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In spite of the successful examples listed in Table 6-2above, many challenges still remain in SPI in SIDS, includinginadequate communication between researchers andpolicymakers and the related tendency of line ministries towork in isolation from one another and for researchinstitutions to operate with a silo structure. A tendency touse the same group of experts again and again, forinstance, or appointing scientists to advisory boards whoact as only passive members have been noted asdrawbacks. The outcome documents from differentconferences on SIDS (see Figure 6-1 above) haveemphasized the importance of regional institutions andinter-regional collaboration, which needs to be enhancedfurther.6.5. Data gapsThe report of the UN Secretary-General’s Expert AdvisoryGroup on a Data Revolution for Sustainable Development,A World That Counts: Mobilising The Data Revolution forSustainable Development 525 , recognized data as a basis forensuring that no one is left behind. Reliable data,disaggregated in such a way that no one is invisible, is keyto inform decision makers. Yet, crucial data is missing incountries in special situations. Although most of thesecountries are able to conduct a census – 83% of LDCs, 91%of LLDCs and all SIDS have conducted at least one censussince 2005 526 – these data are typically available only everyten years. Due to their low frequency, census data has tobe complemented with reliable administrative records orsurveys. But in many countries reliable administrativerecords do not exist and surveys are scarce. For instance,since 2005, only 46% of LDCs, 75% of LLDCs and 53% ofSIDS have conducted a labour force survey 527 – one of themain sources of data on employment. An analysis of dataavailable in official international databases on 17 indicatorsrelated to diverse topics pointed out some data gaps (seeFigure 6-9).Since 2005, very few SIDS have data on poverty or oninequality measures – all these indicators require livingstandards measurement surveys, which are costly for SIDSdue to small populations. Since sampling errors do notchange much with population size, the per capita cost ofreaching similar levels of sampling errors tend to be higherfor smaller countries compared with larger countries. 528Data on slum populations that relies on census or surveys isalso not available for many countries. In this as in otherareas, innovative approaches to data gathering may be ableto fill gaps, for example by using information andcommunication technology (ICT) to support communitybasedenumeration and mapping in human settlements 529 .Other data that relies primarily on surveys – like water andelectricity access – are more widely available becauseinternational agencies apply models and produce estimatesto fill data gaps. Although models can produce informativeestimates to guide policymaking, it is unclear if countries inspecial situations have the capacity to develop their ownmodels and use these estimates to inform their policydecisions. Data that relies on inventories or easy-tomaintainadministrative records – like CO2 emissions andforest area – are more widely available. But administrativedata systems that require more resources – such as lists ofenrolled students and their gender, or registered births –are not so widely available.With cheap cell phones and increasingly cheaper satelliteimages, indicators based on big data may be explored tocomplement survey data gaps (see Chapter 7, section onBig Data). In Senegal, cell phone detailed records have beenused to estimate literacy rates at very fine geographicalresolution 530 (see also Chapter 8). In Haiti, social and newsmedia have been used to monitor cholera outbreaks. 531Some LDCs are also using mobile devices to collect datafaster and with reduced costs (see e.g. Chapter 8 for use ofmobile devices in data collection in Burkina FasoMozambique and Senegal).120