The exploitation perspective – The benefits of adopting the LinDA toolset as a development accelerator

Given the availability of huge amount of information in heterogeneous public and private data sources worldwide, the realization of advanced analysis over the available data is considered crucial for Small and Medium Enterprises (SMEs) in order to properly exploit the available information and turn it into competitive advantage.

Data analytics have the potential to help SMEs to identify the data that is most important to the current and future business decisions, provide insights based on the analysis, answer specific business questions and facilitate/guide the decision making process. The combination of publicly available data (e.g. governmental open data, environmental data) with privately transformed data, maintained by SMEs, can help enhancing their experience of managing and processing of data, in ways not available before.

It should be noted that many major companies are already moving towards the transition from business intelligence to business analytics approaches, since they consider analytics as the scientific process of transforming data into insight for making better decisions. Towards this direction, the engagement of data scientists in addition to information scientists in the data analysis process is considered a must.

Given the need for interlinking of concepts described in different datasets towards the preparation of meaningful datasets for analysis, the modern approach adopted is Linked Data, a set of best practices for representing and connecting structured information on the web. Following these practices enables the creation of a web of data – a large interconnected web consisting of integrated data elements. Within the Linked Data domain, the LinDA (Linked Data Analytics) project is going to provide a set of tools that will assist SMEs in efficiently developing novel data analytical services that are linked to the available public data, therefore contributing to improve their competitiveness and stimulating the emergence of innovative business models. The proposed approach is building upon the collection of data from available data sources, their transformation in proper format (e.g. RDF format) and their interlinking for the creation of extended linked datasets, fed as input in the analytics extraction process. Then, the analysis part can be realised, while the output can be fed up to visualisation tools.

LinDA is handling several technical challenges regarding the creation, publication and consumption of Linked Data. However, in addition to the technical challenges, a business-oriented challenge regards the learning curve for the adoption of the provided solutions from SMEs. The adoption process can be quite steep, because few educational resources actually exist for users new to the concepts, and very fewer resources can be found that discuss how to apply these technologies in real world scenarios. The approach followed within LinDA concerns the design and development of an ecosystem consisting of well interconnected tools, a set of consumption applications targeted to the provision of specific functionalities to end users per domain, along with the preparation of a set of guidelines for the use of each of the considered tools.

It could be argued that the learning curve for SME’s with a basic background of knowledge organization, web services and analytic tools will be minimal, given that the deployed ecosystem will hide complex details from the end-user. Furthermore, concerning the algorithmic part of the tools and the detailed knowledge of the algorithms execution process and configuration parameters, a simplified version of specific algorithms will be supported with predefined configuration setup. Advanced configuration will be also supported, however, this will possibly require the engagement of data scientists in the process with expertise in the data analytics domain.

Summarizing, it could be claimed that the adoption of the LinDA technologies from SMEs can provide them the potential to produce advanced knowledge, leveraging the power of linked data analytics and, thus, acquire a significant competitive advantage in the decision making process and increase their overall effectiveness.