Data Management & Data lifecycle

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

2. Type of info per usage Introduction

3. From Data.. to Information Introduction Survey Conception Data System Architecture Data Analysis & Dissemination Data Collection management Operation Data Manager should be involved in all the steps of a “Data Lifecycle”. 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. 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. Good practices for Data Collection Forms Survey Conception • Questionnaires used in survey research should be clear and well presented. • Think about the form of the questions, • Keep the survey as short as possible. • Make definitions of data elements consistent with standard definitions and analytic conventions • Plan clearly how answers will be analyzed. • Test the survey for “understandability” and respondent effort through focus groups

6. Data model Data System Architecture • 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 Site / community Assessment Beneficiary registration Demographics • Multi sectoral assessment: • Health • Education • Water Bio Data Vulnerability Needs Site Infrastructure Inventory Base indicators Delivered Assistance Organization Who’s doing what where? Project activities description Performance Indicators Activity monitoring

7. 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. Reports are part of the data system Data System Architecture 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

9. Data collection strategies Data collection management • 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

10. Implementation matrix Data collection management Avoid conflict of interest

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

12. GeoPortal and Open Data API Data Dissemination 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)

13. Data, Law & License Data Dissemination 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 http://www.unhcr.org/refworld/pdfid/3ddcafaac.pdf http://www.opendatacommons.org/licenses/odbl/1.0

14. 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