GoodData Product Journey: Inside Q2 Features, Roadmap, and Customer Success

00:55:40
https://www.youtube.com/watch?v=t35VLXwnGCw

摘要

TLDRIn this webinar, Ryan Dolly, VP of Product Strategy at Good Data, discusses the latest features and updates for Q2, including a demo of Good Data AI and a customer spotlight on Sustain 360. Key highlights include the introduction of smart search and an AI assistant, which enhance user experience by providing quick access to analytics content. The session also covers flexible layouts, dynamic text references, and ongoing accessibility improvements. A live demo showcases Sustain 360's use of Good Data to create a unified climate intelligence platform, emphasizing the importance of multi-tenancy and data integration for effective carbon footprint analysis. The webinar concludes with a roadmap for future developments and upcoming events, encouraging attendees to stay connected for the latest updates.

心得

  • 👤 Ryan Dolly introduces the webinar and its agenda.
  • 🔍 New features include smart search and AI assistant.
  • 🌍 Sustain 360 demonstrates climate intelligence using Good Data.
  • 📊 Flexible layouts and dynamic text references enhance user experience.
  • 🛠️ Accessibility improvements are underway for better usability.
  • 📅 Upcoming features for Q3 include table improvements and relative date filters.
  • 📈 The audit log provides transparency in user actions.
  • 💬 Join the Good Data community on Slack for updates.
  • 📅 Next product update webinar is on September 17th.
  • 🌟 Good Data aims to empower users with customizable analytics solutions.

时间轴

  • 00:00:00 - 00:05:00

    Ryan Dolly, VP of Product Strategy at Good Data, introduces the webinar, outlining the agenda which includes new feature updates, a sneak peek at the Q3 roadmap, and a customer spotlight featuring Sustain 360. He emphasizes the importance of the Good Data platform for analytics and invites attendees to engage through the Q&A feature.

  • 00:05:00 - 00:10:00

    Dolly discusses the launch of Good Data AI, highlighting its two main features: Smart Search and AI Assistant. Smart Search allows users to find content quickly by understanding context rather than just matching names, while the AI Assistant helps users find and create content through a chat interface, enhancing user experience and accessibility.

  • 00:10:00 - 00:15:00

    The AI Assistant is demonstrated, showcasing its ability to interpret user queries and suggest relevant data visualizations. Dolly explains how users can interact with the AI to generate charts and modify visualizations, emphasizing the ease of use and the assistant's capability to understand user intent.

  • 00:15:00 - 00:20:00

    Dolly introduces flexible layout options for dashboards, allowing users to create more complex and customized layouts. He explains the new container feature that enables users to arrange visualizations more freely, enhancing the overall dashboard design experience.

  • 00:20:00 - 00:25:00

    Dynamic text references are introduced as a powerful feature that allows real-time data values to be displayed in text elements on dashboards. This feature enhances communication of insights and can be used in descriptions for better context.

  • 00:25:00 - 00:30:00

    Dolly highlights the new audit log feature, which provides a comprehensive record of actions taken within the platform, available to enterprise customers. He also discusses ongoing accessibility improvements to ensure that analytics are usable for all customers, including keyboard navigation and screen reader support.

  • 00:30:00 - 00:35:00

    The webinar covers the introduction of query cancellation, which improves performance by canceling in-process queries when users issue new actions, thus preventing overload and enhancing user experience.

  • 00:35:00 - 00:40:00

    Looking ahead to Q3, Dolly outlines upcoming features such as interactive table improvements, out-of-the-box relative date filters, and aggregate awareness, which will enhance data querying and reporting capabilities for users.

  • 00:40:00 - 00:45:00

    Dolly addresses questions from attendees regarding new features and their compatibility with existing tools, confirming that many enhancements will be available soon and that user feedback is valued in the development process.

  • 00:45:00 - 00:50:00

    Bos Couti, CEO of Sustain 360, joins the webinar to discuss how Sustain 360 utilizes Good Data's platform to build a unified climate intelligence solution, emphasizing the importance of multi-tenancy and complex data integration for their analytics needs.

  • 00:50:00 - 00:55:40

    Couti explains Sustain 360's approach to measuring and reducing carbon footprints across various industries, showcasing how Good Data's analytics capabilities support their mission to provide actionable insights for climate change mitigation.

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思维导图

视频问答

  • What is Good Data AI?

    Good Data AI is the first AI product launched by Good Data, featuring smart search and an AI assistant for enhanced analytics.

  • What are the new features in Q2?

    New features include smart search, AI assistant, flexible layouts, dynamic text references, and accessibility improvements.

  • How does Sustain 360 use Good Data?

    Sustain 360 uses Good Data to build a unified climate intelligence platform, focusing on carbon footprint analysis and reporting.

  • What is the significance of multi-tenancy in Good Data?

    Multi-tenancy allows for secure data separation and role-based access, essential for applications like Sustain 360.

  • What upcoming features are planned for Q3?

    Upcoming features include table improvements, relative date filters, and aggregate awareness for better performance.

  • How can I stay updated on new features?

    Join the Good Data community on Slack for announcements on new features and updates.

  • What is the purpose of the audit log?

    The audit log tracks actions and changes within the platform, providing transparency and accountability.

  • What accessibility improvements are being made?

    Good Data is enhancing accessibility through keyboard navigation, screen reader integration, and accessible PDFs.

  • When is the next product update webinar?

    The next product update webinar is scheduled for September 17th.

  • What is the focus of the Sustain 360 demo?

    The demo focuses on how Sustain 360 leverages Good Data for climate intelligence and carbon footprint management.

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  • 00:00:05
    Hi everybody. I'm Ryan Dolly, VP of
  • 00:00:07
    product strategy here at Good Data and I
  • 00:00:10
    am excited to walk you through what we
  • 00:00:12
    have coming up on our good data product
  • 00:00:16
    journey inside Q2 features, the roadmap
  • 00:00:19
    and uh a great customer success story
  • 00:00:21
    we're happy to share with you at the end
  • 00:00:23
    of the show today. Uh so let's get
  • 00:00:26
    started. Um,
  • 00:00:30
    what are we going to go through? A new
  • 00:00:31
    feature update, a sneak peek at the Q3
  • 00:00:33
    roadmap, and a customer spotlight uh
  • 00:00:35
    with our with our with uh Sustain 360,
  • 00:00:39
    who is one of our great customers.
  • 00:00:41
    They're going to we're going to be doing
  • 00:00:42
    a live demo of their solution. It's
  • 00:00:44
    really cool. So, um so you're definitely
  • 00:00:46
    going to want to check it out. Um of
  • 00:00:48
    course, I'm Ryan Dolly, VP of product
  • 00:00:50
    strategy. I am joined today by Bos
  • 00:00:53
    Couti, the CEO of Sustain 360. Um, would
  • 00:00:57
    you like to say hi to everyone real
  • 00:00:58
    quick here? Bos,
  • 00:01:00
    hi Ryan. Thanks for the opportunity.
  • 00:01:02
    Great to meet everyone. Um, Bas, CEO and
  • 00:01:06
    founder and CEO of Sustain 360 and
  • 00:01:08
    welcome the opportunity to demonstrate
  • 00:01:10
    how we're using good data technologies
  • 00:01:12
    to power our applications.
  • 00:01:15
    Awesome. Um, so I look forward to that.
  • 00:01:18
    This is going to be a really great demo.
  • 00:01:19
    Make sure you stick around to see it at
  • 00:01:20
    the end here. Uh, before we get to Bos
  • 00:01:23
    though, a couple points of order. First
  • 00:01:25
    of all, uh you attendees are all muted
  • 00:01:28
    upon entry. There is a Zoom Q&A box. So,
  • 00:01:32
    if you use the Zoom Q&A box, you can get
  • 00:01:35
    questions in and we will try to answer
  • 00:01:36
    the questions during the course of the
  • 00:01:38
    webinar. Anything we can't get to, of
  • 00:01:40
    course, we will answer after the fact
  • 00:01:42
    via email. And we will be sending out a
  • 00:01:44
    follow-up email with a recording of this
  • 00:01:46
    to anybody who registered or attended
  • 00:01:50
    the webinar. If this is your first time
  • 00:01:52
    joining us, of course, we are good data.
  • 00:01:54
    We are the analytics product platform
  • 00:01:55
    for when good enough is just not good
  • 00:01:57
    enough for your customers or users. Uh
  • 00:02:01
    we focus on simplicity, flexibility,
  • 00:02:03
    productivity and scalability all wrapped
  • 00:02:06
    in an analyticses code API first uh
  • 00:02:10
    super iterative scalable customizable
  • 00:02:13
    composable approach that will allow you
  • 00:02:15
    to build the analytics experience that
  • 00:02:17
    is exactly what you need, not just good
  • 00:02:20
    enough. So check us out at gooddata.com
  • 00:02:22
    to learn more there. All right, I'm very
  • 00:02:25
    excited uh for for this sake because we
  • 00:02:27
    have a lot of great stuff to show you.
  • 00:02:29
    So let's see what we're going to go
  • 00:02:32
    over. Um we're going to be giving an
  • 00:02:35
    overview of Good Data AI. Now, if you
  • 00:02:37
    don't know yet, Good Data AI is our our
  • 00:02:40
    uh first AI product. It launched a few
  • 00:02:44
    weeks ago. Very exciting. I'll be giving
  • 00:02:45
    a demo of that. Many of you were
  • 00:02:47
    involved in the beta. Uh uh but I'll be
  • 00:02:48
    showing you what's new uh in that
  • 00:02:50
    product. Even since launch, there's been
  • 00:02:52
    enhancements that have gone into it. Um
  • 00:02:54
    we're going to show dynamic text
  • 00:02:55
    references, uh updates to flexible
  • 00:02:57
    layouts, the audit lo the new audit log
  • 00:03:00
    that you have access to. We'll give you
  • 00:03:03
    an update on accessibility, talk about
  • 00:03:05
    query cancellation and what how that
  • 00:03:07
    impacts performance and then we will
  • 00:03:09
    jump into the road map and then bring B
  • 00:03:11
    on. So, with that said, let's get into
  • 00:03:16
    the demo. Here we go. So, um I'm going
  • 00:03:19
    to start from from here. Now, those of
  • 00:03:22
    you who are really familiar with good
  • 00:03:23
    data may already recognize some elements
  • 00:03:25
    on this screen that are different uh
  • 00:03:27
    from what you're used to as far as
  • 00:03:28
    layouts and and some of you know the
  • 00:03:30
    text on there. And I will get to those
  • 00:03:33
    later. What I want to start with is the
  • 00:03:36
    AI features that we recently launched.
  • 00:03:38
    So, our AI features come in two flavors.
  • 00:03:41
    There's what we call smart search, and
  • 00:03:43
    then there's what we call the AI
  • 00:03:44
    assistant. You have access to both of
  • 00:03:46
    these here at the top of the screen. Um,
  • 00:03:50
    and everything I'm going to show you in
  • 00:03:51
    both smart search and the AI assistant
  • 00:03:53
    is available through our UI. It's an
  • 00:03:56
    embeddible component that you can put
  • 00:03:59
    into your UI and it's available via an
  • 00:04:02
    API level integration. So if you want to
  • 00:04:04
    do a you know direct integration between
  • 00:04:07
    your applications and your own custom
  • 00:04:08
    front end you don't want to use our UI
  • 00:04:10
    elements at all but you want to use this
  • 00:04:12
    core tech that's available too just like
  • 00:04:14
    with everything else on our platform.
  • 00:04:17
    Looking at smart search what smart
  • 00:04:18
    search is is it is a semanticdriven
  • 00:04:21
    search. So it is there to just provide
  • 00:04:23
    the quickest possible path for your
  • 00:04:26
    users and customers to get to
  • 00:04:27
    pre-existing content by searching here.
  • 00:04:30
    Um so you can see um if I go ahead and
  • 00:04:34
    search for something like like revenue
  • 00:04:38
    um not product if revenue there we go um
  • 00:04:44
    what is going to happen
  • 00:04:48
    just click the back button there
  • 00:04:50
    accidentally uh fat fingered my mouse on
  • 00:04:52
    that search all right if you go to
  • 00:04:54
    search for revenue you'll see I get all
  • 00:04:57
    of these results and and the just the
  • 00:04:58
    important thing to highlight right, is
  • 00:05:00
    that it's it's not doing a dumb name
  • 00:05:03
    match, right? It is actually looking at
  • 00:05:05
    the the context of the objects that
  • 00:05:09
    exist in good data as well as the um
  • 00:05:12
    metadata you provide for it. So the
  • 00:05:14
    information you provide in the
  • 00:05:15
    descriptions in order to do this
  • 00:05:17
    matching. So I type in revenue. We don't
  • 00:05:19
    actually have any metrics or anything
  • 00:05:22
    called revenue in this data set, but it
  • 00:05:24
    knows that someone who's searching for
  • 00:05:26
    revenue is probably interested in
  • 00:05:28
    something like gross profit. And so you
  • 00:05:30
    see it it it um surfaces all of these
  • 00:05:32
    gross profit items first. Now, what are
  • 00:05:35
    these? Right? You can see it's surfacing
  • 00:05:37
    visualizations.
  • 00:05:38
    Uh it's surfacing metrics uh and and
  • 00:05:41
    that sort of thing. So it it's not just
  • 00:05:43
    a viz search or a dashboard search. it
  • 00:05:45
    will search through every level of
  • 00:05:47
    content that we have within good data
  • 00:05:49
    and surface all of that based on the
  • 00:05:50
    search term that you put in. Um, this is
  • 00:05:53
    one of those things, you know, the more
  • 00:05:54
    data you provide it, the the better the
  • 00:05:56
    descriptions, especially for your your
  • 00:05:58
    kind of term of art, your industry
  • 00:06:00
    specific terms, you're going to want to
  • 00:06:01
    put that in the descriptions of your
  • 00:06:02
    metrics. And suddenly this smart search
  • 00:06:04
    is going to be able to find things even
  • 00:06:06
    when people don't know the right term
  • 00:06:07
    because you'll say hey we call it you
  • 00:06:09
    know we we call it something else here
  • 00:06:11
    not revenue but when you know when they
  • 00:06:13
    type in revenue I want you to surface
  • 00:06:15
    this this uh object instead and it will
  • 00:06:17
    be able to do that. Um so again that is
  • 00:06:19
    just about the quickest possible path to
  • 00:06:21
    finding pre-existing content. Now we
  • 00:06:25
    also launched the AI assistant and here
  • 00:06:28
    you can see the AI assistant. The AI
  • 00:06:30
    assistant is a a chatbot that gets you
  • 00:06:34
    can both find existing content and
  • 00:06:36
    author new content through the same chat
  • 00:06:38
    window. Um, and again, this is available
  • 00:06:41
    in this UI. It's embeddible in other
  • 00:06:43
    UIs. Uh, and it's available via API
  • 00:06:45
    integration. So, let's look at how this
  • 00:06:47
    works. Uh let's imagine I've come in
  • 00:06:50
    I've looked at this dashboard and this
  • 00:06:52
    is great but I really need to do an
  • 00:06:54
    analysis of um our customers based on
  • 00:06:58
    like I'm trying to do an analysis of uh
  • 00:07:00
    customer age right how long have they
  • 00:07:01
    been a customer and what impact does
  • 00:07:03
    that have on the total order volume that
  • 00:07:05
    they do through us now traditionally I
  • 00:07:08
    would have to know how to use the the
  • 00:07:09
    drag and drop analytics designer to be
  • 00:07:11
    able to find that but now I can click on
  • 00:07:13
    this button and it pops up in the AI
  • 00:07:16
    assistant and um you can Start by just
  • 00:07:19
    saying uh something like uh do you have
  • 00:07:21
    any data about sales? Wh you can get
  • 00:07:25
    your fingers on the co home row keys. Do
  • 00:07:28
    you have any data about sales or
  • 00:07:31
    revenue? And see what it comes back
  • 00:07:34
    with. And um what it what we're looking
  • 00:07:37
    to see here is that it's it's going to
  • 00:07:41
    show us um a list of the content. It and
  • 00:07:45
    it didn't, of course. Um uh but what it
  • 00:07:48
    did do so what I was hoping to see there
  • 00:07:51
    was it would show us a list of the
  • 00:07:52
    content that actually exists um in this
  • 00:07:55
    workspace. Now the thing with AI of
  • 00:07:56
    course is it's non-deterministic and
  • 00:07:58
    that means you get a non-deterministic
  • 00:08:00
    demo right um in this case it took my
  • 00:08:02
    question and it interpreted it said well
  • 00:08:04
    listen I can give you some information
  • 00:08:06
    about that um total sales and net sales
  • 00:08:09
    in the form of of this um visualization
  • 00:08:13
    right so so you can type in a general
  • 00:08:15
    term like hey I just want to know
  • 00:08:16
    something about net sales and revenue
  • 00:08:18
    and it can suggest to you pre-existing
  • 00:08:19
    content or author new content like what
  • 00:08:22
    we saw here now in our case we have
  • 00:08:24
    something really specific we want to
  • 00:08:25
    know and that has to do with um with the
  • 00:08:29
    length of time that that customers have
  • 00:08:31
    been here. So um we can go ahead and say
  • 00:08:35
    uh show total
  • 00:08:37
    sales by and then you'll see as I start
  • 00:08:40
    typing customer um it's going to pop
  • 00:08:44
    open with this um this um this type
  • 00:08:47
    ahead essentially. So, I've typed in
  • 00:08:49
    enough for it to identify, hey, there
  • 00:08:51
    are attributes and metrics in this data
  • 00:08:54
    set that meet the the term that that
  • 00:08:57
    someone's typing in and and I will just
  • 00:09:00
    suggest to them, I meaning the AI, will
  • 00:09:02
    suggest to them those terms um to make
  • 00:09:05
    the the query more accurate, right? So,
  • 00:09:08
    so when you do this, you're no longer
  • 00:09:10
    relying on the AI to interpret what
  • 00:09:12
    field you want. You've you've really
  • 00:09:14
    said, you know, I want total sales by
  • 00:09:16
    this field, customer age. And so it
  • 00:09:18
    knows specifically and exactly what
  • 00:09:20
    field to include in the query. So we'll
  • 00:09:22
    we'll hit that and um and now it's going
  • 00:09:27
    to generate for us a chart of total
  • 00:09:30
    sales by customer age. Um and we can see
  • 00:09:33
    that right? So it's broken down it's
  • 00:09:35
    bucketized these this these are uh
  • 00:09:37
    months right this 3M 4 to 6M 7M plus
  • 00:09:40
    right um is uh are the months that a
  • 00:09:44
    customer has been a customer of ours.
  • 00:09:46
    This is a data set that has one year's
  • 00:09:48
    worth of data. Um, and so, so that's
  • 00:09:52
    pretty great. Um, you know, I can and it
  • 00:09:54
    tells me what how it made it, the exact
  • 00:09:57
    metrics and attributes and filters that
  • 00:09:59
    it applied to make this visualization.
  • 00:10:01
    And it applies or provides some
  • 00:10:04
    suggestions of what I might want to do
  • 00:10:05
    next. So, it's in a column chart. It
  • 00:10:07
    knows one thing I might want to do is
  • 00:10:09
    switch it to a bar chart. And I do want
  • 00:10:10
    to do that. So, let's go ahead and make
  • 00:10:12
    that request of the AI assistant. And it
  • 00:10:15
    should come back here with uh a bar um a
  • 00:10:18
    bar chart instead of a column chart for
  • 00:10:21
    this visualization.
  • 00:10:22
    Um and so now it's uh it's you can see I
  • 00:10:27
    can start to make some changes to the
  • 00:10:30
    visualization type. I can also apply
  • 00:10:31
    slicers from here. So let's say slice by
  • 00:10:35
    customer city. And now it should take
  • 00:10:37
    customer city and add that as an element
  • 00:10:39
    to the visualization.
  • 00:10:41
    Um, and that will give me a review of,
  • 00:10:44
    you know, my bucketized customers, how
  • 00:10:46
    long they've been a customer, the total
  • 00:10:48
    sales for each customer age group, and
  • 00:10:51
    then also analyzed um by city. So, you
  • 00:10:54
    can see it did everything we wanted.
  • 00:10:57
    That's great. But now what? Uh, many AI
  • 00:11:00
    assistants are uh really struggle at
  • 00:11:03
    this point like like what do you do with
  • 00:11:05
    it? Okay, you got the answer. Now, what
  • 00:11:06
    do you do with it? Well, there's a
  • 00:11:08
    couple things you can do here within
  • 00:11:10
    good data. you can go ahead and directly
  • 00:11:13
    save the visualization.
  • 00:11:15
    So that it'll give me the opportunity to
  • 00:11:17
    rename it. I think that name is fine.
  • 00:11:19
    And now it's saved into the library of
  • 00:11:22
    visualizations that are available to me
  • 00:11:24
    as a user. The other thing I can do from
  • 00:11:27
    here is say um I want to open this in
  • 00:11:30
    analyze and this will take me into
  • 00:11:32
    analytics designer. And now I can I can
  • 00:11:35
    apply all of the skills I have as a
  • 00:11:39
    designer um tweaking this visualization
  • 00:11:42
    uh to make it look exactly the way I
  • 00:11:45
    want it. For example, you see when I
  • 00:11:46
    came in here, it applied a sort to it.
  • 00:11:49
    So now it's it's sorted um per city uh
  • 00:11:53
    you know in uh descending order. And so
  • 00:11:56
    um those are the types of things. Now I
  • 00:11:57
    can tweak it, do whatever I want to it
  • 00:11:59
    and then save it into the visualization
  • 00:12:01
    library. So um this is all available
  • 00:12:04
    today in good data. Um if you want to
  • 00:12:06
    check it out, go into your environment
  • 00:12:08
    um and you should have access to these
  • 00:12:11
    features. Now um the other things I want
  • 00:12:15
    to highlight here that I think uh are
  • 00:12:18
    really exciting that you have access to
  • 00:12:20
    now are um the ability for you to uh
  • 00:12:25
    let's start with what we call flexible
  • 00:12:27
    layouting. Now, this has been available
  • 00:12:29
    in the uh in um in an experimental state
  • 00:12:34
    for a while, but we're coming close to
  • 00:12:37
    wrapping this up. Uh and there going to
  • 00:12:38
    be a few more changes to it before it
  • 00:12:40
    goes fully into production. But just to
  • 00:12:42
    give you an overview of how this works,
  • 00:12:44
    you know, as you know in good data, um
  • 00:12:48
    it's quite you know, we have a kind of a
  • 00:12:50
    fixed uh layouting format based on rows
  • 00:12:54
    in our dashboards. So, um, if you want
  • 00:12:57
    to create a a dashboard,
  • 00:13:02
    um, like you can see here, um, maybe I
  • 00:13:04
    want to, you know, I want to build a
  • 00:13:05
    dashboard. So, I've got orders over
  • 00:13:07
    time. Uh, and then let's look for, um,
  • 00:13:12
    some KPIs.
  • 00:13:13
    So, you know, net sales. Let's add net
  • 00:13:17
    sales. And let's add, um, maybe a
  • 00:13:20
    customer related KPI
  • 00:13:24
    to this as well.
  • 00:13:26
    um
  • 00:13:27
    let's do total customers. Okay. Now, the
  • 00:13:32
    way that uh you most commonly would want
  • 00:13:35
    to arrange these um it's just we don't
  • 00:13:38
    you know good data traditionally doesn't
  • 00:13:40
    offer you a ton of options for how to
  • 00:13:42
    arrange these. And that's where this
  • 00:13:44
    container feature comes in. So, um what
  • 00:13:48
    I'm going to do here is drop the
  • 00:13:50
    container in next to order over time.
  • 00:13:52
    And then I can add my net sales and my
  • 00:13:56
    total customers into that container. And
  • 00:13:59
    maybe, you know, because I have the
  • 00:14:00
    space, I can add um let's add uh
  • 00:14:05
    let's add this net sales. This is a good
  • 00:14:07
    one. Net sales by product.
  • 00:14:11
    Yeah, net sales by product category. Um
  • 00:14:13
    so let's drop that into this container
  • 00:14:16
    too. Um, and so then you can see I have
  • 00:14:19
    net sales by product and I can arrange
  • 00:14:21
    these to be a little more compact. And
  • 00:14:24
    let's make this a little bigger. And you
  • 00:14:27
    know, now I can do some layouting that
  • 00:14:30
    I've never been able to do in the past.
  • 00:14:32
    Like I said, this has been in kind of an
  • 00:14:33
    experimental state. You can turn it on
  • 00:14:35
    in your environment now and have access
  • 00:14:37
    to this now. And I know many of you
  • 00:14:38
    have. What the changes that are coming
  • 00:14:41
    is um this container object. We're going
  • 00:14:43
    to make it be able to operate so that
  • 00:14:45
    you have some control when you add new
  • 00:14:47
    objects to it. Do you do you basically
  • 00:14:49
    do you want it to be a column container
  • 00:14:51
    where the new objects uh go in
  • 00:14:53
    vertically or do you want it to be a row
  • 00:14:56
    style container where the new objects go
  • 00:14:57
    in horizontally? Um the other thing I
  • 00:15:00
    would add is is you can nest these
  • 00:15:01
    within one another. So it's it's really
  • 00:15:04
    kind of you can get to some very complex
  • 00:15:06
    layouts now that you couldn't in the
  • 00:15:07
    past by using these containers. Um and
  • 00:15:10
    and this should be coming into the
  • 00:15:12
    product like full-blown in production,
  • 00:15:14
    no longer an exper experimental feature
  • 00:15:17
    in the next few weeks.
  • 00:15:19
    Um the next thing I would like to
  • 00:15:21
    highlight here is that we uh the text
  • 00:15:25
    dynamic text references dynamic text
  • 00:15:28
    references are really powerful feature.
  • 00:15:30
    So um let's drop a container in here and
  • 00:15:35
    we'll expand this.
  • 00:15:38
    drop this into the container. Okay. Now,
  • 00:15:42
    um, of course, we've had this rich text
  • 00:15:45
    object for a while now. Um, as you are,
  • 00:15:49
    uh, probably aware.
  • 00:15:51
    Let's move this to the
  • 00:15:54
    Where did it go? Let's move this to the
  • 00:15:56
    front.
  • 00:15:58
    No, I want it on top, please.
  • 00:16:06
    Okay, let's try this again.
  • 00:16:11
    Let's delete this container. There we
  • 00:16:13
    go. Um,
  • 00:16:16
    and add this column container in here.
  • 00:16:20
    Let's expand it.
  • 00:16:23
    Get this into it. And now let's add our
  • 00:16:25
    rich text there to the top where I
  • 00:16:29
    wanted it. So, you've been able to put
  • 00:16:31
    in text and images and links and and
  • 00:16:33
    that sort of thing. Uh, but now you're
  • 00:16:35
    able to put in these um what we call um
  • 00:16:41
    of metric or attribute references. So,
  • 00:16:44
    I'm just going to paste this in. Um, and
  • 00:16:46
    you can see here the the uh syntax that
  • 00:16:50
    you need for these references. So, it's,
  • 00:16:52
    you know, metric slash and then the name
  • 00:16:54
    of the metric, right? And the same thing
  • 00:16:56
    for attribute. Attribute slash and the
  • 00:16:58
    name of the attribute. And what that
  • 00:17:00
    will do is it will in real time when you
  • 00:17:05
    uh execute a query when you open a
  • 00:17:07
    dashboard it will fetch data values and
  • 00:17:11
    fill fill them in to your text um at
  • 00:17:14
    runtime. And these data values respect
  • 00:17:17
    filters and all that sort of thing. So,
  • 00:17:19
    um it's a really powerful way to
  • 00:17:21
    communicate
  • 00:17:23
    um to communicate
  • 00:17:25
    data and insights to your customers
  • 00:17:29
    using text. You can also do this and I
  • 00:17:32
    thought this was very cool. You can also
  • 00:17:35
    um put this in the descriptions of
  • 00:17:36
    objects. Right? So, you'll see here that
  • 00:17:40
    um I now have this in the description of
  • 00:17:43
    this chart. And if I save this,
  • 00:17:46
    when my end user is viewing this,
  • 00:17:50
    they can hover over the question mark
  • 00:17:54
    and they will get that dynamic text
  • 00:17:56
    reference within the description of the
  • 00:17:58
    object. So you can also use those if if
  • 00:18:00
    you don't want to put this dynamic text,
  • 00:18:02
    you know, data description onto the
  • 00:18:04
    dashboard itself, you can use it in the
  • 00:18:06
    description of the object so that when
  • 00:18:08
    someone ho hovers over it, they can get
  • 00:18:10
    some additional details, additional data
  • 00:18:13
    that maybe didn't make sense to include
  • 00:18:15
    in the visualization, but is good
  • 00:18:17
    context for your user to know.
  • 00:18:20
    Um, the final thing I want to highlight
  • 00:18:22
    in changes, um, and this actually went
  • 00:18:23
    in in September, but I suspect a lot of
  • 00:18:25
    you are not aware of it, is that we have
  • 00:18:28
    now the ability to create saved views.
  • 00:18:32
    So, um, let me go ahead. I'll call this,
  • 00:18:35
    say, uh, month view
  • 00:18:38
    and save it. And now I'm going to apply
  • 00:18:42
    a filter state
  • 00:18:45
    to say shift it to this year.
  • 00:18:49
    And you'll notice um all of this
  • 00:18:51
    updated, right? So everything on the
  • 00:18:53
    screen updated uh including my text
  • 00:18:55
    references. These two updated as well,
  • 00:18:59
    right? And now I'm going to create a new
  • 00:19:01
    view and call this year view. And we'll
  • 00:19:06
    save that as well. So now anytime I come
  • 00:19:09
    into this dashboard, I as an individual
  • 00:19:11
    can can switch automatically, not
  • 00:19:14
    automatically, but I can come in and
  • 00:19:15
    switch between my filter states simply
  • 00:19:18
    by clicking uh the button. I can swap
  • 00:19:20
    between month view and year view. I can
  • 00:19:22
    add as many filter states as I want. Um,
  • 00:19:25
    of course, we also have the ability for
  • 00:19:27
    you to where it will just remember your
  • 00:19:30
    last state as a user where it will
  • 00:19:31
    always default to the last state that
  • 00:19:33
    you as an individual left this dashboard
  • 00:19:35
    in. But this is kind of the next level
  • 00:19:37
    of that. If you have, you know, 10
  • 00:19:39
    regions and you want to look at all 10
  • 00:19:41
    regions every Monday, you want to come
  • 00:19:43
    in and look at all 10 regions. It's
  • 00:19:44
    simple. You save your filter states and
  • 00:19:46
    then you can come through and just click
  • 00:19:48
    through the views uh and and you're able
  • 00:19:50
    to do that. So those are the changes um
  • 00:19:53
    that are in production today or
  • 00:19:56
    available as a very latestage
  • 00:19:58
    experimental feature that you have
  • 00:20:00
    access to that I think you should you
  • 00:20:02
    should run back to your desk after this
  • 00:20:04
    and check them out. Now, uh what else do
  • 00:20:08
    we have available in the product live
  • 00:20:11
    now um that we've recently launched that
  • 00:20:14
    is harder harder to see? Um the first
  • 00:20:17
    one and we know this is a huge request
  • 00:20:19
    that we get is the audit log. People
  • 00:20:21
    want an audit log. Well, it's now
  • 00:20:23
    available. Um so the audit log, the way
  • 00:20:25
    it works, it will deliver to an S3
  • 00:20:27
    bucket that you own every 10 minutes. Um
  • 00:20:31
    so you have to set up an S3 bucket. uh
  • 00:20:33
    you give us the information, you you
  • 00:20:35
    contact support and say, "Hey, I'd like
  • 00:20:36
    to turn on the audit log. This is where
  • 00:20:38
    I want it sent. This is the bucket." Um
  • 00:20:42
    and uh
  • 00:20:44
    um
  • 00:20:46
    and then you can um and then we will
  • 00:20:49
    deliver the the um audit log to that
  • 00:20:53
    bucket. It's a complete list of actions.
  • 00:20:55
    So, it's really robust. If you go into
  • 00:20:58
    our documentation, you can see the full
  • 00:20:59
    list of actions, but it's all the
  • 00:21:01
    content, what people did on the content,
  • 00:21:03
    security changes, administrative
  • 00:21:04
    changes. It's all of that kind of stuff
  • 00:21:07
    um that that you have uh that you can
  • 00:21:10
    see in there and it's available today to
  • 00:21:12
    customers on the enterprise tier. Um
  • 00:21:16
    next, we want to give you an update on
  • 00:21:17
    accessibility. So uh we are doing a huge
  • 00:21:21
    accessibility project to make sure that
  • 00:21:24
    you have access to that your customers
  • 00:21:27
    have access to accessible analytics. Um
  • 00:21:30
    and we're in mid-flight. So what we've
  • 00:21:33
    done so far includes keyboard
  • 00:21:35
    navigation, improved metadata on the
  • 00:21:37
    objects, visualization enhancements to
  • 00:21:39
    make them easier to read. Um we're
  • 00:21:41
    midway through screen reader uh screen
  • 00:21:44
    reader integration. We'll be adding more
  • 00:21:46
    keyboard navigation steps in the future.
  • 00:21:48
    And then we'll also be producing uh
  • 00:21:51
    accessible PDFs and an accessible mobile
  • 00:21:54
    experience by the end of the year. We
  • 00:21:56
    are going to have a full-blown
  • 00:21:58
    thirdparty audit of our accessibility
  • 00:22:00
    that we can make available to you. Um
  • 00:22:02
    and uh and so if this is something that
  • 00:22:05
    that you need for your customers and
  • 00:22:07
    users and and it really is, this is this
  • 00:22:10
    is becoming more and more important as
  • 00:22:11
    time goes on. um not the least which
  • 00:22:14
    because of of regulatory changes that we
  • 00:22:17
    see coming on board in the EU um we will
  • 00:22:20
    are committed to being the most
  • 00:22:21
    accessive accessible analytics platform
  • 00:22:24
    in the world. So um so that's the where
  • 00:22:27
    we are on that journey
  • 00:22:29
    and then finally we went into production
  • 00:22:31
    with with query cancellation. So what
  • 00:22:34
    this does is it it cancels in process
  • 00:22:36
    queries when the user issues a new
  • 00:22:38
    action. If the user does something and
  • 00:22:40
    they're watching the wheel spin and then
  • 00:22:43
    they do something new, right? Um rather
  • 00:22:45
    than allowing that query to finish on
  • 00:22:47
    the database, we go ahead and
  • 00:22:48
    proactively we now proactively cancel
  • 00:22:51
    that query, you can see in our our
  • 00:22:53
    little chart here, right? The user takes
  • 00:22:55
    all these actions, but they, you know,
  • 00:22:58
    action one, they take action two before
  • 00:23:00
    the query for action one is finished. It
  • 00:23:02
    makes the query for action one
  • 00:23:04
    irrelevant. So we we cancel it, we
  • 00:23:06
    trigger its cancellation within the
  • 00:23:08
    database. This prevents query overload
  • 00:23:10
    and improves the performance uh for your
  • 00:23:13
    end users particularly when they're when
  • 00:23:15
    they're um really rapidly iterating
  • 00:23:18
    through filter sets and that sort of
  • 00:23:20
    thing. And so this also is available
  • 00:23:22
    live and available to all customers
  • 00:23:25
    today.
  • 00:23:28
    All right, so that's what you can see
  • 00:23:30
    now, what we've delivered in Q2. Let's
  • 00:23:33
    talk about what's coming next. Um so the
  • 00:23:36
    first thing I want to highlight is table
  • 00:23:38
    improvements. We are working on a new
  • 00:23:40
    interactive table object in analytics
  • 00:23:42
    designer and dashboards. This object has
  • 00:23:44
    a lot of the interactive capabilities
  • 00:23:46
    that you have requested. Um so that
  • 00:23:49
    means you can group and collapse rows.
  • 00:23:51
    You can do multicolumn sorts. It offers
  • 00:23:54
    in table aggregations
  • 00:23:56
    um and summaries. Um, it also includes,
  • 00:23:58
    and I know this is a huge request from
  • 00:24:01
    some of you who really need this, is the
  • 00:24:03
    ability uh to turn on and off text
  • 00:24:06
    wrapping within table and pivot table
  • 00:24:08
    objects and the ability to easily copy
  • 00:24:11
    text out of our tables and pivot tables.
  • 00:24:13
    Um, so this is going to be a total
  • 00:24:15
    revamp of how we do these objects within
  • 00:24:18
    good data. They are going to be much
  • 00:24:19
    more interactive for your end users
  • 00:24:22
    where your end users can can do all
  • 00:24:25
    sorts of stuff
  • 00:24:27
    that that you know uh in order to filter
  • 00:24:30
    um sort uh group uh and that sort of
  • 00:24:34
    thing directly within the table uh
  • 00:24:37
    objects themselves. Um so uh that is uh
  • 00:24:43
    another huge thing that you can look
  • 00:24:44
    forward to coming up. Uh and then
  • 00:24:46
    relative date filters out of the box. Of
  • 00:24:49
    course, you've always been able to
  • 00:24:50
    calculate relative dates with um with
  • 00:24:53
    MWOL and and create relative date
  • 00:24:55
    metrics, but now we will provide these
  • 00:24:58
    to you out of the box. So, year-to-
  • 00:25:00
    date, quarter to date, monthto date,
  • 00:25:01
    week to date will all be default filter
  • 00:25:03
    selections for all of your users uh
  • 00:25:06
    without you having to do any extra
  • 00:25:07
    modeling to provide it. Um and that will
  • 00:25:09
    be coming in Q3.
  • 00:25:13
    Um so uh the final thing that we want to
  • 00:25:17
    highlight that's coming up in Q3 and I I
  • 00:25:20
    see we have some questions. I will get
  • 00:25:21
    to the Q&A um right after this um is uh
  • 00:25:25
    aggregate awareness. So this is a design
  • 00:25:28
    pattern where you might have say a
  • 00:25:31
    detail fact table and then a day level
  • 00:25:34
    fact table and a month level fact table
  • 00:25:36
    within your data warehouse. um we will
  • 00:25:40
    be able to intelligently select the
  • 00:25:42
    correct fact table based on the usage of
  • 00:25:48
    your uh based on the the user input. So
  • 00:25:51
    when the user is looking at things at a
  • 00:25:53
    month level and then they drill down
  • 00:25:56
    into a day level, we will intelligently
  • 00:25:58
    switch from the month level table that
  • 00:26:00
    you've prepared to the day level table
  • 00:26:02
    that you've prepared. This can
  • 00:26:04
    dramatically increase performance
  • 00:26:06
    especially in those situations where
  • 00:26:08
    where your users do like to do those
  • 00:26:10
    types of of filters where they look at
  • 00:26:13
    different um different grains of data,
  • 00:26:15
    right? Um so this is coming uh in in uh
  • 00:26:19
    Q3 and um it will be configurable via
  • 00:26:23
    the SDK. So you're going to need to
  • 00:26:25
    provide uh two good data via the SDK.
  • 00:26:29
    these um these multi-grain level or
  • 00:26:32
    aggregate level relationships between
  • 00:26:35
    your tables and then our query engine
  • 00:26:37
    will just take them into account whether
  • 00:26:39
    you're running a pre-existing dashboard
  • 00:26:40
    or building visualizations. Nobody has
  • 00:26:43
    to know this. You don't have to expose
  • 00:26:45
    these different you know detail level
  • 00:26:47
    tables to your end users. It will look
  • 00:26:49
    like one table to your end users, but in
  • 00:26:51
    reality, it's three tables in this
  • 00:26:53
    example under the hood. And we
  • 00:26:55
    intelligently select the right one based
  • 00:26:57
    on user input to give the best possible
  • 00:26:59
    performance.
  • 00:27:00
    Woo. All right. So, um, let me take a
  • 00:27:03
    couple questions here and then we're
  • 00:27:04
    going to move into the road map and
  • 00:27:06
    we'll invite Bos to join us. So, um,
  • 00:27:09
    Tomas asks, "Can I drop containers into
  • 00:27:11
    containers?" Um, I think I addressed
  • 00:27:13
    that, but, uh, yes, Tomas, you can. Um,
  • 00:27:16
    so that's really great. Um, we have an
  • 00:27:18
    anonymous attendee asked, "Are there
  • 00:27:20
    plans for an official good data MCP
  • 00:27:22
    server?" Stay tuned. We will have an
  • 00:27:24
    announcement in July. I'm not going to
  • 00:27:25
    say any more than that. Um, when
  • 00:27:28
    flexible layouts are generally
  • 00:27:29
    available, will they also be compatible
  • 00:27:31
    with Analytics's code? Um, I believe the
  • 00:27:34
    answer is yes. Uh, like 95% certain. If
  • 00:27:38
    they're not immediately usable with
  • 00:27:41
    Analytics's code, I'm sure they will be
  • 00:27:42
    in short order, but we we can get a 100%
  • 00:27:45
    answer on that for you. Um Tomas asks uh
  • 00:27:49
    is it possible to use dynamic text in
  • 00:27:51
    tool tips? Yes, it is possible to use
  • 00:27:55
    dynamic text uh in tool tips. Um yeah,
  • 00:27:59
    and then Tomas says freeze headers would
  • 00:28:00
    be uh amazing and big thumbs up for
  • 00:28:03
    aggregate awareness. Thank you Tomas for
  • 00:28:05
    all the great questions and the thumbs
  • 00:28:06
    up. Um
  • 00:28:09
    uh and if you have any more questions of
  • 00:28:11
    course um get them in and I am happy to
  • 00:28:13
    answer them. All right. So, what's
  • 00:28:16
    what's the longerterm road map look
  • 00:28:17
    like? Well, you know, we have kind of
  • 00:28:19
    three layers to good data. We have good
  • 00:28:21
    data BI, good data AI, and good data
  • 00:28:23
    analytics lake. As far as our analytics
  • 00:28:25
    lake, what we're looking at here is all
  • 00:28:28
    about um in in the first half of 2025,
  • 00:28:31
    improving query performance and putting
  • 00:28:33
    into into place aggregate awareness. Um,
  • 00:28:36
    and that aggregate awareness uh should
  • 00:28:38
    be shipping in the next few weeks uh or
  • 00:28:41
    month, I would say. After that, what
  • 00:28:43
    we're looking at is caching strategies,
  • 00:28:46
    right? So, um the cache right now is
  • 00:28:48
    very manual. You have to set when you
  • 00:28:50
    want the cache to uh um to uh you when
  • 00:28:55
    you want the cache to empty. Um we're
  • 00:28:57
    going to be using um giving you the
  • 00:28:59
    ability to have much more uh use case
  • 00:29:03
    specific ability to do that at the level
  • 00:29:05
    of data sets and workspaces and not just
  • 00:29:07
    your full data model like you have
  • 00:29:08
    today. Um we will be providing native
  • 00:29:12
    integration with iceberg and uh and um
  • 00:29:15
    star rocks which is a really powerful
  • 00:29:17
    MPP OLAP engine um so that you can house
  • 00:29:22
    all your data with good data in the most
  • 00:29:24
    modern in the most modern well-adopted
  • 00:29:28
    blazingly fast technology that exists.
  • 00:29:30
    So for those of you who use us for that,
  • 00:29:33
    for those of you who are on good data
  • 00:29:34
    platform and you use us as your
  • 00:29:36
    endto-end analytics provider, your data
  • 00:29:38
    warehouse, your transformation and your
  • 00:29:39
    BI and AI, you we will be able to
  • 00:29:42
    provide all of that going forward. But
  • 00:29:44
    you should expect um you know much much
  • 00:29:46
    better performance uh and that sort of
  • 00:29:48
    thing because um we are integrating
  • 00:29:50
    these bestand breed technologies into
  • 00:29:52
    that. Um and then in the long term AI
  • 00:29:56
    controlled caching. So you know
  • 00:29:58
    intelligently pre-caching the the uh the
  • 00:30:02
    the cache based on your usage statistics
  • 00:30:05
    of your customers. Um and then finally
  • 00:30:07
    providing uh full data marts a
  • 00:30:09
    multi-tenant data mart experience where
  • 00:30:11
    each workspace has its own data mart
  • 00:30:13
    with just the data it needs for that
  • 00:30:15
    particular customer um or user group so
  • 00:30:18
    that you have the best possible
  • 00:30:20
    performance and ironclad security
  • 00:30:22
    because there's no data mingling.
  • 00:30:25
    Um as far as AI is concerned there you
  • 00:30:28
    go MCP support on the on the AI roadmap.
  • 00:30:31
    Um so yes there will be MCP support. um
  • 00:30:34
    you're going to be looking at uh uh that
  • 00:30:37
    coming on board on uh online shortly.
  • 00:30:41
    And then um we're going to be doing more
  • 00:30:44
    with uh with the actual data. So um
  • 00:30:47
    Genaii data stories and situations where
  • 00:30:49
    you give us access to the data uh our AI
  • 00:30:52
    access to the data. We'll be able to
  • 00:30:54
    generate narratives um and that sort of
  • 00:30:56
    thing based on on the data. Um of course
  • 00:31:00
    it's the year of the agent. So setting
  • 00:31:02
    up uh agentic processes within good
  • 00:31:04
    data. Um and then finally you know in
  • 00:31:06
    2026 what we call perceptive analytics
  • 00:31:09
    always on autonomous data analytics that
  • 00:31:12
    are are automatically detecting data
  • 00:31:15
    situations that your customers should
  • 00:31:17
    know about. Um and then finally user
  • 00:31:19
    specific analytics content um that that
  • 00:31:22
    takes into account that individual users
  • 00:31:25
    preferences and and revealed preferences
  • 00:31:27
    from their usage patterns. And then
  • 00:31:29
    finally, you know, let's not forget BI.
  • 00:31:31
    Um, you've seen what we have today. U
  • 00:31:36
    coming shortly, we're going to be
  • 00:31:37
    bringing the ascode experience into the
  • 00:31:39
    web portal to allow you to to access the
  • 00:31:42
    code directly from the good data UI, not
  • 00:31:46
    having to use some some thirdparty IDE
  • 00:31:48
    in order to access it. Table
  • 00:31:49
    improvements, we showed you, of course,
  • 00:31:51
    geo charts and geo analytics. We're be
  • 00:31:54
    beginning a big sprint on this to give
  • 00:31:56
    you uh completely replace the charting
  • 00:31:58
    experience with good data in good data
  • 00:31:59
    with something much more modern uh and
  • 00:32:01
    much more flexible. And then in the long
  • 00:32:03
    term UI composability we're really
  • 00:32:06
    committed to making the UI as composable
  • 00:32:08
    as possible to support your engineers to
  • 00:32:10
    give you that just so experience where
  • 00:32:13
    you can build you know something like I
  • 00:32:14
    said at the beginning that's not good
  • 00:32:15
    enough you know but that really meets m
  • 00:32:18
    matches your use case perfectly and this
  • 00:32:19
    composability is a big part of that
  • 00:32:21
    story. And then finally, um, giving you
  • 00:32:23
    the ability to manage all of the
  • 00:32:26
    metadata within the good data UI itself.
  • 00:32:28
    That's going to be really important for
  • 00:32:29
    AI in particular that you have to have
  • 00:32:31
    great metadata if you want great great
  • 00:32:33
    AI. Today, you manage metadata almost
  • 00:32:36
    exclusively through API and SDK. We will
  • 00:32:39
    be bringing that experience into the UI
  • 00:32:40
    as well. Okay. Woo. Okay. Um, Corey,
  • 00:32:44
    good to see you here, Corey. Um, custom
  • 00:32:45
    height and width possible for and within
  • 00:32:48
    the containers. Um, so I wouldn't like
  • 00:32:50
    not at the pixel level. Um, but you can
  • 00:32:54
    within the containers, you know, change
  • 00:32:56
    the the height and width of things, but
  • 00:32:58
    it's kind of like a step change. Not not
  • 00:33:00
    at not to the the degree of it like, you
  • 00:33:04
    know, I want this five pixels wider,
  • 00:33:05
    right? Um, but that's something we can
  • 00:33:07
    look at if it's something your team
  • 00:33:08
    needs. So, let us know.
  • 00:33:11
    All right. With that said, uh I'm going
  • 00:33:14
    to invite Bos on to share with us um
  • 00:33:17
    what it is that they're doing at Sustain
  • 00:33:19
    360 with good data. Bos, welcome back to
  • 00:33:23
    the uh welcome back to the show. Okay,
  • 00:33:27
    thanks Ryan. Appreciate the opportunity.
  • 00:33:30
    And Basy, the founder and CEO of Sustain
  • 00:33:33
    360. Um the the if you go through the
  • 00:33:36
    slides here, the software essentially is
  • 00:33:39
    designed to build a unified climate
  • 00:33:42
    platform. I don't know if you're going
  • 00:33:43
    to flip the slides for me this time. Oh
  • 00:33:46
    yeah. Oh, sorry. I got to reshare them.
  • 00:33:48
    My bad.
  • 00:33:50
    There we go. Yeah. Just move on. Yep. So
  • 00:33:54
    basically who are we? We're a SAS
  • 00:33:56
    company focused on climate intelligence
  • 00:33:59
    and what we're building is a unified
  • 00:34:01
    climate intelligence platform. What
  • 00:34:03
    traditionally clients have done is built
  • 00:34:06
    siloed solutions or bought silo
  • 00:34:08
    products. Um but as we go more and more
  • 00:34:11
    into what we call climate economy in the
  • 00:34:13
    future then we need a a solution which
  • 00:34:16
    addresses that. So what sustain 360 does
  • 00:34:20
    is uses the power of data analytics and
  • 00:34:23
    AI uh with a common data model built on
  • 00:34:26
    good data underneath this which brings a
  • 00:34:29
    holistic approach to uh answering the
  • 00:34:32
    following questions for our clients. So
  • 00:34:33
    firstly the client's trying to
  • 00:34:34
    understand across the enterprise. So it
  • 00:34:37
    could be a major corporation with
  • 00:34:39
    hundreds and thousands of plants around
  • 00:34:42
    the world. Um what is my carbon
  • 00:34:44
    footprint? And that is not an easy thing
  • 00:34:47
    to go and do. You can maybe do it at a
  • 00:34:49
    plant level or a product level. But when
  • 00:34:51
    you're doing it at an enterprise level
  • 00:34:53
    with you know 30 50,000 products how do
  • 00:34:57
    you do that? So that's kind of what's my
  • 00:34:59
    carbon footprint? Well, once you know
  • 00:35:01
    your carbon footprint, then what are you
  • 00:35:04
    going to do about it? How do I reduce
  • 00:35:05
    it? How do I now take action to now
  • 00:35:08
    reduce where changes in material,
  • 00:35:10
    changes in energy, changes in suppliers,
  • 00:35:14
    supply chain networks, you're going to
  • 00:35:15
    make some kind of degree at different
  • 00:35:17
    levels, at different plants at different
  • 00:35:19
    product levels. And so how does that all
  • 00:35:22
    all the changes you're going to make get
  • 00:35:24
    rolled up and understand the impacts of
  • 00:35:26
    that both from a uh environmental
  • 00:35:29
    perspective but more importantly from a
  • 00:35:31
    financial p perspective. So it has to be
  • 00:35:34
    cost justified and then while you're
  • 00:35:36
    doing that there is obviously continuous
  • 00:35:38
    climate change. So you got to assess the
  • 00:35:41
    risk to doing these changes if you're
  • 00:35:43
    changing your suppliers out. Well,
  • 00:35:45
    what's the current risk in terms of uh
  • 00:35:48
    supply chain risk in terms of climate to
  • 00:35:50
    change high rising sea levels and so on.
  • 00:35:53
    So that's what the risk side does uh
  • 00:35:56
    brings in that dimension and if you're
  • 00:35:59
    doing that then you're building
  • 00:36:00
    resiliency into your organization
  • 00:36:03
    because you're taking account the
  • 00:36:05
    changes in dynamics as you go forward uh
  • 00:36:08
    with your with your operations. So all
  • 00:36:10
    these things are coming together and so
  • 00:36:12
    we call that climate intelligence and to
  • 00:36:15
    do that we needed a platform. We needed
  • 00:36:17
    an analytics platform, a data platform
  • 00:36:20
    and AI together to do that. So just move
  • 00:36:24
    on. Um right
  • 00:36:29
    so what were our needs? Our needs kind
  • 00:36:31
    of as a as a analytics company SAS
  • 00:36:34
    company building climate intelligent our
  • 00:36:36
    needs kind of came down to these areas.
  • 00:36:38
    We absolutely had to have multi-tendency
  • 00:36:41
    from the beginning. It wasn't something
  • 00:36:43
    kind of afterthought. You cannot build
  • 00:36:45
    something and retrofit multi-tenency
  • 00:36:48
    just does not work. Um and with
  • 00:36:51
    role-based access down to a field level
  • 00:36:54
    and so um how do we build that out
  • 00:36:57
    natively? That was a key requirement.
  • 00:37:00
    the ability to fuse together numerous
  • 00:37:04
    different data sets which are highly
  • 00:37:07
    complex and interrelated had to be
  • 00:37:10
    created and so we needed a tool to go do
  • 00:37:13
    that. All the metrics we use have to
  • 00:37:15
    align to industry standards because of
  • 00:37:17
    regulatory needs. So we have to show
  • 00:37:20
    that the metric was calculated in this
  • 00:37:22
    particular method and the method was
  • 00:37:24
    aligned to this industry standard and as
  • 00:37:26
    we know standards change. So that has to
  • 00:37:29
    evolve as well. We have to make sure the
  • 00:37:31
    reporting was configuration and we could
  • 00:37:33
    build the analytics and dashboards as
  • 00:37:36
    code into our applications. And then we
  • 00:37:38
    were really looking for a platform which
  • 00:37:41
    was really a modern architecture. A lot
  • 00:37:42
    of what was out there from the
  • 00:37:44
    alternatives perspective was even some
  • 00:37:46
    of it was pre-cloud and what they had
  • 00:37:48
    done is a lift and shift or they'd
  • 00:37:51
    wrapped around various services and in
  • 00:37:55
    more recently wrapped around AI. So it
  • 00:37:58
    wasn't really native from the beginning.
  • 00:38:00
    So we're born in the cloud, we're born
  • 00:38:01
    in in AI and so how does how do we pick
  • 00:38:06
    a platform which allows those thing to
  • 00:38:08
    be natively there and obviously as a as
  • 00:38:11
    a software company here we have to make
  • 00:38:13
    sure that everything we we do can be
  • 00:38:14
    maintained and the support overhead is
  • 00:38:17
    low. So that's kind of our needs. We
  • 00:38:19
    looked at a lot of alternative options
  • 00:38:22
    out there in the market. Um many of them
  • 00:38:25
    you can see the red crosses there really
  • 00:38:27
    didn't handle our needs in terms of
  • 00:38:29
    complexity of data models. They really
  • 00:38:32
    had I had to buy a different product to
  • 00:38:33
    go do that. Um native multi-tenency
  • 00:38:37
    um you know we had to explain
  • 00:38:39
    multi-tenency to some of them uh as to
  • 00:38:42
    what it meant and really they didn't get
  • 00:38:44
    it. Um and then a lot of these guys were
  • 00:38:47
    born before the cloud or even before the
  • 00:38:50
    AI for sure. uh and they were really
  • 00:38:53
    retrofitting AI into what they were
  • 00:38:55
    doing. So siloed tools, poor
  • 00:38:57
    multi-tenency support, very high
  • 00:38:59
    integration g cost because I had to plug
  • 00:39:03
    different components together and then
  • 00:39:05
    hence support goes up. Good data we had
  • 00:39:08
    a great experience in terms of
  • 00:39:10
    fulfilling our needs in each of those
  • 00:39:12
    areas. And so we saw co cohesive
  • 00:39:15
    platform easy to use easy to uh start
  • 00:39:18
    working with the analytics start to
  • 00:39:20
    build the applications integrate them uh
  • 00:39:23
    in in what we're doing and then what we
  • 00:39:25
    saw was more importantly because this is
  • 00:39:27
    our journey is that they were future
  • 00:39:29
    ready and um you know we we know this is
  • 00:39:32
    we're in a journey ourselves and so we
  • 00:39:35
    needed a a pl partner which could
  • 00:39:37
    actually be ready for us as well. So
  • 00:39:39
    that was kind of our needs. Let me just
  • 00:39:41
    show you how all this sort of comes
  • 00:39:43
    together in our application. Um so let
  • 00:39:47
    me start sharing here.
  • 00:39:50
    Um share my screen.
  • 00:39:54
    Can you see that? Okay. So um first
  • 00:39:57
    thing you notice I mean sustain 360
  • 00:40:00
    SASbased application.
  • 00:40:02
    uh the way we work we work in um high
  • 00:40:05
    energy intensive and as a result high
  • 00:40:09
    carbon intensive industries. So cement
  • 00:40:12
    um is a very uh carbon intensive
  • 00:40:15
    generating industry emitter high emit
  • 00:40:17
    emissions steel is the same glass and so
  • 00:40:20
    on. So where we got a lot of energy
  • 00:40:22
    being used you've got to generate a huge
  • 00:40:25
    amount of carbon. Um in in this view
  • 00:40:28
    here in this demonstration what we've
  • 00:40:30
    done is that there's there's a company
  • 00:40:33
    there's a kind of an enterprise view or
  • 00:40:36
    company level view of these two plants.
  • 00:40:38
    Okay. And m another plant as well and
  • 00:40:41
    these are then aggregated into company.
  • 00:40:43
    So if I'm in corporate and I'm looking
  • 00:40:45
    at my total carbon footprint across all
  • 00:40:48
    the assets that we've got then I can
  • 00:40:51
    look at a a corporate view. I'm a tenant
  • 00:40:53
    in that perspective. or if I'm in the
  • 00:40:56
    plant at a plant level or an individual
  • 00:40:59
    asset level then I can just look at the
  • 00:41:01
    asset and the plant I need to look at
  • 00:41:04
    and so with good data and the
  • 00:41:05
    multi-tenency support we're able to do
  • 00:41:08
    this aggregation and disagregation
  • 00:41:11
    effectively so if I go log in the first
  • 00:41:14
    thing it uh access the company level one
  • 00:41:17
    the first thing you notice is that there
  • 00:41:19
    is a canvas we call this a
  • 00:41:21
    sustainability canvas and this canvas
  • 00:41:24
    allows us to navigate between baseline.
  • 00:41:27
    So this is the plants and all these
  • 00:41:30
    plants rolled up and their baseline
  • 00:41:33
    carbon. So just think about our
  • 00:41:35
    cholesterol effectively it's all been
  • 00:41:37
    collected and then stored as a baseline
  • 00:41:40
    and then we're going to go on some
  • 00:41:42
    degree of an improvement plan. Um so
  • 00:41:45
    these are the alternatives and you can
  • 00:41:47
    have as many alternatives as you want
  • 00:41:49
    and so you've got alternatives which
  • 00:41:51
    have been rolled up across all those
  • 00:41:53
    plants and now aggregated and so we can
  • 00:41:57
    now easily compare and contrast the
  • 00:41:59
    reduction in carbon uh between the
  • 00:42:02
    baseline and the alternatives. These
  • 00:42:04
    nodes here are what was required from a
  • 00:42:08
    standards perspective. So the GHC
  • 00:42:10
    protocol um out created from the Paris
  • 00:42:13
    agreement requires that carbon is
  • 00:42:16
    measured in scope one, two and three.
  • 00:42:19
    And so we basically allow that metric to
  • 00:42:22
    be calc automatically calculated. If I
  • 00:42:24
    then break down into the nodes here, I
  • 00:42:28
    can now go into individual plants and
  • 00:42:31
    look at individual plants emissions
  • 00:42:34
    um easily. I can just navigate all the
  • 00:42:36
    way through in in what I'm doing. So
  • 00:42:38
    hopefully that gives you kind of a a
  • 00:42:40
    quick feel for the the the experience
  • 00:42:43
    here. But what's really powerful is when
  • 00:42:46
    you've got this these relationships all
  • 00:42:48
    mapped out in a knowledge graph, how do
  • 00:42:51
    you bring the analytics in? And then the
  • 00:42:53
    power of good data uh widgets allow us
  • 00:42:56
    to effectively view these uh dashboards
  • 00:43:00
    interactively with our our application.
  • 00:43:04
    And so you could see clearly that I can
  • 00:43:06
    the reduction between the baseline and
  • 00:43:09
    alternative by scope has been mapped
  • 00:43:11
    out. I can add charts and hover over
  • 00:43:13
    things. I can sort of make this zoom
  • 00:43:16
    this out and so on. I can change the
  • 00:43:18
    metrics. I'm looking at metrics per ton
  • 00:43:21
    or I can look at if I'm a kg point of
  • 00:43:24
    these are huge numbers. So you can see
  • 00:43:26
    here the reduction is around 500 million
  • 00:43:30
    kgs of CO2 emissions. if you went to
  • 00:43:32
    went down to that alternative. Um so
  • 00:43:35
    that kind of gives you an overall view.
  • 00:43:37
    But if I then want to explore further, I
  • 00:43:40
    can then go and say okay let me look at
  • 00:43:42
    that particular node and I can go say
  • 00:43:45
    okay here's the emissions on that or I
  • 00:43:47
    can go to a node here. I can drill down
  • 00:43:50
    and understand the exact emissions.
  • 00:43:52
    Standards are embedded into everything
  • 00:43:54
    we do and that has to be reported. So I
  • 00:43:57
    can now navigate my way through the the
  • 00:44:01
    the chart here and build further
  • 00:44:03
    analytics which are do essentially
  • 00:44:06
    allowing me to explore uh the actual
  • 00:44:09
    numbers behind the total aggregation at
  • 00:44:13
    the plant level or subplant level.
  • 00:44:18
    And so just to help set the context
  • 00:44:21
    here, you know, how much of this is
  • 00:44:23
    being driven like by the multi-tenency
  • 00:44:25
    features at good data? Is each level of
  • 00:44:27
    this like a separate tenant or how how
  • 00:44:29
    is it structured under the hood? All
  • 00:44:31
    right, that's a great question. So
  • 00:44:33
    firstly at the physical level you've got
  • 00:44:36
    uh if I just uh for the physical level
  • 00:44:39
    there's separation of um the the
  • 00:44:43
    customer. So you got a physical
  • 00:44:45
    separation of a tenant. Then you've got
  • 00:44:48
    um at the company level. So you're now
  • 00:44:50
    in the company. So this cement plant for
  • 00:44:53
    example. And we've got a segregation of
  • 00:44:57
    the plant that company's plants now. And
  • 00:45:00
    so we're now looking at multiple plants
  • 00:45:02
    because I'm looking at that particular
  • 00:45:05
    uh organizational structure. Now if I if
  • 00:45:08
    I go into the other plant and I now only
  • 00:45:11
    want to look at that plant then I can do
  • 00:45:13
    that as a sub uh sort of logical view of
  • 00:45:17
    that tenant if that makes sense. Yeah.
  • 00:45:20
    Yeah. Good. Thank you. Um now in terms
  • 00:45:23
    of reporting uh we just showed you the
  • 00:45:26
    dashboard capabilities there. Um the
  • 00:45:28
    other thing we can do quite easily is
  • 00:45:30
    put in where are the highest emitters in
  • 00:45:32
    where we where things are. Um, uh, let
  • 00:45:36
    me just bring that back up again. Um,
  • 00:45:38
    there's usually live demos. It's not
  • 00:45:41
    going to do it for me. Um, it's a
  • 00:45:43
    guarantee the ability to Yeah, it always
  • 00:45:45
    happens through it. Was working a few
  • 00:45:48
    seconds ago. Um, the next area is around
  • 00:45:51
    how do I do the reporting? Um, there's
  • 00:45:54
    two ways of accessing the reporting. You
  • 00:45:57
    can simply right click on a node and
  • 00:45:59
    it's contextual to that node. the report
  • 00:46:01
    will appear or you can um select from
  • 00:46:06
    the menu here. So either way, so if I do
  • 00:46:09
    it here, pretty cool. You can go into
  • 00:46:13
    that node um and I'm remember I'm
  • 00:46:16
    looking at the baseline and I'm looking
  • 00:46:18
    at all the plants and their total carbon
  • 00:46:21
    emissions um
  • 00:46:25
    aggregated here for those plants. And
  • 00:46:27
    then you can see the ability to generate
  • 00:46:30
    rich graphics here from an emission
  • 00:46:32
    perspective and stages and so on. And
  • 00:46:35
    these charts are interactive. So if I
  • 00:46:37
    click on a particular area, it will then
  • 00:46:40
    um change the actual um view. So you can
  • 00:46:45
    see that's now reduced there. And
  • 00:46:47
    there's a I can reset um so the
  • 00:46:50
    interaction of the charts is really
  • 00:46:52
    good. Um and then all the emission
  • 00:46:54
    factors and these are you can see all
  • 00:46:56
    the emission factors we have by
  • 00:46:57
    materials, energy sources, categories
  • 00:47:00
    are all broken down um as we go forward
  • 00:47:03
    and these rich visualizations really
  • 00:47:05
    help analyze where the the impacts are.
  • 00:47:09
    I can then flip that view from baseline
  • 00:47:13
    to alternative and apply that and you
  • 00:47:16
    can see the alternative has a reduction
  • 00:47:18
    from 1.5 to 1.4 four uh reduction at the
  • 00:47:23
    overall plant level and it's the same
  • 00:47:25
    visualization again for that baseline
  • 00:47:28
    view and alternative views. So I can
  • 00:47:30
    look at this and cut it that way or I
  • 00:47:33
    can say okay I really don't want to look
  • 00:47:35
    at it another way. I want to look at it
  • 00:47:37
    you know at a plant level and so I can
  • 00:47:39
    now bring in the plants. I can change
  • 00:47:41
    plants around. I can look at them
  • 00:47:43
    baseline um or I can look at the at an
  • 00:47:47
    alternative view. So I could flip uh
  • 00:47:50
    between different plants and different
  • 00:47:52
    designs easily uh within uh within my
  • 00:47:56
    report. I can also export that report
  • 00:47:59
    out. And if I just flip over here um and
  • 00:48:06
    trying to find the actual report, it
  • 00:48:09
    believe me exports out exactly as you
  • 00:48:11
    see uh in in in the reports here. So one
  • 00:48:15
    of the key things was when you export to
  • 00:48:17
    PDF, you sometimes lose lose resolution.
  • 00:48:21
    With the good data reports, we do there
  • 00:48:24
    was no reduction in that. And then we
  • 00:48:26
    can add any labels as we want into this.
  • 00:48:30
    Um so that was kind of looking at at a
  • 00:48:32
    group level. Um and I've now got a a
  • 00:48:35
    view where I've got to look at this um
  • 00:48:39
    from a comparison point of view. So we
  • 00:48:41
    have a set of features around
  • 00:48:42
    decarbonization.
  • 00:48:44
    One of the obviously important features
  • 00:48:46
    is to look at it from a waterfall
  • 00:48:48
    perspective. So how do I now know that
  • 00:48:51
    my baseline was x amount of x billion
  • 00:48:55
    amounts of CO2 emissions on an annual
  • 00:48:58
    basis? As I say, these are very very
  • 00:49:00
    high emitting um assets and these are
  • 00:49:03
    multiple plants which are rolled up. So
  • 00:49:05
    these are are actually in the billions.
  • 00:49:08
    And so now I got a a reduction here and
  • 00:49:11
    I got a further reduction here. And I
  • 00:49:13
    can look at that by where are the
  • 00:49:15
    reductions occurring within within
  • 00:49:18
    energy within various material changes,
  • 00:49:22
    electricity changes and so on. And so
  • 00:49:25
    how was that 500 million kg reduction
  • 00:49:28
    app going to happen and what over what
  • 00:49:31
    over what time period as well as we go
  • 00:49:34
    for and then design changes as well with
  • 00:49:36
    the material or energy. So we could
  • 00:49:39
    build the waterfall charts and other
  • 00:49:41
    charts as we go forward. So that's kind
  • 00:49:43
    of the group view. Um as I go to a um
  • 00:49:47
    individual view uh I can now and this is
  • 00:49:50
    the power of a common data model
  • 00:49:52
    underneath because I'm now going into a
  • 00:49:56
    cement sector and a steel sector indust
  • 00:50:00
    two completely different industries
  • 00:50:02
    um and remember that was all rolled up
  • 00:50:04
    into the group view. So diff different
  • 00:50:07
    sectors, different industries um now
  • 00:50:10
    have to be aggregated using those
  • 00:50:12
    standard metrics. Um so I'm now going to
  • 00:50:15
    cement I've got the cement plant exactly
  • 00:50:18
    the same visualization
  • 00:50:21
    um exactly the same navigation to go and
  • 00:50:24
    look at you know the um
  • 00:50:27
    drill down into things um are built into
  • 00:50:30
    the dashboard.
  • 00:50:31
    And then if I go into the reporting
  • 00:50:34
    again, I'm only going to see that
  • 00:50:36
    specific uh plant and and the details of
  • 00:50:40
    that plant. Okay. So you can see here
  • 00:50:43
    and further details around exactly
  • 00:50:45
    what's going on here. Yeah. Um the next
  • 00:50:50
    area in terms of um this particular view
  • 00:50:54
    is if I again the power of that data
  • 00:50:57
    model is going to flip a completely
  • 00:50:59
    other industry. I'm going to go into the
  • 00:51:01
    steel sector. So the steel industry,
  • 00:51:04
    same visualization, same analytic view.
  • 00:51:07
    I go in here, I go and see the emission
  • 00:51:10
    factors. Here I'm just looking at the
  • 00:51:12
    plant, not the alternatives. Uh in this
  • 00:51:15
    view, same thing. Uh they've also got
  • 00:51:17
    their carbon emission report, but for
  • 00:51:19
    this particular plant, we had to create
  • 00:51:22
    the ESGs. So it was additional reporting
  • 00:51:26
    which had to be uh provided. And so here
  • 00:51:29
    are all the ESG so environmental
  • 00:51:31
    sustainable uh metrics for that plant
  • 00:51:34
    specifically. Um and so if I go into the
  • 00:51:38
    employee here um these
  • 00:51:41
    employee metrics and you can see I have
  • 00:51:44
    to align it to a standard. So all these
  • 00:51:46
    metrics have to be calculated to a
  • 00:51:48
    standard um and I have to show that I'm
  • 00:51:50
    compliant to that standard. So that has
  • 00:51:52
    to be embedded. So I could look at my
  • 00:51:55
    gender, my age, age groups, types,
  • 00:51:57
    ethnicity,
  • 00:51:59
    gender breakdown, uh even down to makeup
  • 00:52:02
    of employees around middle executive
  • 00:52:05
    middle management by gender as well. So
  • 00:52:08
    this level of granularity and this data
  • 00:52:10
    is an API which is coming from the HR
  • 00:52:14
    system for that plant and that which
  • 00:52:16
    allows us goes into our data uh data
  • 00:52:19
    warehouse to produce this level of
  • 00:52:21
    analytic. the um if I then show the
  • 00:52:24
    power of the the data model here is
  • 00:52:28
    energy obviously these are high energy
  • 00:52:30
    intensive industries so we've got the
  • 00:52:32
    energy data to calculate the carbon but
  • 00:52:35
    now we can use that energ same energy
  • 00:52:37
    data to report for e reporting to see
  • 00:52:41
    what the kilms are doing for that steel
  • 00:52:43
    plant remember this is steel plant it's
  • 00:52:45
    going to use different um assets so
  • 00:52:48
    these are furnaces and so these furnaces
  • 00:52:51
    and their emissions. See the amount of
  • 00:52:55
    energy being consumed. It's humongous
  • 00:52:57
    amount of energy is being used by these
  • 00:53:00
    plants uh on an annual basis in terms of
  • 00:53:03
    kilowatt hours. And we can break that
  • 00:53:05
    down. You can see exactly where those um
  • 00:53:08
    uh energy usage is uh is occurring. So
  • 00:53:11
    completely different industry,
  • 00:53:13
    completely different asset allows us to
  • 00:53:16
    look at their energy footprint against
  • 00:53:17
    the standard. Um, I hope that gives you
  • 00:53:21
    a a quick feel for how we're using
  • 00:53:24
    Sustain 360 uh to enable climate
  • 00:53:28
    intelligence and the power of good data
  • 00:53:30
    tools and platform to make our products
  • 00:53:33
    successful and help our clients and
  • 00:53:36
    address, you know, how do we measure,
  • 00:53:39
    report and reduce carbon and
  • 00:53:41
    environmental impacts, but at the same
  • 00:53:44
    time help them achieve their net zero
  • 00:53:46
    targets. Thanks, Ry. That was that was
  • 00:53:48
    great boss. Thank you for uh for sharing
  • 00:53:50
    that with us. Really cool to see uh what
  • 00:53:53
    you what you are able to do there. Um
  • 00:53:55
    and and like really putting like the
  • 00:53:57
    good part in good data, right? This is
  • 00:54:00
    data being used for good. So um that's
  • 00:54:03
    awesome to see. Well, that that uh wraps
  • 00:54:07
    things up here for us, folks. So thanks
  • 00:54:09
    for joining us today. Of course, you can
  • 00:54:11
    always stay in touch with us. Um uh
  • 00:54:13
    we're active everywhere. uh LinkedIn,
  • 00:54:16
    Facebook. Um I there was a question, we
  • 00:54:19
    got a question earlier, you know, how do
  • 00:54:21
    I know when I have a new feature? Look,
  • 00:54:23
    the best way to do it, go to
  • 00:54:24
    gooddatacconnect.slack.com.
  • 00:54:27
    Every new feature, we have a new
  • 00:54:29
    features channel and you get an
  • 00:54:31
    announcement the day it's available that
  • 00:54:32
    describes it with links to the
  • 00:54:34
    documentation and a demo of how to use
  • 00:54:36
    it. So, please join the community if you
  • 00:54:38
    want to get these new feature
  • 00:54:40
    announcements as quickly as possible.
  • 00:54:42
    Also uh coming up on July 23rd, we will
  • 00:54:45
    be doing a webinar. The best embedded
  • 00:54:47
    analytics strategy isn't just build or
  • 00:54:49
    buy. We will be going deep on the
  • 00:54:52
    strategies our customers use to develop
  • 00:54:54
    really custom tailor made data products
  • 00:54:57
    like what Bos has that meets exactly
  • 00:55:00
    their users needs. Um and then you can
  • 00:55:03
    uh catch us live September 10th and 11th
  • 00:55:05
    at Big Data Expo uh Utrect. uh September
  • 00:55:08
    17th, we'll be doing another Q3, another
  • 00:55:11
    product update webinar, this time for
  • 00:55:13
    Q3. Um and then finally, September 24th
  • 00:55:16
    and 25th at Big Data London. I hope to
  • 00:55:19
    see you there. So, um I want to thank
  • 00:55:21
    Bos again for joining us and um until
  • 00:55:24
    next time, folks. Take care and we look
  • 00:55:26
    forward to seeing you on another good
  • 00:55:28
    data webinar. Bye. Thank you very much.
  • 00:55:31
    Thanks, guys. Thanks, Bos. Thanks,
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