Publication database

With permission of Emerald

With permission of Emerald

Na tentativa de extrair informação da imensidão de dados hoje colhidos de fontes internas e externas, muitas empresas estão investindo pesado em ferramentas de TI e contratando cientistas de dados. A maioria, contudo, pena para conseguir um retorno digno do esforço. Isso porque estão abordando projetos de “big data” e analítica da mesma forma que abordam qualquer outro projeto de TI, sem perceber que são dois bichos completamente distintos.

Customer decision-making uncertainty (DMU) is a persistent phenomenon in business-to-business markets. However, there is substantial variation in the degree customers perceive DMU and hence suppliers should react to it. Based on existing industrial buying typologies, this paper proposes a new classification scheme to explain variance in customer DMU. To this end, market offering complexity and co-creation are used as defining dimensions and four ideal types of industrial market offerings are constructed. We show theoretically that DMU is especially prevalent for complex solutions. The paper closes with guidance for suppliers of industrial market offerings and an outlook for future research.

With permission of Emerald

Argues that, despite their large investments in data scientists, and IT tools, many companies are struggling to capitalize on the massive amounts of data now available to them. Suggests that most managers are employing the conventional approach to designing and installing IT systems and this approach may not be appropriate for exploiting the tremendous amounts of data now available from internal and external sources. Notes that a big data or analytics project is likely to be smaller and shorter than a conventional IT initiative, such as installing an enterprise resource planning (ERP) system. Identifies five guidelines for taking an alternative approach to IT systems development: place people who will create meaning from the information at the heart of the initiative; unlock value from IT by asking second-order questions and giving teams the freedom to reframe business problems; equip teams with cognitive and behavioural scientists, who understand how people perceive problems and analyse data.; focus on learning by facilitating information sharing, examining assumptions, and striving to understand cause and effect; and worry more about solving business problems than about deploying technology.