{"id":6120,"date":"2025-04-22T08:00:10","date_gmt":"2025-04-22T06:00:10","guid":{"rendered":"https:\/\/news.sap.com\/denmark\/?p=6120"},"modified":"2025-04-23T11:31:59","modified_gmt":"2025-04-23T09:31:59","slug":"du-goer-klogt-i-at-lade-data-blive-i-driftsystemerne-til-gengaeld-boer-du-aabne-doeren","status":"publish","type":"post","link":"https:\/\/news.sap.com\/denmark\/2025\/04\/du-goer-klogt-i-at-lade-data-blive-i-driftsystemerne-til-gengaeld-boer-du-aabne-doeren\/","title":{"rendered":"Du g\u00f8r klogt i at lade data blive i driftsystemerne – til geng\u00e6ld b\u00f8r du \u00e5bne d\u00f8ren"},"content":{"rendered":"

Mens data-revolutionen forandrer drift-orienterede it-systemer ganske radikalt, st\u00e5r ledelsesinformationen stadig lidt i stampe. Symptomet er, at virksomheder til en vis grad h\u00e5ndbygger KPI-rapporteringer, Business Intelligence-systemer og data analytics-milj\u00f8er. Det sker, selv om f\u00e6llesm\u00e6ngden med naboen er oppe p\u00e5 80-90 pct.<\/p>\n

Kun 34 pct af ledere har h\u00f8j tillid virksomhedens data og dens analytiske kapacitet. 56 pct n\u00e6rer moderat tillid og 11 pct har lav tillid.<\/p>\n

Sp\u00f8rger man til virksomhedernes udfordringer p\u00e5 vejen mod pr\u00e6cise, kontekst-relevante og transparente data, placerer 55 pct af lederne datakvalitet og datakonsistens som st\u00f8rste udfordring.<\/p>\n

Problemer med at integrere data fra forskellige forretningsapplikationer ligger p\u00e5 andenpladsen med omkring 48 pct. Til sammenligning peger kun omkring 29 pct p\u00e5 mangel p\u00e5 dataspecialister.<\/p>\n

Frav\u00e6r af data og frav\u00e6r af analytiske v\u00e6rkt\u00f8jer ligger lavest nede p\u00e5 listen over udfordringer og opleves blot af omkring 20 pct.<\/p>\n

Klumpet r\u00e5-olie<\/strong><\/h2>\n

Alle disse tal kommer fra en international unders\u00f8gelse blandt 1200 ledere og CIO\u2019er gennemf\u00f8rt af SAP i slutningen af 2024 med titlen \u201dThe trust factor: Data, resilience, and innovation<\/a>\u201d.<\/p>\n

I en tid, hvor data er den nye olie, har mange virksomheder alts\u00e5 problemer med ledelsens tillid til p\u00e5lideligheden af deres data. For at blive i metaforen er der meget stor forskel p\u00e5 klumpet r\u00e5-olie og raffineret h\u00f8joktan benzin. K\u00e6den fra r\u00e5 data til ledelsesm\u00e6ssig indsigt og handlemulighed er alts\u00e5 ikke alle steder lige velsmurt.<\/p>\n

Hvis jeg kigger ud over det danske landskab, s\u00e5 tror jeg egentlig, at tilliden til ledelsesinformationen er noget h\u00f8jere. Men jeg ser ogs\u00e5, at mange virksomheder har en h\u00e6r af data scientists, data stewards, analytikere og controllere til at opbygge rapporteringer og ledelsesinformation.<\/p>\n

Potentiale for rationalisering<\/strong><\/h2>\n

Samtidig er der et overlap p\u00e5 op til 80-90 pct af den opbyggede funktionalitet. Alle \u00f8nsker jo at styre efter et s\u00e6t af KPI\u2019er, og hvis de kommer fra et ERP-, Supply Chain Management-, Finans- eller HR-system, s\u00e5 er der alts\u00e5 en meget stor f\u00e6llesm\u00e6ngde, som alle \u00f8nsker at have styr p\u00e5.<\/p>\n

Her ligger et rationaliseringspotentiale og venter p\u00e5 os gennem standardisering. Det er ikke n\u00f8dvendigt, at hver virksomhed for sig bygger de samme modeller. I stedet b\u00f8r de systemer, hvor data f\u00f8des og lever, v\u00e6re i stand til at levere f\u00e6rdige \u201ddataprodukter\u201d til analytiske form\u00e5l.<\/p>\n

Alts\u00e5 g\u00f8re det nemt at komme fra r\u00e5 driftsdata og til aggregeret overblik og kritisk KPI-styring. S\u00e5dan et \u201ddataprodukt\u201d kan m\u00e5ske d\u00e6kke 80 pct af alle virksomheders behov, og s\u00e5 kan in-house dataspecialister fokusere p\u00e5 at klarg\u00f8re data til de sidste 20 pct. Samme rationale ligger bag, at ingen virksomheder i dag egenudvikler ERP-systemer.<\/p>\n

V\u00e6rditab i kopiering <\/strong><\/h2>\n

Selv om data skal bruges til analytiske form\u00e5l b\u00f8r de bevare deres oprindelige semantik, historik og forretningsm\u00e6ssige kontekst. Vi skal g\u00f8re op med fortidens v\u00e6rditab i forbindelse med eksporten fra driftsmilj\u00f8et via ETL-processen til en data lake et data warehouse eller andre analytiske sandkasser.<\/p>\n

Men s\u00e5 er der problemet med data fra flere kilder.<\/p>\n

Vi har en meget lang tradition for rent faktisk at kopiere data v\u00e6k af deres oprindelige system og l\u00e6gge dem et andet sted af hensyn til et analytisk form\u00e5l. Her kan de s\u00e5 joines med data fra andre systemer og s\u00f8d musik i form af klog indsigt kan opst\u00e5.<\/p>\n

Forplumrede data lakes<\/strong><\/h2>\n

Brugen af data lakes er ofte en \u201dhjemmebygget\u201d vej fra data til f\u00e6rdigt resultat. Derfor sidder der mange ressourcer internt eller hos konsulentfirmaer og genneml\u00f8ber de samme ti trin for at bygge n\u00e6sten de samme beregninger p\u00e5 1000 forskellige m\u00e5der (ESG-rapportering er et aktuelt \u00a0omr\u00e5de, hvor vi ser dette dobbeltarbejde).<\/p>\n

Udover at data lakes kan blive noget forurenede, for\u00e6ldede og forplumrede, s\u00e5 vil det n\u00e6sten altid v\u00e6re langsommelige processer, og data vil tabe nogle af sine attributter i kopieringsprocessen.<\/p>\n

Det er et problem i en AI-tid, fordi vi gerne vil automatisere effekten af den analytiske indsigt i konkret handling. Det vil sige at det analytiske output, skal skabe handling i drift-systemerne og derfor skal outputtet alligevel sendes retur til kildesystemet.<\/p>\n

Glasd\u00f8r p\u00e5 dit lakehouse<\/strong><\/h2>\n

Mit bud er, at vi g\u00f8r klogt i at lade data blive i driftsystemerne, men vi skal have en fuldst\u00e6ndig transparent og \u00e5ben \u201dglasd\u00f8r\u201d fra vores lakehouse til vores driftsystemer. Det skal v\u00e6re lige s\u00e5 nemt for data scientists at arbejde med data i eksempelvis HR- eller ERP- eller CRM-systemet, som hvis de l\u00e5 i en data lake. De ligger bare i en \u201dudestue\u201d.<\/p>\n

En AI-agent skal have et interface til alle data, og det kan v\u00e6re i form af en knowledge graph, som sprogmodellen kan sp\u00f8rge. Driftsystemet skal alts\u00e5 v\u00e6re f\u00f8dt med et \u201dAPI\u201d til AI-agenter.<\/p>\n

I mine 17 \u00e5r i SAP har jeg aldrig oplevet en s\u00e5 massiv og pludselig interesse p\u00e5 et specifikt tema. \u00a0Og jeg var ellers med ved lanceringen af HANA in-memory databasen og S\/4HANA ERP-l\u00f8sningen. En stor r\u00e6kke SAP-kunder (det er jo tilf\u00e6ldigvis dem jeg er eksponeret for) eftersp\u00f8rger muligheden for at kombinere ERP-procesdata med \u00f8vrige datakilder – is\u00e6r n\u00e5r de planl\u00e6gger at bruge AI og ML.<\/p>\n

Interessen g\u00e5r is\u00e6r p\u00e5 at f\u00e5 adgang til data uden at skulle kopiere og genskabe forretningskonteksten. Kalenderen er fuld de n\u00e6ste to m\u00e5neder, og p\u00e5 vandr\u00f8rene h\u00f8rer jeg det samme i andre dele af verdenen.<\/p>\n

Fordelene ved denne \u201dnul-kopiering\u201d-strategi for analytisk indsigt er hastighed og anvendelighed. Det analytiske output vil meget hurtigere kunne skabe v\u00e6rdi, n\u00e5r det f\u00f8des direkte via komplette data og t\u00e6t integreret i kernesystemerne.<\/p>\n

Og bare rolig: Der er ingen data scientists der bliver br\u00f8dl\u00f8se i de n\u00e6ste \u00e5rtier!<\/p>\n

Af Thomas Madsen, Enterprise Architect og Technology Explorer hos SAP Danmark\u00a0<\/em><\/strong><\/p>\n

\"\"<\/p>\n

Oprindelig publiceret i Computerworld den 7. april 2025.<\/em><\/p>\n","protected":false},"excerpt":{"rendered":"

Mens data-revolutionen forandrer drift-orienterede it-systemer ganske radikalt, st\u00e5r ledelsesinformationen stadig lidt i stampe. 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