Policy Manager and Transparency Center demo

Teresa Rojas

0:03 We're now going to talk about how governance has to adapt to people. Every organization has unique needs, structures, processes, and people. Before I jump into the demo per se, let me share what's what shaped what you're about to see, and it's nothing but dozens of customer interviews where we talked so much about the need for a common language and how data governance is a team sport.

0:41 We also heard over and over how governance was seen as a layer on top or a siloed away system that is manual, scattered, and just disconnected from business needs, and very often an afterthought. Let's face it, we'll never be successful at making people adapt to governance, but we can adapt how governance works so that it adapts to organizations how they really work.

1:26 So let's start by getting under the skin of a steward.

Teresa Rojas

1:32 So this person can be somebody like what Steven was mentioning, like a financial controller in the a finance function, or maybe it's an analyst in the logistics organization. They have a full-time job, so the the last thing that they want is to be dealing with manual low-impact scatter tasks. This is a drumroll moment for us right now. So let me introduce you to Atlan's Transparency Center, a home for all things data governance, where stewards can spend less time, not more, less, to be more effective and impactful by managing by exception.

2:23 You're probably wondering how, so let me show you. So first aspect of managing by exception, I have to know if there's something important that requires my attention, and we literally have this widget that tells you whether there's any open incident, policy breaches, new ungoverned assets, or things for you to approve. I can also see everything else that the other stewards are working on. I can see a bit the breakdown of the policies on their status, and as you can see, we cater for different policy types. I can dive into my policies and look into what is awaiting for approval under attestation or about to expire.

Teresa Rojas

3:13 Let's look into policymaking for a minute. Policy creation can be time-consuming, especially with emerging types for new technology demanding searching for multiple sources or I, I don't, I don't know about you, like I'll be very honest, for me, starting from a blank page, it's always very, very difficult. So this is where we thought, like why can't we just go into this era of AI-augmented stewardship? This is a step of governance adapting to people.

3:58 So let's assume here I'm, let's say in a pharma sector, and I want to do a global policy for the AI Act. So here, Atlan AI is going to help me out writing down a purpose for it. This is obviously just a starting point which I can edit and adapt to the needs of my organization. But I get a head start, and I avoided that blank page syndrome, right? As we mentioned before, this we can do many different types of policies. We can set the validity periods, the review periods. We can associate terms.

4:47 I hope you'll all agree with me, a glossary is the backbone of every data governance initiative. We can also document exceptions when we have them, right? I'll show you I had used Atlan AI also to draft the policy over here, and let me show you the ultimate value unlock. So here is where we define the technical scope of the policy.

Teresa Rojas

5:21 Not only can I define it that, that technical scope, I can also do it in natural language. So let's say all verified. Oof. Let's say from Snowflake. So Atlan AI is there looking for the assets that comply with these conditions, and I can check the assets that it's proposing me, and I can add them to my policy. Policies with this stop being a piece of paper or a PDF collecting dust somewhere.

6:09 We're actually connecting the policy to data. We can also have policies related to each other. In this case, I'm with a human oversight, so we have other data ethics policy around, so I put it together. But you can do this also for running campaigns or other types of initiatives. Let me correct the workflow here. I want to use this approval workflow for this particular one. So now I'm ready to send this for approval. So I'm going to go ahead and send this. That was easier, right? Let's now see how it looks like for the approver.

Teresa Rojas

6:57 This is very solicited people. Having to handle multiple requests and a variety of scattered sources, or even worse, deciphering cryptic messages from multiple systems. Well, at Atlan we said, "No more of that." So we have created the Atlan Inbox. This is really the idea to have a purpose-built, focused, actionable inbox. You can see over here, I can see all the requests that I have created, and I can see their exact status.

7:43 I can see what has been approved for me already, and I can see the things that are assigned to me, even with not just when something is overdue. And I can take action in context. I can complete here. I'm going to go ahead and approve this. And now back. So now, with a little refresh, we now see what happened, and my policy is now approved. So I bet you're wondering, how is that approval set up? Do we now need armies of people to create and maintain workflows?

8:34 Well, let me tell you, you couldn't be more wrong. At Atlan, we know that the best people that can create the approval workflows are the people that actually work in the process. We see no reason to have SMEs having to create requirements to then send them for for IT, to then have them something back, test it around, and so on. There's no need to blow departments. So we thought of building a workflow engine, completely no code, that can fully empowered SMEs to build their own workflows.

Teresa Rojas

9:14 So you see over here we have different workflow templates, change management, say, to govern your assets, glossaries, and more. Data access request, policy, that is what I just showed you, and much more. We can see in the monitor the things that have been running, what is the status of each of them. I can also zoom in on the ones that are created by me. Let me show you how that looks like. I promise you, fully no code. So here it is. So first of all, remember how we talked about the need to adapt to how organizations work? Well, this is another example.

10:04 You can create workflows that are for one specific policy type or for all of them. You choose the granularity that makes sense. You heard Stephen talking about domains, right? So we allow also for that federation of policies where a policy can be exclusively for one domain or more. Global policies are, of course, also possible. We have different strategies for approvals, like anyone approves, all approve in a particular order or in a specific sequence, where we can use people and groups and select the order in which this happen.

10:52 We can select the appro— the expiry period for that approval, and for other types of workflows that I won't have the time to show you right now, you also have the possibility to select what is the auto approval criteria. So when you don't want a human in the loop, but you still want to have that governance cycle going on. Then I can only check how I want the users to be notified, and that's it. With a simple update or a publish button, my workflow is ready. No staging, no promotion, no waiting time. The next request that comes in will be immediately governed by this.

11:46 All right, so those are our governance workflows. Let's get back to the Transparency Center to the parts that I haven't showed you before.

Teresa Rojas

11:54 Like, so okay, I see there's an incident over here. Let me take a breath, show you the rest, and we'll deal with that just now. So we can see over here what's the landscape of our policies in terms of status and policy type. We can actually track assets govern over time. You saw how we linked that policy to assets. Well, we can also see the trends. So an uptick, a downtick. As a governance tool, it gives me a lot of information, maybe there's something I have to look at. We can see the scope of our different data policies, and an old favorite of mine, assets with multiple policies.

12:42 I was talking not long ago with one of our customers on how this is just, like, such a natural use case when you have a single asset that has to comply to more than one policy, but the problem is 100% of the time this is just solved through tribal knowledge. Well, no more. No more need for just tribal knowledge, right? Like, what happens if the person that knows is out with the flu? That's no longer the case, because with that connection, we can actually see at the asset level what policies govern it, and on the policy level, what's the technical scope. Isn't that great? We are very proud.

13:27 We can also monitor the exceptions because, of course, exceptions happen, and we have correct visibility for them. So now let's get into that nasty incident, and this is how we were talking about managing by exception, right?

Teresa Rojas

13:45 So I can see here... So the system's already telling me this is a problem with the AML retention policy. So I know we have a specific policy to retain those records for longer, so I'm just going to take a quick look to see, and I can spot it right here. You see, this is an asset that is under AML, but it has a one-year data retention period. That is clearly a mistake because we have a policy for 10 years. So now I just have to create a task for the owners of those affected assets, and this will be solved. Now imagine if this was the real world.

14:29 What would have taken for me to find out that there's been this this error was made? It would have been many maybe a matter of days, a matter of weeks, months. How would we know? But with the Transparency Center, we have actually our fingers on the pulse of our data, and we can act immediately. So that was a lot.

Teresa Rojas

15:05 This is what we learned. Old ways for managing policies and approvals created friction. Today, more than ever, with everyone racing to understand the impact of AI on our data and our business, we need governance adapting to people to drive innovation. Atlan's Transparency Center and governance workflows brings stakeholders together, adapting to the way modern teams operate, breaking all walls by directly connecting policies to data. No more loose wikis or PDFs taking dust. We now have a true home for data governance.

15:52 So we can move from the world of hoping that our policies are being followed to the world of action and the world of knowing that our policies are being followed.