BI thiab Predictive Analytics ua hauj lwm ua ke – Cov ntaub ntawv loj npaum li cas yuav pab tau?
Txheej txheem cej luam:
Cov ntaub ntawv loj yog muab ua lag ua luam txoj kev ntse (LIS) Ib lub neej tshiab raws li nws tam sim no pub txim siab txiav txim siab ua txoj hau kev yuav los kwvyees. Raws li txhua hnub kis tau, Ntxiv thiab ntau cov ntaub ntawv yog sau, uas tau pom muaj thaum integrated nrog lub lag luam tam sim no. Nyob rau yav tom ntej, Lag luam yuav tau ib zaug xwb view tsaug ntaub ntawv—uas muaj eluded txim makers kuj ntev heev tam sim no. Cov ntaub ntawv loj yog transforming yuav luag txhua qhov kev lag luam. Nrog ntau cov ntaub ntawv sau txhua hnub, cov neeg ua hauj lwm seem muab cov ntaub ntawv no los siv zoo kawg thiab. Koj yuav outsource muaj ntaub ntawv ua hauj lwm los ntawm ib tug cov ntaub ntawv engineers, Leej twg yuav siv cov chaw muab kev pab thiab cov ntaub ntawv ntawm lawv pov tseg coj koj lub lag luam rau ib theem tshiab. Ntaub ntawv loj yog xa hau zaws. Nws muaj transformed tus txiv neej uas cov neeg leverage cov ntaub ntawv uas siv lub hwj chim merger ntawm kev lag luam txoj kev ntse thiab predictive analytics.
Cov ntaub ntawv loj ua analytics tau, txawm tias muaj neeg tsis nco qab
Yav tag los, Cov neeg muaj xav tias predictive tsom tau rau cov kws txawj uas tau tsim nyog, Nrog ob lub peev xwm to taub lub tshuab uas yog siv thiab kev txawj ntse, zoo ib yam li ib tug txheeb cais los yog ib tug paub txog tus paub txog. Tam sim no, tsaug ntaub ntawv loj, Tsom xam predictive yuav txawm yuav nyob ntawm kab-level tswj, Cov ntaub ntawv uas siv cov ntaub ntawv uas tsis cia tej muas thiab ces ua mus sij hawm ntev thiab xaiv yaam kev txiav txim raws li cov ntaub ntawv. Tam sim no txawm tias neeg nqa tawm predictive tsom xam nrog cov pab ntaub ntawv loj, tus nqi lag luam txoj kev ntse mas nws yeej yuav nce, ua rau nws zoo kawg thiab siv rau ib lub lag luam.
Rov saib dua unstructured kev tawm cov ntaub ntawv
Kev tawm yog ib yam ntawm cov chaw uas cov ntaub ntawv yuav tau generated. Ntau cov lag luam tau muab rau kev tawm thaum nws tuaj cuag cov ntaub ntawv thiab tau txais kev los ntawm cov neeg muas zaub. Cov tasks muaj kuj tau yooj yim ntawm disease fact tias muaj txhiab tus neeg siv kev tawm tam sim no, thiab kuj heev heev kev tawm platforms. Cov ntaub ntawv generated ntawm kev tawm yuav muab siv los ntawm cov lag luam los pab lawv cov neeg muas zaub ob cov neeg muas zaub thiab xav tau. Tag nrho cov no yuav tiav los ntawm ib tug fusion ntawm qhov chaw qhib tshuab, muag khoom thiab cov xov xwm, thiab muaj peev xwm muaj peev xwm mus sau thiab khw loj loj, cov ntaub ntawv uas tsis yog-transactional tagnrho.
Ntau yuam kev tsom xam
Ntawv loj Muaj kuj tswj kom txo tus nqi lub sij hawm coj mus tsom xam cov ntaub ntawv ua lag ua luam, Raws li cov ntaub ntawv no feem ntau yog muab cia rau hauv ib huab. Yav tas los, it took data scientists a considerable amount of time to come up with a model that would enable them to analyze huge data volumes so that businesses were able to answer questions with regards to supply chain and production schedules. Thaum lub caij, the analyzing speed has greatly increased, as big data used together with machine learning is able to generate thousands of data models for each product line in every market. This is enabling users to be able to forecast what is likely to happen in the coming one and a half years.
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Xaus
While all these appear promising, the power of big data has not been fully utilized to its maximum potential. Txawm tias cuab yeej nyiam Hadoop uas pom tias yuav haib tig kom tau primitive thaum nws tawm los mus tuav tej teeb meem uas muaj ntaub ntawv loj loj. Nws yuav tau fascinating saib seb cov ntaub ntawv loj kiv puag nraud tawm.
Tej zaum koj yuav nyeem ntau nyeem ntxiv big Data tsab xov xwm.
