Simplify for Success - Conversation with Chris McLellan
Chris McLellan was on #SimplifyForSuccess, a podcast series presented by Meru Data and hosted by Priya Keshav.
Chris spoke about the Data Collaboration Alliance, a nonprofit working with the mission to advance technology standards and protocols aimed at minimizing the use of data in operational environments.
He also spoke about Dataware technology, which is a network-based architecture that eliminates silos and enables the use of a single network of information that grows over time. Thank you to Fesliyan Studios for the background music.
*Views and opinions expressed by guests do not necessarily reflect the view of Meru Data.*
Transcript
Priya Keshav:
Hello everyone, welcome to our podcasts around simplifying for success. Simplification requires discipline and clarity of thought. This is not often easy in today's rapid paced work environment. We've invited a few colleagues in data and information governance space to share their strategies and approaches for simplification.
Sustainable privacy programs require robust information governance. Data needs to be governed and managed from creation to deletion. Operationalizing privacy means streamlining and simplifying programs at scale. It requires us to rethink the technology and the automation that are currently in place to manage data today.
Our podcast is about dataware and how it can help avoid making multiple copies of data. I really like Chris’ statement where he says if data is valuable, you should not be making multiple copies of it. Hope you enjoy this episode as much as I did. Hi Chris, welcome to the show.
Chris:
Hey Priya, thanks for having me here. It's great to be here.
Priya Keshav:
Tell me more about yourself.
Chris:
Well, thanks for the opportunity for that. Well, my name is Chris McLellan. I'm based in Canada, but I've definitely had a career that spanned the globe and very fortunate to have worked in long time in Europe, particularly in London. But I guess my career has been one of advancing innovation and technology. I started some time ago in telecommunications and digital networking manufacturing and then that was my first taste in the world of technology. I was a project and eventually a product manager for making little black boxes that make up the Internet. That was very exciting, there was this company called Newbridge Networks, and since it was bought out by Nokia, but from that point I think around it's probably fair to say every four to six years, I’ve caught the next wave of technology.
So, I went from, I guess, the infrastructure of the Internet, if you will, to web which is an application sitting on top of it, then into mobile and apps particularly. I was very early on, I guess, in mobile applications and then during that time had shifted into doing same for marketing I suppose in strategic partnerships then product and project management. But I was a part of the founding team, not that I was part of the early team at a company called Halo which was like an early rival to Uber if you can imagine such a thing. But we were based in London, we ended up in 18 countries around the world. Just saying something close to 30,000 plus drivers and it was a really exciting time. The game was changing on the venture capital front and as well as the technology front, as things really, radically moved into mobile, much more faster.
And then fast forward a little bit more to recent times, I recognized I guess around 5-6 years ago that marketing as profession is becoming increasingly data centric and automated. And so for my own curiosity, as much as anything, I started an event series and podcast on artificial intelligence and it's called “Ask AI” and it's a non profit and it said Ask AI dot Oregon and that really opened up a lot of conversations like this one with thought leaders in AI, and eventually that led me through kind of an interesting route to where I work today, which is at the Data Collaboration Alliance which is a nonprofit advancing data ownership and data collaboration or collaborative intelligence.
As well as my role at Cinchy, which is a pioneer in a new category of data management technology known as Dataware that incorporates the hot trend in data management right now called the data fabric technology. So that's the canned history of me if you will, and how I ended up where I am today.
Priya Keshav:
Tell us more about Data Collaboration Alliance and then maybe we could talk about Cinchy and the technology itself, but maybe talk about Data Collaboration Alliance. So what are they and what do they do?
Chris:
The Data Collaboration Alliance is a group of partners and community members who share the same worldview that data has great value and as such it ought to be managed and protected much more like societies around the globe protect, say, their currency or intellectual property. And when you think about those things that enable society to function much as data increasingly does, we make it very difficult, if not impossible in some cases to copy it. Because when you copy something, you devalue it and you lose control of it and so I guess our worldview can be best summed up in where our mission is to advance technology standards and protocols, and concepts that either greatly minimize the use of data in operational environments or end or eliminate silos, data fragmentation and copies. And data integration is sort of a stand-in word for copies.
So, the IT world today, the world of applications is really defined by silos and copies, database silos and copies exchanged between those silos. And so we're really trying to help accelerate world beyond those things so we can get back control of data. And control leads to two things, one is the ability to give data stakeholders or rightful owners meaningful control over their information. Sort of moving beyond the meaningless consent form and into real data level control, and then secondly to encourage collaboration between entities that currently have a lot of friction preventing them from collaborating on data. Things like data sharing agreements or compliance rules or regulations and what is the blocker to meaningful collaboration on data. It's in those areas, so that's a bit about the data collaboration lines in terms of our worldview and mission. I'd be happy to explore some of our programs in more detail, but that’s what we're for.
Priya Keshav:
So tell us more about... so you are basically saying that through the technology, you could promote a single copy of the data and sharing in use cases from just a single copy as opposed to all the copies of data that we see typically in pretty much any every organization. So how do you make that work? I mean what is the technology behind the scenes and why is it different from what's existing today?
Chris:
Well, what's existing today I touched on in my just previously in terms of a lot of people are familiar with the mantra and app for everything. Apple came out with that along time, but there's an app for everything, sure, but there's an app for everything in a database for every app and copies exchanged at scale unrestrictedly between databases at scale so. That's what is the situation today and even some of the, I guess, the mitigating technologies out there, such as data warehouses, data lakes, while they're great technologies and data virtualization. These are great technologies and they serve really good purposes, particularly in analytics, but they are still silos. And when you think about them and so they are not intended, they weren't built to help simplify organizations in IT ecosystems. They don't remove t any old data silos or databases necessarily, or not as a direct result of the use. Nor do they eliminate the need to create new silos moving forward as organizations build new technologies, new business solutions.
So we're in a funny place today where there's now that's been recognized That these were really good interim technologies, but we're all heading towards a world of increased IT complexity again because they're not getting to the root cause of data insecurity and data risk, which is silos and copies. So, how do you get beyond those. Well, at the alliance, we don't advocate any one specific technology, we are technology-agnostic. But it's clear that though, and I should also caveat this with, there's no silver bullets in technology and in data privacy and data protection, data governance, what there is a steady, hopefully, willful transformation to a better place and so data minimization technologies, data encryption technologies, blockchain, adding what it does in terms of decentralization and things like contracts, dataware is an emerging technology. I happen to work with that, it incorporates, like I mentioned, at the top of data fabric, these are all technologies and you can look them up, they're sort of hot things in data management within analyst firms like Gartner and Forrester.
I think in large part because they're seeking to simplify the very complex and through that act what we're doing is minimizing data usage, minimizing data copies, minimizing data silos. And the end result here is control and increased control, so that I hope that sheds some light on some of the technologies. But there are also protocols and standards and we can touch on that. The Alliance, we advocate and participate in the development of a new standard called zero-copy integration, which is soon to be a national standard in Canada. People can look that up but there are other people working on this like Tim Berners-Lee, he has a solid protocol which is also very interesting and seeks to achieve many of the same goals I was talking about.
Priya Keshav:
Let's talk about Dataware right so you're also part of Dataware, which is one of the technologies that can simplify so, would you like to tell us a little bit more about what Dataware really is and why is it different?
Chris:
Yeah, sure, I mean and I want to be very transparent here. I'm the director of operations at the Data Collaboration Alliance which is a nonprofit and agnostic to any specific technology, but has the mission that I stated before of data simplicity, data ownership and collaboration. And my job at Cinchy, which is a software vendor in Dataware you described ,is to advance Dataware as a category. So I'm not in sales, I'm not marketing, my job is to raise awareness and understanding of some of these new categories, which include that are driving, that control simplicity and collaboration and dataware is one of them.
Given this is audio and it's hard to show and tell, the best way I could describe it is it uses a new type of data architecture, so how it manages datasets is quite different, but what I love about it is that what it really reflects is the natural design brought to the digital world? I mean, so often is the case, the natural world has figured out, solved problems that we seem to have forgotten about when we come back to. So, think of the brain, the brain manages information that manages each one of us listening to this podcast, manages more information than even the largest organization on Earth. And they do like to like comparisons, which is incredible. And it does so in a very small, compact format, like fits between your ears, that's incredible. And but how does it do that? Well, the short answer here is it manages information as a network, right? You have axons neurons and it's not making copies. It's not making copies of itself. And it's like all networks, they just scale. The really unique thing about networks, whether it's the phone network, the power grid, or the brain or dataware technology is they abstract complexity by using a network-based approach.
So when you look under the hood of a Dataware platform, what you're seeing is a combination of technologies in data management. One of which I mentioned, the hot one in the analyst world right now, the data fabric that provides the ability to connect legacy data into a dataware platform. Data then is instantly interconnected to form that network I was talking about and you have, like a linked data and other capabilities brought in that way. It also because it's a zero-copy environment introduces domain-centric governance. So in federated data governance so teams are empowered to control the data they use. Let's say finance or marketing or IT, each one of these teams are empowered within the platform to manage the data they use and should be the owner for and that sometimes referred to as a data mesh, which is another word or term category you'll hear a lot about.
And then when you think, finally, it incorporates the core capability of a no-code platform, so you can actually use something that we call autonomous data. Some sort of self protected data the access controls are baked into the data to combine that with active metadata to build new solutions. So the purpose of dataware is to build new apps, new dashboards, new automations, new business capabilities. But to do so hundreds of them can be powered by this single network of information that grows over time with every project. And so, what's not in that situation, there's no new database is being generated so no new silos and there's no point-to-point data integration because there's no new silos, so you're eliminating copies and what that means is you get control and simplicity and agility. So I know that it's probably difficult, but think of it as, and it's what's different about it is that network-based architecture that makes all the difference?
Priya Keshav:
So are there any use cases where somebody has implemented dataware or any other similar type of technology to sort of eliminate data silos?
Chris:
Yeah, for sure. I mean the use cases we tend to refer them to as themes or patterns in terms of how people are using our technology, but there's a few that are emerging and again you can check it out some of these things at Cinchy.com. But for example, I touched on some of the patterns are like to gain something like over 50% in time and cost back in the development of new technologies, automations, applications, dashboards, real-time systems. It's also used to add...this is really fun capability in terms of adding new capabilities to existing apps. So I mentioned data fabric was part of a Dataware platform so imagine you connect your CRM into a dataware platform.
It's data can then be updated, upgraded, protected and then sent back to the CRM platform in as sort of modernized upgraded data and information, so you can actually give your legacy apps like CRM superpowers because the data is being augmented within the dataware platform. That's really cool. It's also used for things like to standardize and distribute reference data. Another popular use case is building 360 views of your customer, of your organization, of your partnerships as well as digital twins to replicate mainly architect infrastructure within a data environment to test outcomes through scenarios.
There's some other outcomes that result as well, like the elimination of spreadsheets, operational spreadsheets from the organization, well, spreadsheets are great, but I recently posted on LinkedIn that the NHS, the National Health Service in the UK during COVID, had used a spreadsheet in part to create a contact tracing solution and not only is there no scalability in the spreadsheet and not only do they kind of confuse how it manages rows and columns, which is somewhat unforgivable? But there's no access controls on the spreadsheet, and there's no means to prevent copies. And so you should not be using spreadsheets for operational outcomes if you care about data privacy and data protection. So these are some of the use cases that people are using Dataware for.
Priya Keshav:
So, we talked about data ownership, right? So coming back to the Data Collaboration Alliance, what are you doing? What are some of the things that you're doing to showcase data ownership? Especially, you brought up some great examples right of sharing data where it is currently a big issue. So how can some of these technologies help in sharing data more securely and also in a privacy-preserving manner? And also maintain data ownership.
Chris:
Yeah, and I would just remind the listeners that there are no silver bullets and there's no one technology that's going to solve that problem, that challenge. But just when you think about the Internet, it's a combination of many technologies in routers and switches, and fiber optics but also in software, when you think of protocols and TCP IP, there's a lot that goes into making things like this work at scale. So we run four programs at the Data Collaboration Alliance. The first is we run interoperability tests between these types of technologies I mentioned earlier, like encryption, dataware, security encryption, web3 technologies, blockchain. What we're trying to do through our collaborative intelligence network or CIN program is to provide a lab of form in which to run interoperability tests. To try to build a global architecture that but what they all have these technologies.
The outcomes that we're trying to achieve here is control, simplicity, accessibility, transparency, federation, decentralization. These are all the themes we're working towards, so that's the collaborative intelligence network. And we have a free community, and the community app is built on dataware platform. It's called the data collaboration community, you can check that out at data collaboration.org/community. It's free to join and what we do there is really demonstrate that collaborative intelligence side. So what happens in that community is people login, they can get free access to open datasets, most of them are currently in the data privacy domain, but our goal is to extend into more domains like sustainability, healthcare, agriculture, open society, open finance. But they get access to open datasets and they get access to free tools that are powered by those open datasets. So we have all these are really kind of research tools, but they would really help privacy professionals do their job and so I encourage folks to join us for free, to access these open datasets and free tools.
But the heart of the community is really what we call collabs, and these are like projects where our members can come together and create a sort of a crowd sourcing situation where they can all contribute along with our partners who can connect data in through a data fabric to sort of crowd source open datasets. And it's a really exciting prospect 'cause this is like really facilitating that cross-team, cross-person, cross-organization collaboration on a singular outcome like a new tool or a new open data set. So that's what goes on in the data collaboration community. We have a free partnership program which we probably don't have time to go into too much, but they participate in that community and they can run certain things with us and then we have a software for good program.
Finally, where we work with partners like Cinchy and others to give away free software for good causes, free data management software, and particularly research institutions like universities and global nonprofits and charities. And that's what happens at the alliance. It's the final thing that's exciting is in terms of advocacy, we participated in helping to promote zero-copy integration, that standard I mentioned, and really, it's a framework to encapsulate all of these principles that have been talking about for developers of the future to look at. So think of it as a modern or an upgrade of privacy by design, but taking things a lot further in terms of controlling data.
Priya Keshav:
Any other closing thoughts?
Chris:
No, I mean, I hope I didn't go on too much. It's...I enjoy, hope people can hear it in my voice that I'm we're very excited about this. At the Data Collaboration Alliance, the approach we're taking communicates pretty well I think, and I hope which is that data has value, owning it is a human right, not a property right in our view.
And if you believe that data has value, then one hopes that you're aligned to our worldview that you shouldn't copy unrestrictedly things of value. And so that I'm always happy to get on calls like this to help sort of cut through the complexity and offer that analogy, which is if it has value, let's stop copying it.
And can it really be that simple as a path to control ownership, privacy, protection, governance? Kinda... like there are nuances and complexities on the road. Of course, like even sometimes the notion of what is a copy is a temporary cache of some personal information a copy, yes, but it's better than a lift and shift of my entire data profile or history going from one app to another app who might be in another jurisdiction in another country. So there are nuances here. But I think we're on a good road and it's a really exciting time in data management technology. Things are fundamentally changing at the architectural level in a way that's really exciting.
Priya Keshav:
Thank you so much for joining us, Chris. It was really fun talking to you.
Chris:
My pleasure as well. Thank you so much for having me.
