“The Data Privacy Benchmarks Have Moved From Strictly Compliance to a More Forward Leaning Policy That Considers Evolving Regulation in Different Geographies and Solutions.”
Please tell us about your role and the team / technology you handle at Dun & Bradstreet. How did you arrive at Dun & Bradstreet?
As chief data scientist, I am responsible for many aspects of creating new ways of deriving insight from data. I work across all parts of the organization. My team is focused on many aspects of data science including identity resolution, geospatial inference, drawing inference from unstructured data, veracity adjudication, and advanced anomaly detection. I have been with the organization for over 20 years. Immediately before joining this organization I was in management consulting working with very large organizations globally in the context of data and technology. I came to Dun and Bradstreet specifically to focus on opportunities around identity resolution.
Could you tell us more about your role and how it evolved during the pandemic months? What technology / applications did you leverage to boost your remote workplace?
During the pandemic, we were already dealing with pre-existing disruption from changes in governments around the world, trade agreements, supply chain challenges, and other reasonably well understood disruption. With the pandemic, we had a very large global event which did not happen at the same time everywhere. In many cases, the environments were changing faster than the data about those environments, which is a particularly pernicious problem in data science. We worked very hard to challenge bias in the data that was available and to understand what methods are appropriate in such a hyper disrupted context.
In many respects, we were already working in a very geographically dispersed posture, so collaborating in the context of working remotely was less of a challenge than it may have been for others. That said, there are times where one needs to just go to the whiteboard and fill it with equations and such. The technology that is available for collaboration in this regard is interesting, but sometimes distracting. We did have to invent some new technological solutions for sharing interim work product as well as protecting information differently in a Federated model.
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Dun & Bradstreet is one of the oldest tech companies in the US. Tell us how you envision the technology space evolving through convergence of AI, ML and Data Science?
Artificial intelligence is nothing new. What is new is the corpora of data and the amount of compute power which is now coalescing. It is a very exciting time and also a time which causes us to think very seriously about ethical use of data, completeness of data, adversarial manipulation in some regards, data rights, trustworthy artificial intelligence, and many other emerging challenges. This space will continue to evolve. I think perhaps the most interesting evolution will happen in the way we think about how we use the tools and data available to us. As the complexity of the system continues to mount, data science practitioners will need to adopt more of a clinical mindset. They will establish a differential diagnosis and then test environments and methods in many ways like medical practitioners do today. The way we interact will become more Federated as tools and technology become more democratized. Many parts of the organization will have access to methods and tools that were only the provenance of certain parts of the organization in the past. The transformation that will occur with the convergence described in this question will affect people, process, tools, and mindset. It is a very exciting time to be working in this field.
What does your product engineering roadmap look like for the next 2-3 years? Where do you want to invest your efforts and resources?
As our products and services evolve to meet the needs of our customers and the changing environment, it is certainly important for us to think in ways that are more agile and more inclusive. We do a lot of coordination with our best customers for example. In my group, investment in efforts and resources is as much about meeting the known unmet needs as it is about exploring the unknown, unmet needs.
What kind of events and webinars are most keenly following? How do these events help you understand and analyze the industry trends? [You could mention your favorite webinars, podcasts, etc]
I regularly participate in webinars and events, both as a participant and also in the capacity of keynote speaker or forum panelist. It is critical that we find opportunities to say what we think out loud and allow others to challenge those beliefs. These events are helpful in understanding industry trends in two principal ways. First, of course, paying attention to what people are talking about- especially in the parts of their presentation where they talk about what they see in the future – is very important. Second, as a presenter, it’s very important to listen to the implications behind questions. The unspoken challenges that lie in the way questions are asked- often the prelude to a question or the follow-up contain very interesting insights if one listens carefully.
GDPR’s third anniversary – how have data privacy benchmarks evolved in the last 2-3 years? What lessons have organizations learned from the mistakes of others?
Certainly, the most interesting evolutions have been in seeing how companies have risen to the challenge of complying with this complex regulation. Also, regulators around the world have taken this foundational work as a template for other regulatory efforts in various countries. The benchmarks have moved from strictly compliance to a more forward leaning policy that considers evolving regulation in different geographies and solutions that are more flexible to allow for the nuances in the various regulatory stipulations.
I think it might be a bit early to declare victory on lessons learned. Lessons learned are only lessons learned if we learn from them. To be honest, I see some similar mistakes being made by different parties in different contexts.
How do GDPR and CCPA strengthen marketing efforts? How do you manage these at Dun & Bradstreet?
We are certainly not all the same in terms of our expectations of privacy. Some of us want customization, some of us want strict privacy. These are in some ways opposite things to want. Marketing, especially digital advertising, is extremely complex and growing more complex by the day. The regulatory boundaries which have been set have added another layer of complexity which certainly strengthens marketing in the context of addressing concerns and actually reaching the right people who may be receptive to the message. That said, we don’t all want the same thing so once again I think that the assessment of whether things are stronger needs some time to coalesce.
We have a multi-faceted approach which includes thought leadership from extremely knowledgeable and seasoned professionals, strong internal assessments of feasibility and advisability, and vigilance with regard to the emergence of new technologies and corpora of data.
Do you think we need “One Nation- One Law” for data privacy?
No matter what I personally believe, it is clear that the framers of the constitution contemplated that there will always be certain aspects of regulation that are Federated and certain ones which are not. Establishing an overall policy for data privacy is an extremely complex undertaking. We are learning a lot from the dialogue which is going on in the European Community for example. This is certainly an emerging field of focus and I do not believe there is a simple answer. Furthermore, as the regulators continue to consider the implications of such a question, the technology continues to advance. This advance in technology exacerbates the boundaries of what is possible from a privacy standpoint and from an invasion of privacy standpoint. For example, if one wishes to use all of the applications on a mobile phone to their fullest capability, it is nearly impossible to do so while turning off all location services and other features. Going forward, we probably have more duty to interact with our privacy rather than expecting that it will be conveniently regulated for us.
What role do AI and Blockchain play in influencing data privacy frameworks? Have we slowed down on GDPR adoption / compliance due to the pandemic?
These are really two very separate things. Artificial intelligence has the ability to influence privacy in many ways. For example, not only the absolute letter of regulation can be breached but the implications of regulation can be breached. Data can be used in new and unexpected ways, causing the need for evolution in privacy frameworks. Blockchain has a very specific transmission modality which definitely presents certain advantages in terms of understanding the degree to which a transaction may have been perturbed, but really does not address fundamental principles of the risk associated with parties on either side of the transaction. It is a method which has great promise in certain regards, but it is certainly not a panacea.
I do not see any evidence that we have slowed down as a human race on adoption of GDPR. That said, the pandemic certainly has challenged certain expectations of privacy globally. I have witnessed some very interesting debate around the need to share information versus the constraint preventing sharing certain types of information. I think the pandemic was probably a very good teacher with regards to the complexity of issues around data origination, data rights, data for good, and even manipulation of data, both intentional and unintentional.
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Your opinion on the idea of reskilling needed within the Cloud industry for high-growth industries such as healthcare, marketing and advertising.
It used to be that we needed to hire individuals that have skills in data discovery, curation, synthesis and related quality and insurance quality assurance and governance to enable product evolution and customer service in high growth industries. Today, we need all of that and more. We need practitioners who can think about privacy, unintentional use of data, transparency of methods, and so much more. It’s about ADDING to skills as much as it is about reskilling.
Thank you, Anthony! That was fun and we hope to see you back on itechnologyseries.com soon.
[To participate in our interview series, please write to us at sghosh@martechseries.com]
Dr. Anthony Scriffignano, Senior Vice President and Chief Data Scientist for Dun & Bradstreet, is an internationally recognized thought leader in the data science space. He leads a team of data scientists focused on advancing Dun & Bradstreet’s core capabilities and IP globally. With extensive background in advanced algorithms and linguistics, he holds multiple patents and presents globally on data and technology trends, multilingual challenges in business identity, and artificial intelligence.
Dr. Anthony Scriffignano, Senior Vice President and Chief Data Scientist for Dun & Bradstreet, is an internationally recognized thought leader in the data science space. He leads a team of data scientists focused on advancing Dun & Bradstreet’s core capabilities and IP globally. With extensive background in advanced algorithms and linguistics, he holds multiple patents and presents globally on data and technology trends, multilingual challenges in business identity, and artificial intelligence.
