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Data Management & Data lifecycle Survey Conception Data System Architecture Data collection management Data Analysis & Dissemination.

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Presentation on theme: "Data Management & Data lifecycle Survey Conception Data System Architecture Data collection management Data Analysis & Dissemination."— Presentation transcript:

1 Data Management & Data lifecycle Survey Conception Data System Architecture Data collection management Data Analysis & Dissemination

2 Data Management & Data Lifecycle Type of info per usage Introduction

3 Data Management & Data Lifecycle From Data.. to Information Introduction Operation Data Manager should be involved in all the steps of a “Data Lifecycle”. Survey Conception Data System Architecture Data Collection management Data Analysis & Dissemination Any break of this cycle ends with the failure of the system : A data collection form that is ill- designed either because it does not satisfy operational information requirements or is flawed from a technical standpoint A well designed survey with a poorly designed and therefore poorly maintained database A structurally well designed database with no data, as data collection cycles have not been integrated/respected A well populated database without implemented reports and queries and therefore no output

4 Data Management & Data Lifecycle Before the Form… Survey Conception Avoid reinventing the wheel – check what has been designed and piloted before Consultation with all stakeholders – avoid duplication of efforts and assessment fatigue of beneficiaries Layers of data collection Collect Simple base reference data first Embark on detailed info based on samples defined from the base reference Data collection frequency should vary according to how frequently the phenomena being tracked or measured changes

5 Data Management & Data Lifecycle Good practices for Data Collection Forms 1.Questionnaires used in survey research should be clear and well presented. 2.Think about the form of the questions, 3.Keep the survey as short as possible. 4.Make definitions of data elements consistent with standard definitions and analytic conventions 5.Plan clearly how answers will be analyzed. 6.Test the survey for “understandability” and respondent effort through focus groups Survey Conception

6 Data Management & Data Lifecycle Data model Data models are the key for interoperability (i.e easy data exchange with partners) Implementing partners should not have to draft and decide on a core data model; it should be the same everywhere and just adapted locally where necessary; support (guidelines) need to be there Importance of a common referential Data System Architecture Beneficiary registration Site / community Assessment Activity monitoring Site Who’s doing what where? Multi sectoral assessment: -Health -Education -Water Bio Data Vulnerability Needs Demographics Project activities description Performance Indicators Base indicators Delivered Assistance Infrastructure Inventory Organization

7 Data Management & Data Lifecycle System architecture Data System Architecture Building an Interface for data collection: Mobile Offline desktop Web/Server based OCR* ready form (can be scanned) Integration of external data source (ETL**) Offering analysis capacity (OLAP*** and Stats) * Mechanical or electronic translation of scanned images of handwritten, typewritten or printed text into machine-encoded text ** Extract, transform, and load (ETL) is a process in database usage that involves Extracting data from outside sources, Transforming it to fit operational needs (which can include quality levels), Loading it into the end target (database or data warehouse) *** An OLAP (Online analytical processing) cube is a data structure that allows fast analysis of data.

8 Data Management & Data Lifecycle Reports are part of the data system Queries and tools to extract data from the databases need to be designed along with the database Must give abilities for reporting officers to -Set up queries and reports without high level IT knowledge -To be clear on the standard indicators these queries should be based on Data System Architecture

9 Data Management & Data Lifecycle Data collection strategies Direct coordination with partners ex : Somali protection cluster Establishment of a « data collection project » ex : UNOPS Goma Specific Contract with a dedicated partner Ex: CartONG in Uganda Data collection management

10 Data Management & Data Lifecycle Implementation matrix Avoid conflict of interest Data collection management

11 Data Management & Data Lifecycle PDF reports and maps Targets mostly local partners and decision makers Can be disseminated through mailing list (cf Somali protection) Google group (cf Goma Update) Website (cf ReliefWeb) Data Dissemination

12 Data Management & Data Lifecycle GeoPortal and Open Data API GeoPortal: is a tool to ensure institutional memory and “Master Data” management Can be a tool for desk officers to visualize a situation and use map extracts in their reporting Data API: Can be used for global dissemination: cf Worldbank Data API or Google public data Offers material for data journalism (e.g. computer assisted reporting on data through journalists) Data Dissemination

13 Data Management & Data Lifecycle Data, Law & License For all data sets that do not fall under the “Guidelines for the Regulation of Computerized Personal Data Files” (for instance protection data) …. …. The “Open database license” (ODBL) can give a legal frame to all our data collection activities Data Dissemination

14 Data Management & Data Lifecycle Providing support for the 4 phases of the process Conclusion 4 specific types of expertise that are difficult to combine in one profile: Statistician/Analyst: Creating a questionnaire and compiling analyzing the resulting statistics IS Architect: Building the information system Manager: Managing the stakeholder consultation process during the design phase, the collection in the field and dissemination of results Data journalist: Developing sound and sexy reports Need to find where are the gap among the “Operation Data Management” officers network Need to define the training & support need for each of those specific domains

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