View Full Transcript00:05 \[music\] 00:13 00:13 >> Across the Asia Pacific, governments are 00:15 00:15 actively regulating how public agencies 00:17 00:17 share data. 00:20 00:20 Singapore's Public \[music\] Sector 00:21 00:21 Government Act, Australia's Data 00:23 00:23 Availability and Transparency Act, 00:25 00:25 China's Data Security Law and Public 00:27 00:27 Sector Data Directives all govern how 00:29 00:29 public sector \[music\] data may be shared 00:31 00:31 and protected under heavy safeguard. 00:34 00:34 Here's the challenge. 00:36 00:36 To ensure inter-agency collaboration. 00:39 00:39 How do you share live, highly governed, 00:41 00:41 and semantically unified data across 00:43 00:43 agencies for human and AI consumption 00:45 00:45 respecting these data sharing laws? 00:48 00:48 Denodo provides the AI data layer 00:51 00:51 enabled by active context for data 00:53 00:53 sharing and for agents. 00:55 00:55 Four pillars make it work. Universal 00:58 00:58 connectivity, zero copy delivery, 00:60 00:60 \[music\] governed access, and semantic 01:02 01:02 trust. 01:03 01:03 Hold on a few minutes and you will see 01:05 01:05 \[music\] it working across an actual 01:06 01:06 smart city. 01:08 01:08 Let's start with the Denodo data 01:10 01:10 marketplace, which is an intuitive shop 01:12 01:12 for data experience with semantic trust 01:14 01:14 \[music\] built in. 01:16 01:16 A modern city has several independent 01:18 01:18 agencies like emergency \[music\] 01:19 01:19 services, police, public health, and 01:22 01:22 transport. 01:24 01:24 Here, each agency \[music\] owns and 01:26 01:26 governs its own data products, metadata, 01:28 01:28 and business definitions inside its own 01:30 01:30 boundary called a domain. 01:33 01:33 These domains can be the foundation of 01:34 01:34 governed inter-agency sharing. This is a 01:37 01:37 shared operations domain. 01:39 01:39 It combines authorized information from 01:41 01:41 all four agencies. 01:43 01:43 Live, zero copy, \[music\] and the 01:45 01:45 governance policies are enforced right 01:47 01:47 here in the data layer. 01:49 01:49 For example, through this shared 01:51 01:51 operations domain, the police agency 01:53 01:53 will not have access to data owned by 01:55 01:55 public health while engaging in shared 01:57 01:57 operations. 01:59 01:59 It does not matter which data sources 02:01 02:01 each individual agencies are using. 02:04 02:04 The Denodo platform federates authorized 02:06 02:06 information from all the four agencies. 02:09 02:09 This is universal connectivity. 02:11 02:11 Metric views are reusable metric objects 02:13 02:13 defined once with sources, dimensions, 02:16 02:16 and metrics. 02:18 02:18 So, a measure like active incidents 02:20 02:20 across the city has one consistent 02:22 02:22 definition. 02:23 02:23 This makes sure that every human and AI 02:25 02:25 reads the same certified number for 02:27 02:27 active incidents or average severity. 02:31 02:31 This is semantic trust in action. 02:33 02:33 Now that we have seen the foundations, 02:35 02:35 let us take a visit to Meridian City 02:37 02:37 \[music\] and explore how the Denodo 02:39 02:39 platform helps the local authorities 02:40 02:40 deal with a sudden tier one emergency. 02:44 02:44 This is Meridian City Smart City 02:46 02:46 Operations Center. 02:48 02:48 The three-dimensional situation map of 02:50 02:50 Meridian City has colored columns, which 02:52 02:52 are incidents, and the height of each is 02:54 02:54 risk severity. 02:56 02:56 The red pulse is the critical incident 02:58 02:58 we are investigating in this example. 03:00 03:00 >> \[music\] 03:01 03:01 >> A hazardous material spill just hit the 03:03 03:03 Meridian interchange. 03:05 03:05 It touches all four agencies at once. 03:09 03:09 Let's see how we can arrange a 03:10 03:10 coordinated response across all the 03:12 03:12 agencies governed by interagency data 03:14 03:14 sharing regulation. 03:17 03:17 Here is the AI operations agent. 03:20 03:20 The AI has analyzed the incident and 03:22 03:22 proposed a coordinated response, and 03:24 03:24 every action is assigned to the agency 03:26 03:26 that owns it. 03:28 03:28 Now, let's ask this agent what to do 03:29 03:29 next as a fictional operations 03:31 03:31 commander. 03:33 03:33 The agent has come up with a coordinated 03:35 03:35 response plan. 03:37 03:37 For example, secure perimeter, dispatch 03:39 03:39 ambulances, set up air \[music\] 03:41 03:41 monitoring, etc. 03:43 03:43 Now, if we switch the operating agency 03:45 03:45 to police and ask the same question. 03:49 03:49 The AI's answer changed completely. 03:52 03:52 You can see that the actions listed are 03:53 03:53 applicable only to the police department 03:55 03:55 or agency. 03:57 03:57 Let's try the same experiment with the 03:59 03:59 public health agency. 04:01 04:01 Again, you can see that the action plan 04:03 04:03 recommended has been tailored 04:05 04:05 specifically for public health. 04:08 04:08 Now, let's look at a fire test of this 04:09 04:09 governed access layer. 04:12 04:12 As the public health agency, let's ask a 04:14 04:14 question that a public health persona 04:16 04:16 may not necessarily need to know the 04:17 04:17 answer to. 04:19 04:19 For example, who is the suspect? 04:22 04:22 As you can see the AI answers, but 04:24 04:24 strictly \[music\] within policy. 04:27 04:27 Police investigation details are not 04:29 04:29 available within the public health 04:30 04:30 operating context. 04:32 04:32 However, the police has secured the 04:34 04:34 scene. 04:35 04:35 It told public health exactly what they 04:37 04:37 are allowed to know. 04:39 04:39 This is one step above using the simple 04:41 04:41 \[music\] masking or reduction you see in 04:43 04:43 many other platforms. 04:45 04:45 Active context here is the enabler of 04:47 04:47 governance rather than simple data 04:49 04:49 protection. 04:51 04:51 As the operations commander, let's 04:53 04:53 approve the response \[music\] plan, which 04:55 04:55 has been recommended by the AI 04:56 04:56 operations agent. 04:59 04:59 Response plan has been approved and is 05:00 05:00 immediately auditable \[music\] with 05:02 05:02 timestamps included. 05:04 05:04 Four agencies just coordinated one 05:06 05:06 response over live governed data that 05:08 05:08 never left each agency's ownership. 05:12 05:12 In many platforms, AI reasons over a 05:14 05:14 centralized copy of data. 05:17 05:17 What you saw is AI reasoning over the 05:18 05:18 Denodo I data layer, which is enabled by 05:21 05:21 context, delivering zero copy across 05:24 05:24 \[music\] four agency domains in real time 05:26 05:26 with policies enforced. ### 데이터 복제 없이 기관과 기관 간에 신뢰할 수 있는 실시간 데이터 액세스를 가능하게 합니다 디노도 플랫폼은 데이터의 소유권을 보호하고 거버넌스 정책을 시행하면서 공공 기관 간에 승인된 데이터를 연결하는, 액티브 컨텍스트 기반의 AI 데이터 계층을 제공합니다. **연결, 거버넌스, 신뢰, 조율** 완벽한 연결성, 제로 카피 배포, 액세스 거버넌스, 시맨틱 신뢰를 통해 실시간으로 데이터를 공유할 수 있게 함으로써, 사람과 AI가 일관되고 신뢰할 수 있는 정보를 바탕으로 작업할 수 있게 합니다. AI 에이전트가 각 기관의 데이터 접근 정책을 준수하면서 스마트 시티의 응급 상황 대응을 어떻게 조율하는지 확인해 보세요. **AI를 위한 신뢰할 수 있는 데이터 공유에 관심이 있으시면 데모 동영상에서 자세한 내용을 확인하시기 바랍니다.** ### Roles AI DeveloperArchitectDeveloper ### Solutions - AI - Cloud Data Integration ### Industry - [Public Sector]() [디노도에 문의하기](/kr/contact-us)