Building Smart Communities Using Big Data
One of the most valuable resources in today’s global village is data. Data can improve learning, help one try new practices and continuously improve their knowledge and skills. Data is a community asset to be valued and shared to enhance data-driven decision-making. The use of big data and analytics has expanded to rural areas through Business Processing Outsourcing (BPO) companies that have set up rural centers. This trend is common in India where achieving extreme cost benefits has been the underlying force driving businesses to set up BPOs in rural India.
Rural BPOs are commited to delivering business value to a wider range of clients and at the same time transform the rural ecosystem to sustain non-agricultural jobs. This contributes greatly in building smart communities within rural India where the BPOs have set up base because most of them hire employees residing in nearby villages. Such ventures can improve the ability of the community and its members to compete in today’s world since the employees get to interact with customers from across the globe.
Data science and data analytics are at the core of every modern globalized industry. Working in today’s technology-centric workforce not only requires superior leadership skills, but the ability to translate data problems into the bigger picture for the organization. Companies are today using big data for consumer profiling, personalized services and predictive analysis to optimize sales.
This technique can be applied to gather information about people’s wellbeing as well. This is very exciting but at the same time, there is the challenge of privacy and potential abuses of data. Legal frameworks in many countries have not yet caught up with the exponential potential of advanced technology. With information shooting from different corners, we are required to find the most reliable sources by validating data to enhance its credibility. Companies need to invest in data security in order to curb unauthorized access to confidential information and at the same time, prevent data corruption.
On September 25, 2015 world leaders gathered at the United Nations in New York to adopt the 17 Sustainable Development Goals (SDGs), a truly universal, and transformative global development agenda. Countries need to have high quality disaggregated data for policy making and for monitoring the progress of implementation of the 2030 Sustainable Development Goals.
The role of corporates by and large has been understood in terms of a commercial business but that narrative is changing. To achieve the Sustainable Development Goals, we require the efforts of everybody including the pulling together of resources by governments, businesses and civil society.
Data can be disaggregated by sex, age, geography, income, race, ethnicity, migratory status, disability and other characteristics relevant in national contexts of different countries. When data is disaggregated, the analysis can account for the most vulnerable and marginalized populations and enhance measurements of discrimination and inequalities both within and among contries.
Big Data for development is about turning imperfect, complex, often unstructured data into actionable information. This implies leveraging advanced computational tools (such as machine learning), which have developed in other fields, to reveal trends and correlations within and across large data sets that would otherwise remain undiscovered. Above all, it requires human expertise and perspectives to create data driven policies, businesses and cultures for a better future.