Knowledge Base

In 2001, the industry analyst Douglas Laney at Gartner described data management challenges along the three dimensions volumes, velocity and variety in the E-commerce branch. Volumes stands for the quite huge increase of volumes of data, Velocity for increased point-of-interaction speed and the pace of data generated by interactions and used to support interactions. Data Variety means variety of incompatible data formats, non-aligned data structures and inconsistent data semantics. This 3-V-model has been widely used attempting to define big data since this publication in 2001. [1]

Predictive analytics brings together advanced analytics capabilities. It extracts information from existing data sets in order to determine patterns and predict future impacts and trends. It forecasts what might happen in the future with an acceptable level of reliability, and includes what-if scenarios and risk assessments.[1]
Data analytics encompasses techniques such as regression analysis, pattern matching, forecasting, multivariate statistics, predictive modelling and forecasting. [2]

Cloud Computing is a model that enables ubiquitous access to a shared pool of configurable technological assets available on-demand in a virtualised environment. Cloud services are remotely managed by cloud service providers and can be rapidly provisioned and released with minimal effort or service provider interaction. It can potentially achieve coherence and economies of scale.
The cloud model encompasses the four deployment models Public, Private, Hybrid and Community and the following three delivery models [1]:
Software as a Service

Key Performance Indicators (KPIs) are an integral component of public administrations performance measurement systems. In general, KPIs are assessment criteria that refer to the assessment dimensions Input, Process, Output, Impact and Outcome. [1]

Privacy by design is an approach that promotes privacy and data protection compliance throughout the whole system engineering process. The Information & Privacy Commissioner of Ontario has taken a leading role in developing the privacy by design concept, establishing a reference framework of “Seven foundational principles of privacy by design” with respect to a proactive, transparent and user-centric engineering process. [1][2]
The 7 principles are:
•    Proactive not Reactive; Preventative not Remedial
•    Privacy as the Default setting

Security by design is an approach in software engineering that promotes to design software from the ground up to be secure.
Core pillars of information security are confidentiality (only allow access to data for which the user is permitted), integrity (ensure data is not tampered or altered by unauthorized users) and availability (ensure systems and data are available to authorized users when they need it). [1]

The Data Governance Institute defines Data Governance as a system of decision rights and accountabilities for information-related processes, executed according to agreed-upon models, which describe who can take what actions with what information, and when, under what circumstances, using what methods. It encompasses strategies and technologies used to make sure organisations data stays in compliance with regulatory and legal requirements.[1]

Governance is a term that has a quite broad context and means in its core to shape or design areas of life. E-Governance (Electronic Governance) is devoted to the challenges of shaping life areas in face of digital revolution and information era and affects all sectors encompassing the public, private and civil sector. It also affects communication and exchange between the sectors.

Glocalization is an artificial word combining Globalization and Localization. It is a practice of conducting business according to both local and global considerations. The process allows integration of local markets into world markets in a business context. [1]
The concept can be transferred into a public sector context in the way that local levels of government need to be considered in higher-level policies.

Data philanthropy is a kind of strategic partnership between private and public sector in which private companies donate data as a valuable resource for public benefit, i.e. for humanitarian, corporate, human rights, and academic use. [1]
However, there are numerous challenges to be mastered, such as competing tensions on data control and ownership, personal data protection and the lack of adequate frameworks for coordination and governance. [2]