Myynnin kasvattaminen kokonaisvaltaisen asiakastiedon ja kehittyneiden analyyttisten sovellusten käytön avulla

Companies are required to understand their customers more in depth in order to answer to the challenges introduced by the growingly complex operating environment. This understanding can be acquired through customer analytics in which the available customer information is analyzed with the help of advanced analytical applications. This research studied both customer analytics and the business intelligence architecture required to make customer analytics work. The aim of this study was especially to identify the correlation between the business intelligence architecture maturity and the insightfulness of customer analytics. In addition, particularly the application areas of customer analytics producing customer insight, which can be used to increase sales or sustain current sales, were focused on. The research was conducted as a case study including five different case companies. A semi-structured interview was used as a data collection method. Additionally, case descriptions including both the current status of business intelligence architecture and customer analytics in the case companies were created based on these semi-structured interviews. Furthermore, the case descriptions were analyzed in order to evaluate the business intelligence architecture maturity, amount of different application areas of customer analytics, and the level of customer analytics’ sophistication in the case companies. The results of these analyses were then compared to each other creating understanding from the correlation between these three entities. Based on these results a conclusion was drawn that there exists a correlation especially between the use of comprehensive customer information and advanced analytical applications and the insightfulness of company’s customer analytics. Furthermore, there also exists a correlation between the insightfulness of the company’s customer analytics and its ability to use customer information to further increase sales. The main results of this study can be used as a guideline when developing business intelligence architecture and as a source of ideas for new application areas of customer analytics. /Kir1

From Data to Knowledge in Secondary Health Care Databases

The advent of big data in health care is a topic receiving increasing attention worldwide. In the UK, over the last decade, the National Health Service (NHS) programme for Information Technology has boosted big data by introducing electronic infrastructures in hospitals and GP practices across the country. This ever growing amount of data promises to expand our understanding of the services, processes and research. Potential bene�ts include reducing costs, optimisation of services, knowledge discovery, and patient-centred predictive modelling. This thesis will explore the above by studying over ten years worth of electronic data and systems in a hospital treating over 750 thousand patients a year. The hospital's information systems store routinely collected data, used primarily by health practitioners to support and improve patient care. This raw data is recorded on several di�erent systems but rarely linked or analysed. This thesis explores the secondary uses of such data by undertaking two case studies, one on prostate cancer and another on stroke. The journey from data to knowledge is made in each of the studies by traversing critical steps: data retrieval, linkage, integration, preparation, mining and analysis. Throughout, novel methods and computational techniques are introduced and the value of routinely collected data is assessed. In particular, this thesis discusses in detail the methodological aspects of developing clinical data warehouses from routine heterogeneous data and it introduces methods to model, visualise and analyse the journeys that patients take through care. This work has provided lessons in hospital IT provision, integration, visualisation and analytics of complex electronic patient records and databases and has enabled the use of raw routine data for management decision making and clinical research in both case studies