Research Data Infrastructures

Presentation on national resaearch data infrastructures in Finland. Visit of National Science and Technology Library, PRC

Evaluating the Maturity of Data Management Practices in a Major Energy Provider Company

Data is the most important asset of a company in the information age. Other assets, such as technology, facilities or products can be copied or reverse-engineered, employees can be brought over, but data remains unique to every company. As data management topics are slowly moving from unknown unknowns to known unknowns, tools to evaluate and manage data properly are developed and refined. Many projects are in progress today to develop various maturity models for evaluating information and data management practices. These maturity models come in many shapes and sizes: from short and concise ones meant for a quick assessment, to complex ones that call for an expert assessment by experienced consultants. In this paper several of them, made not only by external inter-organizational groups and authors, but also developed internally at a Major Energy Provider Company (MEPC) are juxtaposed and thoroughly analyzed. Apart from analyzing the available maturity models related to Data Management, this paper also selects the one with the most merit and describes and analyzes using it to perform a maturity assessment in MEPC. The utility of maturity models is two-fold: descriptive and prescriptive. Besides recording the current state of Data Management practices maturity by performing the assessments, this maturity model is also used to chart the way forward. Thus, after the current situation is presented, analysis and recommendations on how to improve it based on the definitions of higher levels of maturity are given. Generally, the main trend observed was the widening of the Data Management field to include more business and “soft” areas (as opposed to technical ones) and the change of focus towards business value of data, while assuming that the underlying IT systems for managing data are “ideal”, that is, left to the purely technical disciplines to design and maintain. This trend is not only present in Data Management but in other technological areas as well, where more and more attention is given to innovative use of technology, while acknowledging that the strategic importance of IT as such is diminishing.

Rising to the impossible challenge: the storage of all research data, forever?

Presentation at Open Repositories 2014, Helsinki, Finland, June 9-13, 2014

Visual Arts Data Skills for Researchers (VADS4R)

Workshop at Open Repositories 2014, Helsinki, Finland, June 9-13, 2014

Open Access Research Data Repository for Corpus Linguistic Data – A Modular Approach

Presentation at Open Repositories 2014, Helsinki, Finland, June 9-13, 2014

‘Just how (re)usable is Research Data? A legal perspective’ - A poster summarizing the recommendations of the OpenAIRE legal and licensing study.

Poster at Open Repositories 2014, Helsinki, Finland, June 9-13, 2014

Building a Research Data Registry for the UK

Presentation at Open Repositories 2014, Helsinki, Finland, June 9-13, 2014

Issues with Fedora & Hydra, experiences from a research-data-driven implementation

Presentation at Open Repositories 2014, Helsinki, Finland, June 9-13, 2014

DSpace for Data Revisited

Presentation at Open Repositories 2014, Helsinki, Finland, June 9-13, 2014

B2SHARE – Storing and Sharing Research Data

Poster at Open Repositories 2014, Helsinki, Finland, June 9-13, 2014

B2SHARE – Storing and Sharing Research Data

Presentation at Open Repositories 2014, Helsinki, Finland, June 9-13, 2014

A rose is a rose is a rose? Defining significant properties of social science research data

Presentation at Open Repositories 2014, Helsinki, Finland, June 9-13, 2014

Integration of marketing research data in new product development. Case study: Food industry company

The aim of this master’s thesis is to provide a real life example of how marketing research data is used by different functions in the NPD process. In order to achieve this goal, a case study in a company was implemented where gathering, analysis, distribution and synthesis of marketing research data in NPD were studied. The main research question was formulated as follows: How is marketing research data integrated and used by different company functions in the NPD process? The theory part of the master’s thesis was focused on the discussion of the marketing function role in NPD, use of marketing research particularly in the food industry, as well as issues related to the marketing/R&D interface during the NPD process. The empirical part of the master’s thesis was based on qualitative explanatory case study research. Individual in-depth interviews with company representatives, company documents and online research were used for data collection and analyzed through triangulation method. The empirical findings advocate that the most important marketing data sources at the concept generation stage of NPD are: global trends monitoring, retailing audit and consumers insights. These data sets are crucial for establishing the potential of the product on the market and defining the desired features for the new product to be developed. The findings also suggest the example of successful crossfunctional communication during the NPD process with formal and informal communication patterns. General managerial recommendations are given on the integration in NPD of a strategy, process, continuous improvement, and motivated cross-functional product development teams.

Master data quality management in a global enterprise

In a networked business environment the visibility requirements towards the supply operations and customer interface has become tighter. In order to meet those requirements the master data of case company is seen as an enabler. However the current state of master data and its quality are not seen good enough to meet those requirements. In this thesis the target of research was to develop a process for managing master data quality as a continuous process and find solutions to cleanse the current customer and supplier data to meet the quality requirements defined in that process. Based on the theory of Master Data Management and data cleansing, small amount of master data was analyzed and cleansed using one commercial data cleansing solution available on the market. This was conducted in cooperation with the vendor as a proof of concept. In the proof of concept the cleansing solution’s applicability to improve the quality of current master data was proved. Based on those findings and the theory of data management the recommendations and proposals for improving the quality of data were given. In the results was also discovered that the biggest reasons for poor data quality is the lack of data governance in the company, and the current master data solutions and its restrictions.

Practical Data Curation and Management for Institutional Repositories

Workshop at Open Repositories 2014, Helsinki, Finland, June 9-13, 2014