{"id":"W4386751557","doi":"10.9734/jsrr/2023/v29i91785","title":"Utilizing Big Data Analytics and Business Intelligence for Improved Decision-Making at Leading Fortune Company","year":2023,"lang":"en","type":"article","venue":"Journal of Scientific Research and Reports","topic":"Big Data and Business Intelligence","field":"Business, Management and Accounting","cited_by":45,"is_retracted":false,"has_abstract":true,"ca_institutions":"Independent Electricity System Operator","funders":"","keywords":"Big data; Business intelligence; Business analytics; Analytics; Data science; Social media analytics; Computer science; SPARK (programming language); Data analysis; Social media; Customer engagement; Software analytics; Knowledge management; Business; Business model; Marketing; Business analysis; World Wide Web; Data mining","routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":false,"invisible_to_affiliation_only":false},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.005560193,0.0006985206,0.0002707101,0.002853457,0.004557225,0.01402066,0.0010246,0.001016893,0.005270293],"category_scores_gemma":[0.01137379,0.0003715506,0.0003983422,0.002860327,0.0008875998,0.00562042,0.001875453,0.001529529,0.001501106],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.005694869,"about_ca_system_score_gemma":0.01145263,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0186225,"about_ca_topic_score_gemma":0.03464922,"domain_scores_codex":[0.9965333,0.0009696612,0.0001280555,0.0003762495,0.00144522,0.0005474946],"domain_scores_gemma":[0.9870064,0.003918327,0.001210796,0.000646585,0.004483949,0.002733936],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"not_applicable","study_design_scores_codex":[0.0005818426,0.001163252,0.1328353,0.0007064859,0.0002408167,0.001850658,0.008399139,0.009689382,0.004722489,0.03846132,0.09824517,0.7031042],"study_design_scores_gemma":[0.0001531934,0.001666467,0.1221348,0.002296975,0.0005058837,0.0009038375,0.06134005,0.1152683,0.01918277,0.07647651,0.5993761,0.0006950597],"study_design_candidate":"not_applicable","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.665495,0.00963583,0.04117637,0.0764152,0.0009846765,0.0006318428,0.0008381968,0.001632579,0.2031904],"genre_scores_gemma":[0.9452469,0.002883076,0.02783901,0.002666499,0.0003386039,0.00007673568,0.0005198773,0.0001042544,0.02032509],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.0186225,"threshold_uncertainty_score":0.04131931,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.4376238090589833,"score_gpt":0.4406011752077968,"score_spread":0.002977366148813554,"validation_status":"score_only:v0-immature-baseline","note":"Baseline scores from an immature model (maturity gate not passed). Scores rank; they never assert a category."}}