Big Data Will Save Us, Right?
Not quite. The Challenge of Fulfilling the Promise of Big Data for Social and Environmental Change
Look at us go. We know more about what is happening in our world than ever. With advances in cognitive computing, AI and machine learning; the sophistication of data processing methods; and emerging technologies like cloud computing, space technology and virtual reality, we have access to data about the biggest challenges and risks facing our world today (1). Climate data about wind, sea levels and temperatures around the world; social data about poverty, income and gender inequality, access to education, and human rights performance of countries (2).
And data availability will only grow. According to IBM, 90 percent of existing data in 2012 was created in the preceding two years. About 2.5 quintillion bytes of data were created every day in 2014. And in 2020 the entire digital universe is expected to reach 44 zettabytes (3). The big data analytics market is set to reach $103 billion USD by 2023 (4). Want more data statistics? See the full size version here:
Drowning in data yet? Probably. And that is exactly the problem. Well one of them.
While the promise of big data and data analytics platforms is that it will enable a more rigorous and evidence-based approach to solving some of the world’s greatest challenges, their specific impact remains assumed rather than fully understood (1). More and better data in itself will not automatically lead to social and environmental change, unless they are used to inform governmental policies and business strategies.
Yet, organisations and government alike struggle to extract value from big data. In addition to the high cost of accessing data sources and platforms, internally there is often a limited understanding of the kind of data available and limited understanding of how to assess the quality and integrity of the data on offer. And even if access to high quality data is secured, there is the challenge of creating meaningful insights from the data and translating these into government policies and corporate (CSR) strategies that effectively address social and environmental challenges. Decision-makers simply haven’t used these kind of data before.
Some social or environmental problems may be readily solved using big data, like weather data that predict the next hurricane, but what if we want to use data to address critical social problems, such as homelessness, or complex environmental issues (2)? Social and environmental challenges are often dynamic and complex in nature with multiple elements involved and numerous feedback loops among interrelated components. These kind of problems may require integrating and interpreting heterogeneous datasets, and how to do that economically and with data integrity (2)?
And how to use big data to assess the effectiveness of company or government initiatives? For instance, which available data could be used to report on company or regional performance against the UN Sustainable Development Goals? Which data sets would be most appropriate to create impact indicators and measure the social or environmental impact of certain policies?
On top of that, 95% of businesses cite the need to manage (unstructured) data as a problem for their business (4). This relates to challenges like integrating the ever-expanding volume of data with existing company data sources, or to responsible data storage and governance — while also maintaining data accessibility.
So what needs to happen for big data to fulfil its potential to help solve the world’s toughest problems? Here are some starting points:
- Build internal data competency
Obviously. Build competences around data organisation, security, preservation, visualisation, search and retrieval and use within your organisation by upskilling or attracting new talent (2). Increasing the capacity of scientists and analysts within business and government to think about what is possible with big data is critical. Equally, the understanding of networked relationships among datasets or how to uncover patterns in datasets, are competencies that need to be developed (2). If your organisation does not have the resources to do this internally, bring in specialists or consultants to advise or educate your organisation (5).
- Do away with thinking in silos
Let’s be honest. Do your C-suite, policy makers and strategists understand or even speak to your data scientists, and vice versa? Too often they speak too different a language to make full and effective use of the data available in decision-making. Bridging the divide between data scientists/analysts and strategists/policy makers means creating platforms for interaction. It means finding ways of communicating through avenues like storytelling or visualisation that build mutual understanding. It could even mean making changes to the organisational structure that puts those with data expertise on more equal footing to decision-makers.
- Share data and collaborate
When it comes to tackling social and environmental problems, drop the competitive thinking and collaborate. Numerous government agencies, non profits and businesses focus on the same challenges, with very limited cooperation and data sharing among them (2). Social and environmental problems are often complex and may require various data sets from different providers — and these data can be expensive. So unite around a common challenge or goal, and create avenues to communicate insights and share data. For corporates, sharing data sets with non profits or government can also be an intentional part of your philanthropic or CSR efforts.
- Closing the loop between data providers and data users
The need for collaboration also applies to the relationship between those who create or collate the data (academics, scientists and data platforms), and decision-makers who use the data to create positive impact. This is about creating feedback loops about usability of data, data formats, but also the actual effectiveness of the data in creating measurable impact. Did the data provided actually help create meaningful change for stakeholders? Recent studies point out the need for academics and scientists to involve the perception of industry practitioners in order to create valuable insights (6). This goes both ways, however, where decision-makers should also reach out to data providers to proactively share their experiences with the data.
- Don’t discount the human element
There is no doubt that big data advances and applications will increasingly foster the automation of organisational decisions and tasks, and in turn influence what the future workplace will look like (6). Nevertheless, recent studies into the impact of big data analytics on business operations point out that human intuition, trust and experience will remain key competences that cannot be simulated in the near future (6). The human decision maker will remain at the center of any type of big data information processing and decision making cycle (1). The successful application of big data in impact-focused decision-making requires essential prerequisites that are not only of a technological but also of a human and social nature (6).
In conclusion, the promise of big data to enable an evidence-based approach for business and government to identify risks and address the world’s greatest challenges is undeniable. But big data alone will not solve our environmental and social problems. Public and private sector organisations need to step up and look for innovative and collaborative ways to use open data and big data in their impact-focused (CSR) strategies and policies.
“Progress is being made, but the chasm must still be crossed. It is a challenge worth overcoming” — Desouza & Smith (2014)
References:
- Big Data and Their Social Impact: Preliminary Study — M. D. Lytras & A. Visvizi (2019): https://www.researchgate.net/publication/335869149_Big_Data_and_Their_Social_Impact_Preliminary_Study
- Big Data for Social Innovation — K. C. Desouza & K. L. Smith (2014): https://ssir.org/articles/entry/big_data_for_social_innovation
- How Much Data is Generated Each Day — World Economic Forum (2019): https://www.weforum.org/agenda/2019/04/how-much-data-is-generated-each-day-cf4bddf29f/
- 25+Impressive Big Data Statistics for 2020 — C. Petrov (2020): https://techjury.net/blog/big-data-statistics/#gref
- 15 Big Data Problems You Need to Solve — SolveXia (2019): https://www.solvexia.com/blog/15-big-data-problems-you-need-to-solve
- The Future and Social impact of Big Data Analytics in Supply Chain Management: Results from a Delphi study — B. Roßmann, A. Canzaniello, H. von der Gracht & E. Hartmann (2018): https://www.sciencedirect.com/science/article/abs/pii/S004016251731329X
