Research processes, production cycles and scientific projects are all contributing to huge quantities of data. The data produced is diverse in terms of formats, sequences, tool dependencies and such factors lead to complex issues in handling data. Systematic organization and retrieval of data is a challenging task as the process involves annotations, descriptions and methods of semantic indexing. Data by itself maybe a string of characters, signals and symbols and may not in its form be amenable to semantic driven applications for organization and retrieval. There are also several other issues that arise to achieve data driven semantics in services which is the goal in exploiting scientific data.
Big data and Open data are all evolving concepts, though data itself has always been both the source of scientific investigations and outcome of such investigations. Evolving concepts around data are numerous and are related to its organizations, tools and techniques, novel applications, reuse and interoperability.
As resolved during the WSIS review of 2016, access to scientific data will have to examined differently as the process to achieve at least ten sustainable development goals will depend on continuous scientific data feed. The focus essentially has to be broadened to include how data is used for analysis, forecasting and projections and for other such crucial functions. The value of data itself has come to be regarded as a factor of its utility and hence value of a dataset is not a constant. The same dataset can be exploited for many different purposes bearing different outcomes. Data that is free to be used and reused and repackaged has much more potential to yield more data and paves the way for interesting viewpoints, applications and interpretations. Although this could be interpreted as one of the strong arguments why data should be released as open data, several issues have to be considered; viz:

• How to build capacities at various levels to understand the context and issues linked to big data and analytics;
• How to address the challenge of data packaging and opening large data sets without compromising quality;
• How to address the challenging tasks of anonymizing data to protect privacy and yet making it available for decision making; and
• How to address the culture of sharing data and aim at a policy framework to institutionalize good practices and processes?

The session will be a moderated panel discussion where panellists will present their views on

• The context of data and analytics within the broader framework of SDGs
• Challenges of data packaging and opening large data sets without compromising quality.
• Challenges for anonymizing data to protect privacy for decision making.
• Culture of sharing data and aim at a policy framework to institutionalize good practices and processes

Some successful projects include – data revolution spearheaded by UNSG and recognition of the role of data and analytics for SDGs. Impact of Human Genome project and Hedron Collider Data released by CERN.