Malik Abdullah (00:03):

Hello everyone. This is Malik. And on behalf of TripleBlind and 451 Research, which is now a part of S&P Global Market Intelligence, I would like to welcome you all and say thank you for attending today’s webinar, titled Data Confidentiality and Utility: A Way Forward Together. Leading off today’s discussion will be Justin Lam, who is a research analyst at 451 Research joining Justin. We have Chris Barnett who is VP of Marketing and Partnerships at TripleBlind. We welcome our speakers today. And with that, I’ll turn it over to Justin to kick off this event.

Justin Lam (01:07):

Thanks Malik. Hey, thanks for making the time everyone out of your busy day to join us. Hey, really super excited. I just want to introduce myself really quickly. I am a research analyst, part of the information security channel. I’ve actually spent quite a bit of time at various data security companies. And so I’ve had experience in being able to see what works and what doesn’t, and really looking forward to today’s presentation and sharing with you what our research is about and what we’ve seen as data confidentiality and utility work together, Chris?

Chris Barnett (01:43):

Thank you Justin, and thanks everybody for joining, so Chris Barnett here running marketing, and go to market, and partnerships at TripleBlind, I have the opportunity to talk to lots of people in healthcare and FinServe and other segments about their needs for data collaboration, and appreciate your time today. Back to you, Justin.

Justin Lam (02:03):

Super, super. Great. And so we made this presentation thinking about you. I mean, we introduced ourselves and I think this is really about the audiences that we wanted to have and identify with today on talking about the joining of data confidentiality and data utility. And what we’re just seeing as a way forward is predominantly a organizational mandate that is out there where data security, devs, GRC, these three different functions within corporations and enterprises are coming together. And a hallmark of that in general is the ability for the different role players to be able to account and understand each other’s functions, so for example, you may have a data security team that is all about making sure that I minimize the data breaches that go on within my organization, for example. And so a big question that we always ask our practitioners is can the security teams account for some of the development and some of the AI/ML models and cycles that change.

Justin Lam (03:20):

And then something that we ask as part of our research, we ask our developer community out there. Is it easy for you to proactively incorporate security into your technology stacks, and for the data security, and for the devs and the product teams? Can I translate some of the technical controls that I’ve put in place, data encryption, data classification, can I actually translate that into a legal posture that satisfies legal requirements. And can legal teams, vice versa, understand the technical controls that are put in place and translate that as well to that legal posture. And so it’s a really interesting sort of exercise to see how your organization views all these disciplines. Do you view them as interrelated and if so, how much can you relate? We find that this is really essential for understanding and having that conversation around data confidentiality and data utility.

Justin Lam (04:25):

On today’s agenda. What I wanted to talk about, what we wanted to talk about, Chris and I wanted to talk about today were just enterprises in what we characterize as data driven fields within financial and healthcare. And we want to talk about how they’re balancing that data utility, the usefulness of data with its confidentiality. We’re going to especially look at how they’re monetizing non-public information. Often at times it seems at odds with the growing privacy regulation, the intellectual property concerns that are out there, we always hear about data breaches or violations in privacy yet the need is for business models to be changing, to be able to somehow monetize and gain insight from the data that’s out there. And to that end, we call that data utility. Again, that change in that business model. We’ll discuss some of that. We’ll discuss how in that change in the business model for confidentiality and data utility are coming together. And so in our today’s agenda, we’ll talk about where are we now? What are the current trends that we’re seeing in analytics and collaboration that are driving data utility and data security?

Justin Lam (05:33):

And we’ll also take a look at what 451 Research has on where we’re headed, what are the future or emerging trends that are out there? And then I turn it over to Chris to talk about what organizations can do to respond and how can the stakeholders better align, okay? In our research talking about current trends, one of the hallmarks in our research is this idea on the digital transformation journey of data driven organizations versus data drifters or data organizations that might not have the need, they might not have the necessity to really truly be data driven. There is just different business models that are coming about from this and so we’ll talk about some of the current challenges as well. What do we mean by data drivers versus data drifters? In our research here recently for data driven practices, as part of our data and analytics channel, we asked organizations are your strategic decisions, data driven. And the question was, is that 64% overall said either nearly all of them, were data driven or most strategic decisions are data driven.

Justin Lam (06:52):

Some organizations in fairness, they may not have the need to necessarily be data driven. Perhaps they are organizations that they don’t have the telemetry, they don’t have the data to actually make those decisions, or they’re in a position where they can’t necessarily act on them, certainly industries that are just beginning their process in being digitally transformed in order to be able to respond and pivot to the data from their customers and their other stakeholders. That’s an ongoing process. It’s not a condemnation that data drifters are laggers by any means because there have been very, very successful organizations that aren’t necessarily data driven. But what I would say is that many, many organizations are looking to use the macro trends in cloud, in automation, in customer experience, where they’re trying to really understand their stakeholder interactions and be able to drive the data to respond to those customer interactions.

Justin Lam (07:57):

So why data driven? Why are we doing it? And I think that the bottom line here is so that we can arrive at faster decisions or a more flexible way of coming up with decisions. Often at times, what we’ve seen in the past without the data driven approach, organizations will make decisions, but they would often be set in stone. And it would be very difficult to understand whether or not the strategic decision was being made correctly or not. And so with today’s trends in automation, and customer experience, and stakeholder experience, what we’re seeing is just a faster analysis, a faster iteration of the data so that I can arrive at not only a faster decision, but I can actually make more decisions or alter my decision if need be.

Justin Lam (08:54):

And so there are significant benefits to being data driven. I think just in our latest survey, again, the Voice of the Enterprise: Data & Analytics, Data Platforms 2022, these changes in the business model, enhancing the customer service and the engagement, improving the business agility and improving the new products, and services. These are all additional benefits of getting more utility out of the data that I have. If I have more insights about my customers, can I service them better? Can I service them more profitably? Can I service them and expand my wallet share, or my revenue from them? And likewise, if I am data driven, can I actually improve my agility to respond faster, or respond more comprehensively to an emerging competitive threat, or simply a change to the overall landscape in the industry?

Justin Lam (09:47):

But we also wanted to survey what are the challenges that you face in your efforts to be more data driven? And so we’ve actually got a poll here and it’s fine and great to be data driven, but there are obviously challenges here. And so I just will leave it up to our audience here. We’ll take a minute here to see what are the challenges you face in your efforts to be more data driven? Are there limitations in the funding? Is it a challenge to take the existing technology stack or the next existing infrastructure that you might have and integrated into being more data riven? Is there just simply a lack of personnel or mandate on your side? Are there security or governance or privacy concerns that are out there? We’ll give it a minute here, a second here and see if anyone wants to chime in their response and we’ll actually show the results of our survey. Chris, any votes on what you think people will choose?

Chris Barnett (10:49):

Well, you know Justin, I don’t want to skew the audience that’s voting, but my personal opinion, I hear all of these concerns pretty loud from folks that I talk to. But the one I hear the loudest is the governance side of it.

Justin Lam (11:06):

Yeah.

Chris Barnett (11:06):

Trying to get through the internal paperwork, the external paperwork, any legal issues, regulatory issues. That seems to be the biggest challenge rather than getting the humans to type the code.

Justin Lam (11:21):

Right, right. Well, let’s take a look and see what our respondents did as part of our study here at 451 Research. Yeah, so our respondents here, they actually responded, some of them responded 50/50% privacy and governance. Some said not enough skilled personnel and integrating the existing technology. And for our actual results for our surveys, for another population. We looked at, we saw data utility and data security being really some of the top concerns here. And so here in the blue part of the slide here are those that are respondents who identified as data driven. They wanted to be able to say, they said that data quality and consistency, that data utility was one of the top barriers to being more data driven is that they wanted to extract more consistency, more quality out of their data.

Justin Lam (12:24):

But also second and third here were the data security and the data privacy controls that are put in place. And again, this is for the blue lines here, indicate data driven respondents versus the data drifter respondents, which are illustrated in red. If you’d like a closer look at some of this results, as Malik said at the beginning of the hour, the slides will be available for you to review if you’d like to look at it in further detail, or if you have a question about it and you’d like to ask, feel free to chime into our Q&A.

Justin Lam (13:00):

Let’s talk a little bit here about where do the future trends, where are we going with this, and where do we see the industry actually headed? And so the first thing I will say is that the innovation and the experimentation will continue that won’t stop. I think that one of the things that’s surprising, especially among security practitioners is how quickly ML and AI projects never meet production. And this is really critical to know in the development life cycle, is because if you are trying to go for a certain privacy stance or a certain governance stance, it’s not so much being able to audit in a point in time, it’s being able to understand the overall process for how projects actually make it to production for that production data. And this was actually surprising to me as a former security practitioner, two and five, or almost 40% of the ML projects that are initially done in POC stages or pre-production stages are actually abandoned here.

Justin Lam (14:20):

And then the other thing that we see as a trend here is the rise of ESG. ESG concerns here are increasingly present, certainly here at 451 Research, now that we are a part of S&P Global, this has been a huge understanding of the way we conduct our business. But as we’re looking at the data, we’re looking at how we get about it. I want to just be able to point out here the social aspect of it, the immoral or unethical usage of data is still concerning to the majority of respondents. 32% actually said it was very concerning. 30% said somewhat concerned. And in our respondent here for AI and machine learning, keep in mind that we are actually surveying the roles that are responsible for creating the AI and ML models that are out to production. Sometimes we could survey security practitioners or legal practitioners who might skew more towards the immoral or unethical usage.

Justin Lam (15:31):

And they might highlight that as a concern, but what’s really telling about this is that is how universal these concerns are even among the product developers and product designers out there, so I think that if you are a security practitioner, if you are a legal practitioner in GRC, if you are an ethicist within your organization, or you have an ethics function within your organization, keep in mind, it’s not a matter of selling ethics or morality to the data and the governance aspect of it. I think it’s much more of an idea of understanding how are you going to apply some of the controls that are done from a policy standpoint? And I’ll be able to translate that back into the technology aspect of things. I think that all sides of the table here are looking for solutions in order to be able to handle some of these challenges that are out there.

Justin Lam (16:26):

And this is also bleeding over into how we feel about society as a whole, from a completely different perspective. Again, if your organization is being data driven and you are indirectly dealing with other enterprises in a B2B kind of fashion, or maybe you are dealing with other enterprises that in turn have a large B2C sort of function, where there is a large consumer base that’s out there. We survey consumers out there about how they felt about the privacy of data online and how concerned were they. And again, no surprises here, but 74% are very or somewhat concerned about their personal data online. And just the color commentary that I would provide here is that your stakeholders, there are beginning to see standards of trust and beliefs, and maybe those are all still perception based, but for enterprises to be able to respond and say, we have a proactive privacy, governance, ethics approach to handling of your personal data online, that’s going to do nothing but build trust with the stakeholders that you have.

Justin Lam (17:46):

We want to shift here a little bit and talk more about some of the changes in the business model. And so I’m going to throw out a question here about data marketplaces. Data marketplaces in a nutshell, they are business models where we are seeing organizations actively sell and monetize data, maybe it’s via subscription, maybe it’s via an API, maybe it’s via a consumption model. But the point is that organizations are buying and selling and trading data in a marketplace like environment for multiple stakeholders to either publish or subscribe to. The question here is to what extent do you agree or disagree with each of the following statements? Data marketplaces will be in my organization’s top five strategic priorities in the next three years. Do you completely agree, mostly agree, mostly disagree, or completely disagree. Feel free to chime in your choice, but Chris I’ll throw it over to you. What do you think?

Chris Barnett (18:52):

Sure. Justin, my prediction here is we’re going to get back mostly agree as the most popular answer. What you’re talking about here is a huge trend. I do think early adopters are there, mid adopters and followers are still trying to convince management and document the business case, so I don’t think we’ll get completely agree today, but I think we’ll get mostly agree.

Justin Lam (19:19):

Yeah, and certainly organizations. I mean, there’s probably worth another session here, but let’s see what our respondents said here. Our respondents well kind of agree. Well, they agree to disagree, I guess. 33% of our respondents said they completely disagree. 33% said they mostly disagree. I guess that’s 66% saying they mostly disagree or completely disagree. And 33% saying that they completely agree, so it’s an interesting result here. Let’s take a look at what our additional survey respondent said here. When we surveyed our respondents, we looked at what they said and again, 44% said they mostly agree. 38% said that they completely agree that it’ll be a strategic priority for them, so I think everyone’s mileage may vary in how this is, but often in times it varies on what your perspective is within the organization, whether or not you understand, or you can relate to the product roles, the security roles, the governance roles as they are intertwined.

Justin Lam (20:33):

But what I would say here is that the data marketplaces, what we’re seeing in the trends in our broader survey data is that there are strategic priorities. I think that this is certainly not an overnight sort of transition for organizations to make. What I would say is that being able, what we’ve seen here, just kind of underneath the covers here is that organizations they can start small, you don’t necessarily need to have a large all encompassing data marketplace. It can simply begin with a single relationship that you have with one of your existing customers. And let’s say you want to be able to say we’ve got services or offerings that we have can they actually include a set of data that we can actually integrate into their particular workflows or their particular product development. And so there’s different ways that we’ve seen organizations get started with this, but clearly the idea behind this data model and this data marketplace is a network effect.

Justin Lam (21:41):

As more organization utilize the data, as more organizations subscribe to the data, and publish to the data. Their analysis, and the way that they’re able to make decisions can be increasingly more comprehensive, more responsive, and just be able to address some of those new markets that are out there. And so this idea of having that marketplace in order to help other partners in the ecosystem we think that, that’s going to have a tremendous impact in the next few years as organizations have digitally transformed. And now they want to know with all this data, can I actually begin to monetize it for not only my purposes, but also for those of my stakeholders and customers?

Justin Lam (22:31):

And what I’ll kind of boil back this to is from the improvements in AI and ML. I just want to take a broad step back here and see what the impact is on all of our organizations, our individuals, our societies, just from all layers. And again, we looked at practitioners out there and again, just the underpinnings of this data driven market, the data driven enterprise, and the data marketplaces as a business model. We’re seeing again that huge, significant impact, 40% for people who believe in the industry, society as a whole, for the organization, modern impact, and very, very few people have responded saying that there is no impact to AI and ML that’s out there. With that, I’m going to actually hand it over to Chris to talk a little bit about some of the steps in what organizations can do now and how they can think about the process of joining data utility and data confidentiality together. Chris, over to you.

Chris Barnett (23:46):

Great, thank you, Justin. TripleBlind is the provider of privacy enhancing technology or privacy preserving technology, depending on the lingo that you are most comfortable with. We have been working for three years almost with quite a number of prospects and customers to understand their business problems and their needs, so I wanted to provide not TripleBlind product literature, but just a perspective on real organizations, especially in healthcare and financial services and what are they trying to accomplish with their data, especially if they’re trying to maximize the intellectual property value of their data, they might call it a data hub or a data platform as they’re working with partners and really what we have found very consistently and this is a generalization of what we’ve heard from a lot of folks in the marketplace is really four classes of activity or four categories of activity that folks are engaged in.

Chris Barnett (24:51):

And as you can see here, there’s aggregate, and invent, and verify, and distribute. Different companies use different language, but that’s a generalization of it, so we’re going to just take a minute on each of these and kind of talk about the class of problems and the class of use cases that folks are solving, so in the first case, we’ll take a look at the invent side and the use case set that I’m going to talk about really is about major hospitals, academic medical centers that aren’t just treating patients, but they’re doing a lot of research and they’re collaborating around the country and around the world around all kinds of statistical and artificial intelligence research. And in that case, they’re working to use data to explore causality between different conditions, different medications, different treatment plans. And there’s really two reasons that they need to go down the road of privacy preserving technology in this case.

Chris Barnett (25:55):

One is efficiency, if they don’t have the technology solution, they have to fall back to business associated agreements. As I say that, I imagine the audience kind of holding their head. Yeah, we know about those, those are difficult, especially if you have to reconfigure and reconstruct those every time you have a new question to address, a new problem to solve, a new analysis that you want to do, you’ve got to go back and revisit that. And it’s also about extendability, that you don’t have to do all this analysis with just a single data set from a single source that might be biased, that might be siloed. And so really the invent is the first category of activity that really everybody that we talk to is wanting to do. Then we’ll go to the next one, which is aggregate, and I probably should have put quotes around aggregate because in the TripleBlind solution, you don’t literally physically aggregate the data into some kind of data lake, but you logically aggregate it.

Chris Barnett (26:54):

And this is kind of the biggest thing that people want to accomplish is to be able to get analysis insights from a group of data sets that are in different countries, different organizations, have different kinds of legal and physical walls between them. And really if you look at it today, those data sets are, they’re not just accidentally siloed, they’re purposefully siloed because of legal and regulatory, different companies, and even different departments are specifically and purposely keeping that data siloed. And you can’t do analysis on it without violating the legal basis of the silos, unless you have the ability to run that analysis without the data moving across boundaries and out from behind firewalls. And a good example is we see a lot of conversation about healthcare provider data and pharmacy data and those are data sets you see analysis done on, but in most cases you can’t really ever connect the data for analysis.

Chris Barnett (27:56):

Healthcare provider data might tell you who’s had what prescribed, but you have no idea if those prescriptions have been filled, if that patient never took those medications, or they had them refilled 12 times a year for 10 years. And it’s just a case that we see very commonly. Okay, so then verify is the next category when straight statistical analysis or AI of model training is done, it’s done on a particular population with a particular data set, but then the users and the builders of those analyses want to be able to generalize them. They want to be able to use them in other countries and other populations in other situations. And that’s really hard to do because they’re typically those analyses and those algorithms are built off these very narrow data sets what’s available publicly from GitHub or what’s available from a particular academic source. And they have to keep going back to that well, and it’s really limited.

Chris Barnett (29:01):

For example, a data set that comes from Europe that’s used to build an analysis or train models. If you were to try to apply that to India, it really wouldn’t work properly because the normal values of blood labs for example, are really different in India, so you can’t really apply the same analysis for a million reasons. That’s just an example. To be able to take that existing algorithm and apply it to a whole series of data sets that are in different silos without violating any of the privacy and legality of it all, that’s really what we see folks wanting to do, whether it’s with TripleBlind or other ways, so that’s a very strong need that we see in the marketplace. And then the other one that we see a lot is distribute and what we see happen a lot is an impasse, really, somebody owns a data set, somebody else has an algorithm, they want to get together and collaborate, but they’re both rightfully very concerned about the IP, about the security of those zeros and ones.

Chris Barnett (30:07):

So we see an impasse where the data owner doesn’t want to post their data somewhere and the algorithm owner doesn’t want to send their algorithm off. Both folks want to keep their very valuable IP behind their firewalls. And so consequently, nothing happens, or not enough happens, or it takes years to get through the legalities of it all because of that impasse, so folks are looking for a privacy preserving technology way to have the algorithm owner and the data owner work together, get the output, but keep their intellectual property private. Does that make sense? Okay. And then the most common model that we see in the healthcare world is, that people desire and want is what we call the hub and spoke model.

Chris Barnett (31:01):

In TripleBlind, we endeavor to provide the layer of privacy preservation around what the medical center is collaborating on and to let them go out and work with different spokes, so in this case, I have a simple example of three different spokes where people are using an SDK or running analysis on the data. And the biggest contrast here with the way it might be done without this kind of technology solution, is that all of these relationships would have to be done pair wise, right? The medical center has to go out to spoke one, do a business associate agreement and go through all the hair balls, and then they have to do it again with spoke two, and they have to do it again with spoke three, so you can see if there’s 10 or 20 or 30 or a hundred folks, that’s an enormous lift. And typically what people do therefore is they just do a couple because the bandwidth and cost and time of doing dozens is really not feasible.

Chris Barnett (31:59):

But when you apply a technology solution to it and create a privacy layer, then very easily and quickly, the right folks can come along and have access to do analysis on the data, through the hub and spoke model here. And that just adds more value for everybody. And it allows a network ecosystem to be really built. Everybody I talk to has a chart like this, where they want to have an ecosystem, but the problem is building out all those spokes is a really, really hard lift. And that’s the biggest thing we enable. This example’s been about again, academic medical center, but other folks in the audience, if you’re wanting to maximize the intellectual value of your intellectual property value of your data, you could be the hub in this as well. Okay, and then that is really a summary of what we see in the market.

Chris Barnett (32:51):

If you did want more information on TripleBlind, you can get these slides as our moderator said, you can go to our website, tripleblind.ai. We do have a white paper on all this. And also you might be interested in the survey that was done recently. We had an independent survey group, talked to hundreds of chief data officers about what they’re thinking about data privacy and what they’re seeing and looking for in the market, so that’s good information that’s on our website as well. Anything that I missed Justin that you think I should cover?

Justin Lam (33:23):

No. That’s great, thank you, Chris. Thanks for that. Well, let’s just take some time here for Q&A here. Let me see if I can bring up the slide here or any questions and answers that are offered.

Justin Lam (33:46):

But maybe I can summarize a couple questions here that are here. How can I summarize this here? This question comes in common questions that’s asked is what are some of the gotchas to being more data driven? And for practitioners, what should we be aware of? I’ll just take a stab at this. And one of the things that I would just say here is one of the biggest gotchas that we see is scope creep. We think that this being data driven is a journey, it’s not necessarily just a final stage. Organizations that even are characterized as data driven, they are looking to be even more and more data driven and so they’re always looking to be able to incorporate more data and in some cases, demote data or deprecate data, that’s no longer useful for them, but that’s always a constant data refinement.

Justin Lam (34:58):

I think for practitioners and just for being data driven, one of the biggest gotchas out there is not limiting the scope for your next incremental change. And this goes in regards to whether or not you are creating a new ML model for your product or your solution offering, whether you are trying to define the scope for a privacy or regulatory compliance effort, or whether you’re just simply wanting to have a new foundation for how you manage the risk of that data security. All three of those, I think it’s really, really wise to limit the scope because that’s one of the things that I’ve seen out there that have really been a gotcha for organizations that are going to be data driven. Chris, I don’t know if you want to chime in here, but?

Chris Barnett (35:51):

Yeah. I would-

Justin Lam (35:51):

Gotcha.

Chris Barnett (35:54):

I agree with what you said there. Just I would add one thing, which is, I have seen a very common pitfall to be getting excessively focused on the pilot or the proof of concept stage. And I’ve seen companies put big teams and lots of money and lots of dollars and time in the pilot without having just a little bit of an increment of time to devote to thinking about the commercial deployment that tends to get postponed.

Justin Lam (36:23):

Yes.

Chris Barnett (36:24):

And so now they’ve done a wonderful, wonderful pilot with seven rows of data and four columns of data. And then when they go commercial, they have hundreds of millions of rows and hundreds of columns. And they haven’t really that through ahead of time, so I see that very commonly.

Justin Lam (36:41):

Yeah, absolutely. That’s a great point I mean sometimes when we think about proof of concept, sometimes the C should be more capitalized than the P of proof of concept just simply from a, we are understanding what that is because the other thing is that’s out there that is a huge risk is generating solutions looking for a problem to solve. And that again, doesn’t do anyone any good. The reason why we took so much time here in today’s session to explain the why of organizations are coming together to be more data driven and the considerations for all stakeholders, current and future for those different business models. I think it’s really, really essential to set the stage, to see is your organization ready to be able to handle this? If you are going to make an incremental change and limited scope, is everyone agreeing or is there alignment on what the outcome will be before I even begin with even a proof of concept sort of endeavor that’s out there.

Justin Lam (37:52):

Another common question that’s asked here, and this is something that is I kind of abbreviate here, but I’m a practitioner not involved with other disciplines within the organization, how do I reach out to know more? And so, you know, might be someone who’s on the legal team and you want to be able to talk more with the developers that are out there, or the product leads that are out there, or you’re a security practitioner, and you want to know more about how the developers are doing, or the developers want to be able to cross the bridge across the aisle if you will, to other organizations. And as I say that there are mechanisms in place. Being part of the security channel here at 451 Research, one of the things that we’ve seen here grow in immense popularity, thanks to organizations like OWASP are things like a security champions program.

Justin Lam (38:42):

Security champions are a way to be able to embed or shift left data security or just security in general, into developmental efforts within software, and hardware, and cloud. And so being able to leverage those existing channels to understand what are the development cycles like, and then can we actually facilitate data security or data privacy that’s easier for developers to sort incorporate proactively. I think that’s something that is a great way. I don’t think that being able to say, Hey, let’s have a new conversation about how we can combine confidentiality and utility. I don’t think that works well. I mean, if you have existing programs like a security champions program that’s already in place and somewhat more mature, that’s a great way to leverage that and add this conversation to that agenda. Chris, did you want to add anything to that as well or?

Chris Barnett (39:44):

No, I think that’s a great summery.

Justin Lam (39:47):

Okay. Well, great. Great. Well, I wanted to thank everyone for the time here today to talk to you about this conversation about putting together confidential and utility. They’re increasingly not exclusive disciplines, but rather they are coming together very, very quickly. I think that just in terms of the change in the business models, the change in the technologies that are out there, the change in the ways organizations become more data driven. Those are all the underpinnings for why all these different stakeholders in legal, in ethics, in trust, in security, and in data consumption and utility, and product development, they’ve all got to come together to find a way forward together, so really want to thank you for this time here. And here’s our contact information. If you have any questions or follow up, feel free, but Malik, I will hand it back over to you to take us out of the session.

Malik Abdullah (40:53):

Thank you, Justin. That concludes our webinar for today. Thank you, Justin and Chris. As a reminder, the on demand version of this event will be available shortly. On behalf of TripleBlind and 451 Research, thank you for attending and have a great day.

Description:

Enterprises in data driven fields such as finance and healthcare must balance data utility with confidentiality.  Often, enterprises and their partners want to analyze or monetize non-public or personally identifiable information, often at odds with growing privacy regulations and intellectual property concerns. Innovations in business models, AI and collaboration grow the demand for data utility.  In this session, 451 Research and TripleBlind will discuss ways enterprise can move both their data confidentiality & utility forward together.

We’ll discuss:

  1. Present – How did we get here? What are the current trends in analytics and collaboration?
  2. Future – Where are we headed? What are the future or emerging trends?
  3. Now – What can organizations do to respond?  How can stakeholders better align?

When:

Date: Wednesday, September 07, 2022
Time: 12:00 PM Central Standard, 1:00 PM Eastern Daylight Time
Duration: 1 hour