligence.
OLAP in DWH Ján Genči PDT. 2 Outline OLAP Definitions and Rules The term OLAP was introduced in a paper entitled “Providing On-Line Analytical.
QC Dashboard v2.0 - Responsive web client and project agregator for Quality Center © Cédric HERVE 31/03/2015.
Metadata By N.Gopinath AP/CSE Metadata and it’s role in the lifecycle. The collection, maintenance, and deployment of metadata Metadata and tool integration.
Distributed Data Analysis & Dissemination System (D-DADS ) Special Interest Group on Data Integration June 2000.
Visualizing data for program improvement with ICAP’s Unified Reporting System (URS): Presented by: Deborah Horowitz, Strategic Information Specialist,
Database Management Systems, 2 nd Edition. R. Ramakrishnan and J. Gehrke1 Data Warehousing and Decision Support.
The Need for Data Analysis 2 Managers track daily transactions to evaluate how the business is performing Strategies should be developed to meet organizational.
Database Management Systems 3ed, R. Ramakrishnan and J. Gehrke1 Data Warehousing and Decision Support Chapter 25.
Cognos 8: Software for Management Information System.
1 Copyright © 2008, Oracle. All rights reserved. Repository Basics.
UNEP Live. What is UNEP Live? - An on-line knowledge management platform - Focuses on open access to global, regional and national data and knowledge.
OLAP Theory-English version On-Line Analytical processing (Buisness Intelligence) Ing.Skorkovský,CSc Department of Corporate Economy Faculty of Economics.
Agree on deployment, UNEP Live – uneplive.unep.org.
Bartek Doruch, Managing Partner, Kamil Karbowiak, Managing Partner, Using Power BI in a Corporate.
Business Intelligence Overview
- The DHIS2 Training Environment -
LSI Business Intelligence Initiative
- The DHIS2 Training Environment -
Data Visualizer.
Presentation of the eTendersNI service Business Intelligence Module
Data Warehouse.
Star Schema.
Databases.
Overview of LDB Technology and Tools
DHIS2 Interaction Program Government of Nepal Ministry of Health & Population Department of Health Services Management Division.
CMPE 226 Database Systems April 11 Class Meeting
UNEP Live – uneplive.unep.org
Data Warehouse and OLAP
Data Warehouse Overview September 28, 2012 presented by Terry Bilskie
An Introduction to Data Warehousing
Data Warehouse Overview September 28, 2012 presented by Terry Bilskie
Tutorial 7 – Integrating Access With the Web and With Other Programs
Analytics, BI & Data Integration
Data Warehouse and OLAP
Presentation transcript:

Analysis in DHIS 2

Principles of DHIS 2 analysis  Flexible data model enables dynamic reporting (input != output)  Customize with report design tools – not with programming  One-click and dynamic tools for analysis are provided  Online system enables horizontal comparison

Dimensionality in DHIS 2

Categories Break-down of data elements Should sum up to a total Should be re-used as much as possible

Aggregation

Aggregation Operator Defined per data element  Aggregation strategy in the time dimension SUM  Routine data  Malaria treated, ANC 1 st visit AVERAGE  Semi-permanent data  Number of staff, Total population

Principles of DHIS 2 analysis Flexible data model enables dynamic reports  Data is collected for atomic units (data elements)  Reports are independent of data entry forms (input != output)  Analysis should be indicator-driven

Principles of DHIS 2 analysis Customize with report designers  Integrates with reporting tools (Jasper Reports/iReport)  No need for programming  Flexible report data source is provided (report table)

Principles of DHIS 2 analysis One-click and dynamic tools for analysis are provided  Facilitates users with different levels of experience  Facilitates requirements for fixed reports as well as deep analysis

Principles of DHIS 2 analysis Online system enables horizontal comparison  Feedback reports available immediately  Compare performance with other districts

Analysis in DHIS 2 DHIS 2 analysis tools  Report table  Standard (one-click) report  Dataset report  Static report  Chart  GIS  Web pivot table  Dashboard Integration with 3 rd party analysis tools  Excel pivot table

DHIS 2 analysis tools Report table  Basis for reporting  Customizable and generic data source for reports  Can be crosstabulated on any dimension  Provides relative periods  Provides report parameters One-click (standard) report  Created in 3 rd party report designers (iReport/BIRT)  Uses report table as data source (potentially any table)

DHIS 2 analysis tools Dataset report  Custom data entry form populated with aggregated data Static report  Upload a document  Link to a URL Chart  Based on indicators, periods, and organisation units  Bar and line charts with legends, labels and regression lines

DHIS 2 analysis tools GIS (mapping)  Completely integrated within DHIS 2  Thematic mapping with drill-down  Multiple data sources (Geoserver, GeoJSON, DHIS 2 database)  Legend sets linked to indicators and data elements  Favorite map views Web pivot table  Pivot on aggregated indicator data Dashboard  Quick access to favorite charts, reports and map views

DHIS 2 analysis tools Excel Pivot client  Local data mart synchronized with central server  Pivot table can be refreshed against local data mart  Provides off-line dynamic analysis capabilities

DHIS 2 analysis tools

DHIS 2 as data warehouse Data warehouse: System designed for integrated collection of data from multiple sources into a central location DHIS 2: Used both as data warehouse and transactional system Data warehouse: Designed using dimensional (star) or normalized (ER) schema DHIS 2: Designed using both principles

DHIS 2 as data mart Data mart: Aggregated data repository designed for easy data access and analysis based on user needs DHIS 2: Data aggregated in time (period) and space (organisation unit hierarchy) dimensions

Aggregation strategy Strategy A: Real-time – Pro: Get aggregated data immediately after entry – Con: Report generation will be slow – Con: Bad performance for other users of the system Strategy B: Batch (Aggregation happens at night) – Pro: Fast access to aggregated data – Pro: Aggregated data will be consistent – Pro: Small impact on system performance – Con: Aggregated data not available until the next day