Typically when one associates the law with analytics, the discussion focuses on how an organization uses analytics to assess the economic value of damages or claims.
Specifically, in the field of litigation analytics, the primary concern is the statistical analysis of legal issues such as torts, employment law, worker’s compensation, and insurance. Litigation analytics also covers stocks, bonds, derivatives, and mortgage-backed security valuations.
However, here I'm focusing on a broader topic -- how the law circumscribes the development and deployment of analytic work products.
For example:
Did you obtain the data inputs for your analytic work with or without the consent of survey respondents? Do the data inputs contain personally-identifiable information? Does your organization collect "competitive intelligence" to increase revenue or profits? Does your big-data include clickstreams from sources outside of the US? Does your contract allow you to sell your databases to other organizations? Do any local, state, or federal laws regulate your data collection or information-sharing activities?
While the legal dimensions of analytics should be your boss's concern and not your own, you should be aware that you cannot do your job without the approval of some legal/regulatory authority or agent. Whether it is a human subjects review board, a federal agency, or your company’s law department, every organization has a gatekeeper that ensures it conducts business activities in compliance with the governing laws.
To the extent that your work products could come under review for legal compliance (such as a system audit) or as part of a criminal investigation, here are some best-practices to follow:
Document your data sources. Whether as part of a program header, system specifications document, or user manual, you should record all known information about the origin and characteristics of the data.
Document any transformations made to the data. You should record all changes to the organization of the records, variables, or data values.
Document the analysis plan. You should record how you were instructed or chose to analyze the data.
One way of simplifying your "audit readiness" is to use tools that capture metadata. This method requires a lot of upfront work in terms of deriving operational definitions and entering a lot of detailed information, but it will pay off if you ever need to produce the information for an audit.
In like manner, tools that graph the data processing and analysis plan can provide auditors easily interpreted visuals of the dataflows. You still may need to produce source code, but having the associated visual aids will reduce the probability of such a request.
In short, someone inside and perhaps also outside your organization has authorized your work as an analyst. To that extent, everything you do is subject to review, especially if your data inputs are sensitive or analytical tasks have far-reaching implications. You should approach your work in a manner that makes every data source and analytic task traceable and trustworthy: Even in analytics, you cannot escape the long arm of the law. Hence, you should be proactive and conduct your work in a manner that would stand up to an unexpected regulatory review.
Have you ever taken the time to examine your analytic work in a legal context? Will you now? Comment on the message board below.
I agree with you that this part of keeping the knowledge to oneself is creating a whole mindset of egotistical behavior that is not helping society at all. Sharing is the only way we have been able to get ahead as a human race.
Excellent observation! The legally binding (and sometimes confusing) language used to be presented in a manner to minimize visibility (SPACE), but now is it visible but presented in a manner that minimizes the opportunity for serious reflection (TIME). As long as one party has put forth the terms and conditions in some manner (whether it minimizes space to avoid detection, minimizes time to avoid comprehension, or even uses confusing language that obfuscates the liabilities), then that party has fulfilled its legal obligations.
Check the small print; you probably agreed to allow your data records to be sold. If not, then there may be local and interstate commerce laws that allow your data records to be shared.
It used to be that you saw the "find print" in occasional legal contracts, like rental or lease agreements, other agreements, etc. I always made sure to read over them.
Now the "fine print" is EVERYWHERE. They like to catch ya in the checkout line, when you're suddenly offered a loyalty card to get a 10% discount, or online when you're clicking among pages in a site and suddenly need to register or something, and you need to move on quickly.
In other words, we seem to be increasingly confronted with legally binding "fine print" in situations that would otherwise really merit more careful, detailed deliberation.
Thanks for the feedback. I hope that our community members invest the time to become familiar with these issues. We do not address the area of information governance too often. But with the advent of big data, as we cast a wider net for data, we also expand our risks and liabilities.
The intent of this thread is not to make analysts paranoid, but we should be aware that if we use data that was illegally obtained by our companies, or if someone is legally damaged (tort) by some type of predictive/analytic application, then our documentation is our only life line. So I encourage everyone to CYA - Cover Your Analytics - do the documentation as a part of your work.
This link (http://ibmdatamag.com/2012/04/big-data-governance-a-framework-to-assess-maturity/ ) provides some useful information regarding data governance. While each domain (there are 11) may not deal directly with the day to day of work of analysts, it would be very wise if you raised your awareness of data governance. For you can be sure that the moment the usage of your work products is subject to review, these 11 domains of data governance concern will also become your concern.For example, this link raises this question:
Are you prepared to handle public relations and legal fallout from the advanced predictive capabilities of your recommendation engines, especially regarding gender and age sensitivities (e.g., a retailer promoting maternity products to a teenager when her parents were unaware of her impending pregnancy)?
The take-away from this is that as we celebrate our access to big data and our innovative analytical tools, we should be proactive in documenting how we handled the data in doing our work.The list below comprises the 11 domains of data governance concern:
Thanks Bryan for your fantastic response. I had wondered where is this view of mental work versus manual work came from. I should have known the Greeks were responsible for this - very interesting.
It really is a philosophy that is very much alive today. I don't know if I agree, but I would much rather work with my mind than my hands any day ! : ) And this fear of sharing knowledge in order to promote job security is also widely practiced, I guess you can't blame the knowledge holder, since companies have proven over and over again that if you do not bring something to the table, you will probably not be around long.
Yet I agree that all the reasons used for not supplying supporting documentation of findings are just excuses, and certainly does not ensure professional security.
Thank you for sharing your insight, I really appreciate it.
I agree. The bosses can look at it from the point of view of net present value of penalties compared to the current cost of alignment. However, not all regulatory actions are penalties. Some might involve bigger consequences such as suspension of activities, publicly announced as being blacklisted by tax authorities and so on. These are the points that bosses need to hear as well before making their decision.
You are so right! Unfortunately there is another factor that feeds into this. People sometimes engage in a practice that economists call 'discounting the future'. That means we make choices as to whether the present or the future is worth more. Many bosses will take the risk of future penalties because they consider the present more valuable than the future. Hence as it relates to future penalties, they have to be convinced that the future will be more expensive than the present. Tough job and a long struggle ...
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