{"id":17651,"date":"2021-08-31T10:55:55","date_gmt":"2021-08-31T08:55:55","guid":{"rendered":"https:\/\/www.dase-analytics.com\/blog\/?p=17651\/"},"modified":"2021-08-31T10:57:37","modified_gmt":"2021-08-31T08:57:37","slug":"analytics-news-august-2021","status":"publish","type":"post","link":"https:\/\/www.dase-analytics.com\/blog\/sk\/analytics-news-august-2021\/","title":{"rendered":"Augustov\u00e9 analytick\u00e9 novinky s DASE"},"content":{"rendered":"<h2>Vizualizujte va\u0161e KPI pomocou nov\u00e9ho typu grafu<\/h2>\n<p>V Data Studiu sa pred p\u00e1r d\u0148ami objavil nov\u00fd typ grafu, tzv. Gauge chart. O \u010do ide? Je to nie\u010do ako cifern\u00edk v aute &#8211; ukazuje v\u00e1m aktu\u00e1lnu hodnotu metriky a ve\u013emi \u013eahko z neho od\u010d\u00edtate, ako \u010faleko je v\u00e1\u0161 cie\u013e. Tento graf je preto vhodn\u00fd najm\u00e4 na zobrazanie va\u0161ich <a href=\"https:\/\/www.dase-analytics.com\/blog\/sk\/5-dolezitych-ukazovatelov-kpi-v-sluzbe-google-analytics\/\">k\u013e\u00fa\u010dov\u00fdch ukazovate\u013eov (KPI)<\/a>.<\/p>\n<p><img decoding=\"async\" loading=\"lazy\" class=\"size-full wp-image-17821 aligncenter\" src=\"https:\/\/www.dase-analytics.com\/blog\/wp-content\/uploads\/gauge-chart.png\" alt=\"gauge chart data studio\" width=\"675\" height=\"432\" srcset=\"https:\/\/www.dase-analytics.com\/blog\/wp-content\/uploads\/gauge-chart.png 675w, https:\/\/www.dase-analytics.com\/blog\/wp-content\/uploads\/gauge-chart-300x192.png 300w, https:\/\/www.dase-analytics.com\/blog\/wp-content\/uploads\/gauge-chart-600x384.png 600w\" sizes=\"(max-width: 675px) 100vw, 675px\" \/><\/p>\n<p>Graf obsahuje \u010d\u00edseln\u00fd \u00fadaj, podobne ako v pr\u00edpade <a href=\"https:\/\/www.dase-analytics.com\/blog\/sk\/google-data-studio-prehlady-scorecards\/\">Scorecards<\/a> a navy\u0161e v\u00e1m zobrazuje nieko\u013eko d\u00f4le\u017eit\u00fdch inform\u00e1ci\u00ed:<\/p>\n<ol>\n<li>Pruhy na pozad\u00ed reprezentuj\u00fa v\u00fdkonnostn\u00e9 rozsahy (napr. n\u00edzky, stredn\u00fd, vysok\u00fd)<\/li>\n<li>Porovnanie s minul\u00fdm obdob\u00edm<\/li>\n<li>Aktu\u00e1lna hodnota metriky<\/li>\n<li>Nastaven\u00fd cie\u013e<\/li>\n<\/ol>\n<p>Podobne ako v\u0161etky ostatn\u00e9 grafy v Data Studiu, aj Gauge chart sa d\u00e1 ve\u013emi jednoducho a intuit\u00edvne upravova\u0165 pod\u013ea va\u0161ich potrieb. Za najv\u00e4\u010d\u0161iu nev\u00fdhodu pova\u017eujem fakt, \u017ee cie\u013e je mo\u017en\u00e9 nastavi\u0165 len manu\u00e1lne, teda tak, \u017ee do pr\u00edslu\u0161nej kolonky vlo\u017e\u00edte \u010d\u00edseln\u00fa hodnotu a nie je tu mo\u017en\u00e9 pou\u017ei\u0165 \u00fadaj napr\u00edklad z Google Sheets alebo hodnotu parametra.<\/p>\n<h2>Archiv\u00e1cia vlastn\u00fdch dimenzi\u00ed a metr\u00edk v GA4<\/h2>\n<p>Najnov\u0161ia aktualiz\u00e1cia Google Analytics 4 upravuje pravidl\u00e1 a sp\u00f4sob archiv\u00e1cia <a href=\"https:\/\/www.dase-analytics.com\/blog\/sk\/vyraz\/vlastna-dimenzia-custom-dimension\/\">vlastn\u00fdch dimenzi\u00ed<\/a> a <a href=\"https:\/\/www.dase-analytics.com\/blog\/sk\/vyraz\/metrika-metric\/\">metr\u00edk<\/a>. Touto zmenou by sa mala zabezpe\u010di\u0165 vy\u0161\u0161ia miera transparentnosti, \u010do je ur\u010dite na mieste, nako\u013eko ide o nevratn\u00fd krok.<\/p>\n<p>Ke\u010f sa u\u017e\u00edvate\u013e rozhodne archivova\u0165 vlastn\u00fa dimenziu alebo metriku, zobraz\u00ed sa mu s\u00fahrnn\u00e1 karta so zoznamom publ\u00edk a reportov, kde je vlastn\u00e1 dimenzia \u010di metrika pou\u017eit\u00e1. U\u017e\u00edvate\u013e tak bude presne informovan\u00fd, na \u010do v\u0161etko bude ma\u0165 archiv\u00e1cia dimenzie \u010di metriky priamy vplyv. A ako vyzer\u00e1 archiv\u00e1cia vlastnej dimenzie \u010di metriky v praxi?<\/p>\n<ul>\n<li><strong>Publikum<\/strong>: ak je dimenzia alebo metrika pou\u017eit\u00e1 v publiku, toto publikum prestane by\u0165 akt\u00edvne v momente archiv\u00e1cie dimenzie \u010di metriky. V\u0161etky remarketingov\u00e9 zoznamy zalo\u017een\u00e9 na exportovanom publiku bud\u00fa na\u010falej fungova\u0165, ale toto publikum prestane zbiera\u0165 nov\u00fdch u\u017e\u00edvate\u013eov.<\/li>\n<li><strong>Reporty (Explorations) a segmenty<\/strong>: reporty a segmenty sa v momente archiv\u00e1cie stan\u00fa neplatn\u00fdmi a nena\u010d\u00edtaj\u00fa sa. Ak budete chcie\u0165 report alebo segment na\u010d\u00edta\u0165, mus\u00edte z defin\u00edcie odstr\u00e1ni\u0165 archivovan\u00fa vlastn\u00fa dimenziu \u010di metriku.<\/li>\n<\/ul>\n<h2>Nov\u00fd bezplatn\u00fd kurz o Data Studiu je tu!<\/h2>\n<p>My v DASE Data Studio milujeme &#8211; <a href=\"https:\/\/www.dase-analytics.com\/blog\/sk\/strava-report-v-data-studiu\/\">tento bezplatn\u00fd vizualiza\u010dn\u00fd n\u00e1stroj dok\u00e1\u017ee divy<\/a>. Vieme v\u0161ak, \u017ee za\u010diatky s\u00fa v\u017edy \u0165a\u017ek\u00e9, ale odteraz u\u017e nemusia by\u0165! Google predstavil v r\u00e1mci svojej Analytics Academy nov\u00fa s\u00e9riu vide\u00ed, v ktor\u00fdch predstavuje Google Data Studio naozaj p\u00fatavou a zrozumite\u013enou formou. Tento kurz je vhodn\u00fd nielen pre za\u010d\u00ednaj\u00facich webov\u00fdch analytikov a market\u00e9rov, ale aj pre majite\u013eov firiem, ktor\u00ed chc\u00fa ma\u0165 preh\u013ead vo svojich d\u00e1tach.<\/p>\n<p>Kurz v\u00e1s nau\u010d\u00ed vytv\u00e1ra\u0165 p\u00f4sobiv\u00e9 interakt\u00edvne dashboardy, ktor\u00e9 m\u00f4\u017eete zdie\u013ea\u0165 so svojimi klientmi \u010di spolupracovn\u00edkmi. Pozrite si \u00favodn\u00e9 video, alebo sa rovno prihl\u00e1ste do online kurzu <a href=\"https:\/\/analytics.google.com\/analytics\/academy\/course\/10\">na tomto linku<\/a>.<\/p>\n<p><iframe loading=\"lazy\" title=\"YouTube video player\" src=\"https:\/\/www.youtube.com\/embed\/4lPmu8fsOHc\" width=\"920\" height=\"518\" frameborder=\"0\" allowfullscreen=\"allowfullscreen\"><\/iframe><\/p>\n<p>V pr\u00edpade, \u017ee sa analytike chcete venova\u0165 hlb\u0161ie, v <a href=\"https:\/\/analytics.google.com\/analytics\/academy\/\">Google Analytics Academy<\/a> n\u00e1jdete kurzy tak pre za\u010diato\u010dn\u00edkov, ako aj pre pokro\u010dil\u00fdch. A \u017ee sa jedn\u00e1 o kvalitu potvrdzuje fakt, \u017ee na pr\u00edprave kurzov sa spolupodie\u013eali tak\u00e9 es\u00e1 ako Krista Seiden, Justin Cutroni a Ashish Vij.<\/p>\n<h2>BigQuery podporuje nov\u00e9 pr\u00edkazy jazyka DDL<\/h2>\n<p>V\u010faka dvom nov\u00fdm pr\u00edkazom DDL (Data Description Language) v <a href=\"https:\/\/www.dase-analytics.com\/blog\/sk\/vyraz\/bigquery\/\">BigQuery<\/a> m\u00f4\u017eete ve\u013emi jednoducho vytv\u00e1ra\u0165 nov\u00e9 tabu\u013eky priamo z SQL pr\u00edkazu. Novo podporovan\u00e9 pr\u00edkazy s\u00fa:<\/p>\n<ul>\n<li><a href=\"https:\/\/cloud.google.com\/bigquery\/docs\/reference\/standard-sql\/data-definition-language#create_table_like\" target=\"_blank\" rel=\"noopener noreferrer\">CREATE TABLE LIKE<\/a> &#8211; pou\u017eite v pr\u00edpade, ak chcete vytvori\u0165 nov\u00fa tabu\u013eku so v\u0161etk\u00fdmi rovnak\u00fdmi metad\u00e1tami inej tabu\u013eky, a to tak, \u017ee nahrad\u00edte zoznam st\u013apcov klauzulou LIKE, ke\u010f pou\u017eijete pr\u00edkaz CREATE TABLE.<\/li>\n<li><a href=\"https:\/\/cloud.google.com\/bigquery\/docs\/reference\/standard-sql\/data-definition-language#create_table_copy\" target=\"_blank\" rel=\"noopener noreferrer\">CREATE TABLE COPY<\/a> &#8211; pou\u017eite v pr\u00edpade, ak chcete vytvori\u0165 nov\u00fa tabu\u013eku s rovnak\u00fdmi metad\u00e1tami a \u00fadajmi z inej tabu\u013eky, a to tak, \u017ee nahrad\u00edte zoznam st\u013apcov klauzulou COPY, ke\u010f pou\u017eijete pr\u00edkaz CREATE TABLE.<\/li>\n<\/ul>\n<p>\u017de e\u0161te st\u00e1le neviete, \u010do je to BigQuery a ako by mohlo pom\u00f4c\u0165 v\u00e1\u0161mu biznisu? Sta\u010d\u00ed ak sa prihl\u00e1site do n\u00e1\u0161ho newslettera a my v\u00e1m to prostredn\u00edctvom na\u0161ich blogov a vide\u00ed radi vysvetl\u00edme.<\/p>\n<h2>Vysk\u00fa\u0161ajte si pr\u00e1cu s BigQuery so vzorov\u00fdm datasetom od Googlu<\/h2>\n<p>Ak v\u00e1s BigQuery l\u00e1ka, ale nem\u00e1te vlastn\u00fd zdroj d\u00e1t, na ktor\u00fdch by ste si pr\u00e1cu s n\u00edm vysk\u00fa\u0161ali, m\u00f4\u017eete pou\u017ei\u0165 d\u00e1ta z Google Merchandise Store. Google spr\u00edstupnil BigQuery dataset, ktor\u00fd obsahuje export d\u00e1t z GA4 vlastn\u00edctva z obdobia 1. novembra 2020 do 31. janu\u00e1ra 2021.<\/p>\n<div class=\"custom-button\"><a href=\"https:\/\/console.cloud.google.com\/bigquery?p=bigquery-public-data&#038;d=ga4_obfuscated_sample_ecommerce&#038;t=events_20210131&#038;page=table\"><span>Stiahnite si vzorov\u00fd dataset do BigQuery<\/span><\/a><\/div>\n<p>Pre pr\u00e1cu s BigQuery potrebujete ma\u0165 vytvoren\u00e9 konto v <a href=\"https:\/\/cloud.google.com\/\" target=\"_blank\" rel=\"noopener noreferrer\">Google Cloud Platform<\/a>. BigQuery m\u00f4\u017eete s ur\u010dit\u00fdmi obmedzeniami pou\u017e\u00edva\u0165 aj zadarmo. Ak v\u00e1m tak\u00fdto pr\u00edstup sta\u010di\u0165 nebude, m\u00f4\u017eete si nastavi\u0165 faktur\u00e1ciu a vyu\u017ei\u0165 tak pln\u00fd potenci\u00e1l, ktor\u00fd BigQuery pon\u00faka. Pozn\u00e1mo\u010dka nakoniec: d\u00e1ta v tomto vzorovom datasete sa nebud\u00fa zhodova\u0165 s d\u00e1tami v GA4 vlastn\u00edctve Google Merchandise Store &#8211; je to len vzorov\u00fd dataset a nie o skuto\u010dn\u00e9 Raw d\u00e1ta z GA4 vlastn\u00edctva.<\/p>\n","protected":false},"excerpt":{"rendered":"<p>Vizualizujte va\u0161e KPI pomocou nov\u00e9ho typu grafu V Data Studiu sa pred p\u00e1r d\u0148ami objavil nov\u00fd typ grafu,&#8230;<\/p>\n","protected":false},"author":70,"featured_media":17828,"comment_status":"open","ping_status":"open","sticky":false,"template":"","format":"standard","meta":[],"categories":[669],"tags":[798,668],"_links":{"self":[{"href":"https:\/\/www.dase-analytics.com\/blog\/sk\/wp-json\/wp\/v2\/posts\/17651"}],"collection":[{"href":"https:\/\/www.dase-analytics.com\/blog\/sk\/wp-json\/wp\/v2\/posts"}],"about":[{"href":"https:\/\/www.dase-analytics.com\/blog\/sk\/wp-json\/wp\/v2\/types\/post"}],"author":[{"embeddable":true,"href":"https:\/\/www.dase-analytics.com\/blog\/sk\/wp-json\/wp\/v2\/users\/70"}],"replies":[{"embeddable":true,"href":"https:\/\/www.dase-analytics.com\/blog\/sk\/wp-json\/wp\/v2\/comments?post=17651"}],"version-history":[{"count":18,"href":"https:\/\/www.dase-analytics.com\/blog\/sk\/wp-json\/wp\/v2\/posts\/17651\/revisions"}],"predecessor-version":[{"id":17834,"href":"https:\/\/www.dase-analytics.com\/blog\/sk\/wp-json\/wp\/v2\/posts\/17651\/revisions\/17834"}],"wp:featuredmedia":[{"embeddable":true,"href":"https:\/\/www.dase-analytics.com\/blog\/sk\/wp-json\/wp\/v2\/media\/17828"}],"wp:attachment":[{"href":"https:\/\/www.dase-analytics.com\/blog\/sk\/wp-json\/wp\/v2\/media?parent=17651"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/www.dase-analytics.com\/blog\/sk\/wp-json\/wp\/v2\/categories?post=17651"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/www.dase-analytics.com\/blog\/sk\/wp-json\/wp\/v2\/tags?post=17651"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}