The 6 challenges of Big Data for Social Good

Figure 1: The six challenges of Big Data for Social Good

A data-driven approach can be taken for each and every one of the Sustainable Development Goals, using data to measure how the public and private sector are progressing, as well as helping policy makers to shape their decisions and have the greatest social impact possible.  As we can see below, there are many different use cases that can be considered by organizations:  

However, many of the examples above refer to one-off projects and pilots and the real acceleration of towards these SDG’s will come from running these projects on a continuous basis with (near) real-time data-feeds to ensure stability and continuity for the next generation of social Data Scientists.

So what are the biggest challenges for companies and organizations who want to contribute their data for the greater good? Is it risky that the data has to leave the company’s premises for analysis by other organizations?  We’ve outlined the challenges decision makers are currently facing when it comes to Big Data for Social Good:

1: Privacy & Security

Data needs to be anonymized and aggregated. But will the anonymization process be good enough? Is it impossible to re-identify customers or users? Once the data is somewhere else, how secure is it? If it becomes a constant data feed, how safe is it?

2: Legal

For many companies, most of the relevant data is customer data. And although it is likely to be anonymized, aggregated and extrapolated, there is no full consensus on whether this is allowed or not.  Organizations also have to face the challenges of there being a wide range of different Data Protection legislation in the different countries across their footprints.

3. Corporate reputation

Even if things are completely legal, professionals may still worry about public opinion and how customers may see things differently. What happens after a data breach, even if the use of data had a social purpose?

4. Big Data is the Key Asset

Businesses also have strategic commercial issues that they may struggle with.  Many companies have only just learned that Big Data is a key asset, so may think why should they share this with someone else, even if for the greater good?

5. Competition. 

Could the competition get hold of my data (asset) and make inappropriate use of it? How would I explain that one in the boardroom? The competition is tough and sending data to an external platform has most CSO’s concerned.

6. Cannibalization.

Does this use of data for social good cannibalize some of my external Big Data revenue? What if I jeopardize an existing business opportunity in order to carry out a Big Data for Social Good project? 

However, there is an existing solution which addresses the first three challenges.  The OPAL Project ( which stands for Open Algorithms) doesn’t require companies to move their data off their premises; it stays where it is. Using OPAL, the algorithms are transferred to the data and are certified (against virus and malware) and produce the insights they are designed for (ensuring quality). Albeit, simple, this is an extremely powerful technology and as it is an Open Source project, all software developed will be freely available. The algorithms will be developed by the community and certified by OPAL.

OPAL is still in early stage and low profile, but we firmly believe that
it will encourager a wider range of companies to contribute
to the Sustainable Development Goals. And while OPAL is an interesting solution for the privacy, legal and reputation concerns, it doesn’t yet solve the strategic and business concerns mentioned above.

Until now, there is a general consensus that Big Data for Social Good should be free of charge, meaning that Social Good implies Data Philanthropy: a form of collaboration in which private sector companies share data for public benefit. However, Big Data for Social Good projects do not have to be necessarily free of charge. While data philanthropy is very important to start the social good movement, in the long run we expect progress to be much quicker if there are also commercial opportunities. Companies are simply more willing to invest in something with a business model.

At the moment there are several examples of Big Data for Social Good not being free:

While the discussion about data for SDGs and Data Philanthropy is far from over, some visionaries predict that any future commercial, business opportunity will have a strong social component. A great read on this is “Breakthrough Business Models Exponentially more social, lean, integrated and circular” which was recently commissioned by the Business and Sustainable Development Commission. 

Will Big Data cause the next revolution in social impact? We believe it can and we’re 100% behind it.