{"id":8464,"date":"2015-10-29T17:33:03","date_gmt":"2015-10-29T16:33:03","guid":{"rendered":"http:\/\/www.solucominsight.fr\/?p=8464"},"modified":"2015-10-29T17:52:16","modified_gmt":"2015-10-29T16:52:16","slug":"demythifier-la-magie-du-big-data-le-processus-dexploitation-de-la-donnee","status":"publish","type":"post","link":"https:\/\/www.riskinsight-wavestone.com\/en\/2015\/10\/demythifier-la-magie-du-big-data-le-processus-dexploitation-de-la-donnee\/","title":{"rendered":"D\u00e9mythifier la magie du Big data : le processus d’exploitation de la donn\u00e9e"},"content":{"rendered":"

Les r\u00e9flexions autour des cas d\u2019usage sont en pleine \u00e9bullition pour nourrir la n\u00e9cessaire transformation du secteur de l\u2019Assurance. Les sources de donn\u00e9es sont pour certaines ais\u00e9ment accessibles.Par exemple, les objets connect\u00e9s domotiques permettent de conna\u00eetre l\u2019activit\u00e9 d\u2019une maisonn\u00e9e\u00a0: \u00e9tat allum\u00e9\u00a0\/\u00a0\u00e9teint des lampes, niveau de consommation \u00e9lectrique, de CO2 \u2026 Mais ces donn\u00e9es brutes, sans transformation, ne sont pas tr\u00e8s utiles. Elles sont un peu comme le bois pour celui qui souhaite construire un chalet. Un processus de transformation est indispensable pour que le bois devienne chalet. De m\u00eame, un processus Big data est indispensable pour que ces donn\u00e9es deviennent signifiantes pour un cas d\u2019usage.<\/em><\/p>\n

DONNER UN SENS AUX DONN\u00c9ES<\/h1>\n

Attention ! Il ne suffit pas d\u2019appliquer un processus pr\u00e9tendument magique sur un ensemble de donn\u00e9es h\u00e9t\u00e9roclites pour qu\u2019un enseignement pertinent \u00e9merge. Un processus Big data doit passer par diff\u00e9rentes phases pour faire ressortir le pouvoir pr\u00e9dictif inclus dans les donn\u00e9es manipul\u00e9es.<\/p>\n

Nettoyer les donn\u00e9es<\/h3>\n

Les donn\u00e9es brutes doivent d\u2019abord \u00eatre rendues valides\u00a0: une ampoule indiquant qu\u2019elle est pass\u00e9e deux fois de suite en mode \u00ab\u00a0allum\u00e9\u00a0\u00bb sans \u00eatre pass\u00e9e par le mode \u00ab\u00a0\u00e9teint\u00a0\u00bb constitue une anomalie qui doit \u00eatre corrig\u00e9e. Une \u00e9tape pr\u00e9liminaire de nettoyage des donn\u00e9es doit \u00eatre pratiqu\u00e9e avant toute exploitation pour corriger\u00a0\/\u00a0valider les donn\u00e9es.<\/p>\n

Interpr\u00e9ter et contextualiser<\/h3>\n

Il faut ensuite donner un sens \u00e0 ces donn\u00e9es \u00e9pur\u00e9es de toutes anomalies. En effet, savoir qu\u2019une lampe est allum\u00e9e ou \u00e9teinte n\u2019apporte pas d\u2019information en elle-m\u00eame si ce n\u2019est par son interpr\u00e9tation qu\u2019entre ces deux \u00e9tats, le logement est pr\u00e9sum\u00e9 occup\u00e9. Par ailleurs, si l\u2019on recoupe cela avec les donn\u00e9es provenant du d\u00e9tecteur de CO2 , du d\u00e9tecteur d\u2019ouverture\u00a0\/\u00a0fermeture de porte, voire de la cam\u00e9ra avec reconnaissance faciale, il est alors possible de pouvoir conclure plus certainement que le logement est occup\u00e9 (et par qui). Cette t\u00e2che de recoupement permet d\u2019\u00e9liminer le cas o\u00f9 l\u2019occupant serait simplement parti en laissant la lumi\u00e8re allum\u00e9e (ce qu\u2019on appelle un faux positif). Il est alors possible, \u00e0 partir d\u2019un recoupement d\u2019informations et d\u2019une contextualisation \u00e0 bon escient, de d\u00e9duire d\u2019une donn\u00e9e brute plusieurs dizaines de donn\u00e9es interpr\u00e9t\u00e9es ayant acquis un sens. Cela permet d\u2019obtenir des donn\u00e9es caract\u00e9risant l\u2019intensit\u00e9, la fr\u00e9quence, la r\u00e9gularit\u00e9 et plus g\u00e9n\u00e9ralement le mode d\u2019utilisation du bien assur\u00e9. Cette \u00e9tape est cruciale car c\u2019est celle qui permet de d\u00e9gager les donn\u00e9es d\u2019int\u00e9r\u00eat ayant une signification par rapport \u00e0 l\u2019objectif \u00ab\u00a0m\u00e9tier\u00a0\u00bb qui aura \u00e9t\u00e9 assign\u00e9 au processus Big data. En effet, une mauvaise interpr\u00e9tation \u00e0 cette \u00e9tape peut invalider une donn\u00e9e pertinente et potentiellement aboutir par la suite \u00e0 des conclusions erron\u00e9es ou moins pr\u00e9cises (laissant passer de faux positifs). Il faut donc allouer un temps suffisant aux indispensables phases de pr\u00e9paration afin de qualifier les donn\u00e9es avant de se lancer dans leur analyse.<\/p>\n

Analyser<\/h3>\n

L\u2019analyse consiste \u00e0 identifier les populations qui ont les caract\u00e9ristiques recherch\u00e9es et celles qui en sont\u00a0divergentes. C\u2019est \u00e0 ce stade qu\u2019intervient la puissance des traitements massivement parall\u00e8les autorisant l\u2019exploitation d\u2019algorithmes machine. Ces algorithmes permettent de distinguer, dans la myriade d\u2019informations, les signaux faibles traduisant un comportement ou une situation qui ne peut \u00eatre d\u00e9couvert autrement. Cette approche sera par exemple particuli\u00e8rement utile pour r\u00e9v\u00e9ler, parmi les populations actuellement difficilement cern\u00e9es par les mod\u00e8les traditionnels, les jeunes automobilistes masculins qui ont des comportements moins \u00e0 risques. Le concept du machine learning<\/em> est totalement nouveau dans le secteur de l\u2019Assurance, dirig\u00e9 par des processus lin\u00e9aires et industriels. Associ\u00e9 aux bases de donn\u00e9es Big data, le machine learning<\/em> va permettre d\u2019enrichir tout au long du cycle de vie des produits d\u2019assurance, des analyses de donn\u00e9es et des nouvelles corr\u00e9lations. Cette technologie n\u2019est pas en opposition avec la Business Intelligence<\/em> mais bien en compl\u00e9ment pour des usages nouveaux. Elle permet d\u2019avoir une vision prescriptive, alors que la Business Intelligence<\/em> fournit une vision descriptive.<\/p>\n

Calibrer<\/h3>\n

Une fois ces populations diff\u00e9renci\u00e9es, il reste \u00e0 distinguer l\u2019intensit\u00e9 de l\u2019effet de cette diff\u00e9renciation sur la variable d\u2019int\u00e9r\u00eat objet de l\u2019\u00e9tude. Cette phase de calibrage est importante car il se peut que des caract\u00e9ristiques tr\u00e8s divergentes n\u2019aient que tr\u00e8s peu d\u2019impact et viceversa. C\u2019est ce qui fait que ce processus exploratoire est un processus it\u00e9ratif. En effet, quand la pertinence du mod\u00e8le n\u2019est pas au rendez-vous, c\u2019est-\u00e0-dire que la r\u00e9ponse n\u2019est pas suffisamment pr\u00e9dictive, il faut recommencer. Il est alors n\u00e9cessaire d\u2019identifier si une meilleure interpr\u00e9tation\u00a0\/\u00a0contextualisation des donn\u00e9es pourrait apporter plus d\u2019enseignements et\u00a0\/\u00a0ou si d\u2019autres sources d\u2019information pourraient apporter des \u00e9clairages nouveaux.<\/p>\n

PROCESSUS IT\u00c9RATIF<\/h1>\n

L\u2019it\u00e9ration du processus Big data est l\u2019aspect le plus d\u00e9concertant pour un assureur habitu\u00e9 au processus lin\u00e9aire R&D traditionnel. N\u00e9anmoins, il s\u2019agit bel et bien de l\u2019ADN de tout processus Big data o\u00f9 le cycle d\u2019accroissement de la connaissance par am\u00e9lioration des mod\u00e8les est un processus non seulement it\u00e9ratif, mais continu. \u00c0 tel point que dans un rythme de croisi\u00e8re, on ne saura plus distinguer l\u2019\u00e9l\u00e9ment premier dans la d\u00e9marche\u00a0:<\/p>\n

    \n
  • Est-ce la recherche d\u2019\u00e9v\u00e9nements permettant de caract\u00e9riser un objectif m\u00e9tier, puis la recherche de donn\u00e9es permettant d\u2019identifier cet \u00e9v\u00e9nement\u00a0?<\/li>\n
  • Est-ce le processus de qualification permettant de valider l\u2019int\u00e9r\u00eat de ces donn\u00e9es par rapport \u00e0 l\u2019objectif m\u00e9tier ?<\/li>\n<\/ul>\n

    L\u2019ILLUSION DE LA MAGIE : PR\u00c9DICTION DES EFFETS<\/h1>\n

    Le processus d\u2019exploitation des donn\u00e9es ne rel\u00e8ve pas de la magie. Il s\u2019agit d\u2019un processus qui tend \u00e0 reproduire les d\u00e9cisions humaines dont une part est li\u00e9e \u00e0 la combinaison d\u2019\u00e9l\u00e9ments tr\u00e8s diffus (signaux faibles) que la prolif\u00e9ration de nouvelles donn\u00e9es rend lisibles et interpr\u00e9tables. Le constat par les algorithmes que les m\u00eames circonstances produisent les m\u00eames effets permet d\u2019en pr\u00e9dire la survenance (voire prescrire des mesures de pr\u00e9vention). Et ce, sans qu\u2019il ne soit n\u00e9cessaire d\u2019\u00e9tablir une relation de cause \u00e0 effet entre ces circonstances et ces effets. C\u2019est probablement ce qui donne l\u2019illusion de la magie \u00e0 un processus tout \u00e0 fait rationnel.<\/p>\n

    \"\"<\/figure>\n

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