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Результаты процесса демократизации BI-решений

13 июня 2013 Как и подавляющее большинство технологий, процесс демократизации решений бизнес-аналитики набирает оборотов. Так, несколько лет назад работа с BI-платформами была посильна исключительно для опытных ИТ-специалистов. Сегодня же самыми распространенными пользователями данных решений являются сотрудники, наделенные правом принимать решения в организации, и зачастую не имеющих навыков работы с ИТ-инфраструктурами. В результате разработчики усовершенствовали свои продукты, делая инструменты более гибкими и интуитивными в применении. (Материал опубликован на английском языке)
But culturally, many organizations still see BI as a dedicated role, not a way of working.  When I refer to “embedded analytics,” I don’t mean just the literal incorporation of analytical capability into existing software or operational processes.  I’m talking about making insight-driven decisions a key part of your business culture and day-to-day activities.

When people say “I don’t see how analytics are relevant to me,” I often ask them if they ever watch football or place a bet on a sporting outcome. Both commentators and bookmakers rely heavily on statistical trends and patterns around the match in-play and previous form to provide a compelling narrative or determine the odds. Without discrediting the pundits, that information isn’t summoned readily off the top of their heads!  Or think about the last time you went to the supermarket and received a voucher off your next grocery shop.  That offer was identified as relevant based on your previous purchases.

Fundamentally, analytical insight touches and enriches all our lives on a regular basis. Once we stop regarding business intelligence as a function in its own right, we can reframe it as a heads-up on any business scenario: whether preventing operational issues from turning into problems, or helping us to understand customers so intimately that we not only meet their expectations but also surprise and delight them.  The trick is to not scrutinize every scrap of data for “hidden meaning” – rather, start with a list of pressing business questions you need to answer, and apply analytics to help you respond with confidence.

Source:  sap.com