Environmental management has climbed a steep learning curve, with smokestack industries benefiting from new environmentally conscious strategies. I argued in my last post that the same goes for big data. Let’s examine how forward-thinking industrial management practices can benefit big data projects and technologies.
The evolution of environmental management
In the early days of environmental compliance, companies placed their environmental managers at the end of the planning process. The design and production departments would decide what they wanted to make and how they would produce it. Then, after completing much of their planning work, they would consult the environmental manager to find out what they needed to do to comply with environmental laws.
All too often, the environmental manager ended up telling the business teams what they could not do and sent them back to the drawing board. This strengthened compliance by the book but hurt production and stifled innovation in environmental protection itself. Moreover, solutions often took the form of end-of-pipe pollution control technologies bolted on at the final stage of the production process.
A shift to Total Quality Management and lean manufacturing
But then the broader management of industrial production shifted towards improving quality by optimizing the whole manufacturing system, rather than by fixing defects at the end of the line.
Rather than capturing a pollution defect at the end of the production process, as most environmental compliance efforts did, the system could be optimized to minimize pollution in the first place. The result of this broader development approach was the wide-spread use of environmental management systems.
Now, working with the design and production teams, the environmental manager can become a collaborator and an innovator, not an internal cop. Instead of a case-by-case approach, collaborative teams look at optimizing the entire system to prevent pollution. Frequently, these front-end pollution-prevention solutions end up saving organizations money compared to end-of-pipe controls.
Big data management systems
Viewed from the perspective of environmental management, a Consumer Subject Review Board management approach, and the Institutional Review Board approach on which it is based, looks a lot like the early, back-end industrial approach.
An element management system–like model, such as a big data management system approach, would have business owners, privacy professionals, data scientists, and programmers collaborating to be aware of potential privacy and discriminatory impacts as they extract valuable insights from diverse data sets to test and develop their algorithms.
A big data management system would have the person responsible for mitigating privacy and discriminatory impacts present at the front end of the process as part of the agile team working on any given big data project.
Preventing privacy and discriminatory impacts
This manager would ensure that product design, engineering, and operations teams are seeing both the benefits and the potential privacy and discrimination issues as they design and implement algorithms and applications. This would reduce the need for late-stage evaluation of the product since societal implications—both beneficial and potentially harmful—would be considered throughout.
Just as environmental management systems help prevent pollution, so big data management systems should help prevent privacy and discriminatory impacts. Just as pollution prevention is less costly than end-of-pipe pollution controls, so prevention of privacy and discriminatory impacts at the front end should be less expensive and more streamlined than a cumbersome review process at the back end.
If you want to explore this topic in more depth, please sign up below and get access to my paper: Big Data Sustainability: An Environmental Management Systems Analogy.
Article written with Prof. Dennis Hirsch.