Due to the enormous amount of Big Data available on a daily basis, business models are now employing the use of data and analytics for marketing and providing their services.

 

How John Snow Labs Can Help

Big Data can create value in the social sector in five ways. This is where John Snow Labs comes in:

  • An increase in the speed and accuracy with which social interventions can be deployed.
  • Efficient monitoring and evaluation of programs and policies as well as better identification of improvements to programs and policies.
  • More targeted and tailored interventions and initiatives to give room for maximum impact.
  • Improved decision making through a faster and more accurate assessment of risks.
  • Creation of new products, services and business models in serving disadvantaged groups in the population.

 

About 2.5 quintillion bytes of data are generated on a daily basis. 90 percent of the data we have in the world today have been produced in the last two years. Each time an update is posted on the social media (Facebook, Twitter or Instagram), the Big Data revolution expands. The same thing occurs whenever a purchase is made with credit cards or the GPS feature on a phone is used.

Due to the ground that Big Data is gaining in the business scene, companies as well as scientists now rely on data for carrying out their market research and providing personalized services to their clients. For instance, we have over 6 billion uses of mobile phones worldwide. 5 billion out of this figure are found in developing countries. Services such as m-Pesa (found in Kenya) allows people to carry out financial transaction on their mobile phones.

In 2011, the local transaction of m-Pesa in Kenya exceeded the transaction carried out through Western Union throughout the world. It will also amaze you to know that Indonesians in Jakarta tweet more than any resident in other cities of the world. These global use of mobile phones, social media as well as internet searches provide huge data which when analyzed, reveal specific and new insights about the trends of human behavior.

Non-profit organizations can benefit from the Big Data revolution in two ways. They can utilize Big Data to come up with new programs and initiatives and to target donors and recipients of aid. This importance is useful in offering reliefs during the occurrence of disasters. For instance, mobile phone position data from Digicel (the largest mobile phone company in Haiti) was used to calculate the magnitude as well as the trends of population movement after the Haiti earthquake that occurred in 2010 and the cholera outbreak that later occurred. The data generated improved the allocation of relief supplies to the victims of the incident. It also helped in assessing the needs after the natural disaster and during the cholera outbreak.

Non-profit organizations can also make use of Big Data in making a difference in what they are doing. They can help them to demonstrate that their programs and initiatives are achieving their objectives.

Big Data provides an evidence base that can help such organizations to enjoy funding from donors. It provides a straightforward correlation between programs or initiatives and improved outcomes.

Non-profit organizations need to be equipped with the skills required to define the problem they are trying to solve or the objectives they intend to achieve before embracing data analysis. They need to monitor and evaluate data before the implementation of a program or project. This monitoring and evaluation can be done by collecting baseline data and tracking changes before and after implementation.

The risk associated with Big Data revolution is the exponential growth associated with data creation when compared with the ability to analyze the data. The volume of data available may be too much for the non-profit organizations to handle. Most non-profit organizations do not have what it takes to store, process and obtain valuable results from large datasets.

When it comes to giving non-profit organizations room to benefit from Big Data, we need to realize that more does not mean better while volume does not lead to veracity. The key task that is required is to pinpoint ways to ensure that the currently available data are effectively utilized.