The Business of Data

‍

Desi: Let's get started. Thank you so much for joining us today. My name is Desi, and I'm the Head of Design here at Metaphor. Alongside Seyi, Kirit, and Jess, we have put together a great webinar for you today. We're going to talk about the business of data. Our goal today is for you to walk away with strategies for engaging business users, emphasizing trust-building and collaboration to advance a data-driven culture.

I'm sure some of you might have questions along the way. Before we get into that, just a quick agenda: we'll do introductions, let you know how you can communicate with us and ask questions during the webinar, have our chat, some fun activities, a few demos, and then we'll hear from the experts and answer any questions you have. 

If you have a question at any time during the webinar, feel free to use the Q&A down in your Zoom window and ask your question there. Jess and I, who is my partner in crime, will be viewing all of your questions and either answer during or after the presentation. So, don't be shy. Let's get into it.

Desi: I'll do a quick intro of our speakers today. I want to welcome Seyi, who is the co-founder and founding engineer here at Metaphor. She has over a decade of experience building software at companies like Facebook. She also co-created DataHub at LinkedIn, and recently co-founded Metaphor. Hi, Seyi.

Seyi: Hey everyone, glad to be here.

Desi: Great. Next, we have Kirit, the Head of Product here at Metaphor. He has over 20 years of experience building products for enterprises and has been the head of product and strategy at companies from zero to exit. He's also the author of *Data Ops: The Authoritative Edition*. Hi, Kirit.

Kirit: Hi everyone.

Desi: Now, a little bit about Metaphor. Metaphor is the modern data catalog powered by social data intelligence and AI. Businesses today typically struggle with data silos and a lack of engagement across teams. Traditional data catalogs do not generally provide value to those outside of the core data team and often become expensive platforms where your data goes to die. 

What we do here at Metaphor is address these challenges head-on by providing a social platform for data where every team, independent of their expertise, can find, understand, and trust the data they use. Our unique approach to data governance and data products integrates within the modern stack and fosters a community around your data.

With that said, let's jump right into our chat. We want to start with the importance of a data-driven culture. Before we start, we have a question for you. Jess just launched this poll: How mature is your organization's data-driven culture? The options are very mature, moderately mature, emerging, or not at all.

Desi: A few more seconds and we'll see what everybody says. It seems like for most of you, it's emerging. You're on your way. The first question for our speakers is: Can you help us define what data-driven culture is? We'd love to talk about its importance today, challenges, components, policies, and how to measure if everything works.

Seyi: Sure. I'll let you start, Kirit.

Kirit: Yeah, so I think we all have some sense of what data-driven basically means, like collecting more data, reporting on it, creating metrics, etc. But the real interesting part is how companies that do really well succeed by getting to the true reason for tracking a particular metric. For example, with a metric like click-through rate, it’s easy to track it because it’s important. But you have to understand the context and what happens after, and that’s where companies that succeed really excel. They don’t just collect the data; they understand it and use it in context.

Seyi: Absolutely. When we talk about culture and the business of data, context and understanding what things mean is a huge part of that. You can have as much data as you want, but if you don’t know how to get value from it, it’s like unrefined oil. A data-driven culture is crucial for companies to be able to respond to changes in the world and gain a competitive advantage. A study by Harvard Business Review found that organizations that took a data-driven approach during the COVID-19 pandemic were best positioned to navigate the upheaval.

Desi: Let's talk about engaging the business users in data governance. Jess, can you prompt the survey question here? We want to know what is the biggest barrier to engaging business users in data governance at your organization: lack of awareness, lack of training, resistance to change, or overly technical UI.

Desi: Resistance to change is leading the poll. Kirit, Seyi, I’d love for you to talk a little bit about engagement strategies, trust-building techniques, and the importance of adoption beyond the data team.

Kirit: Sure. Think about a scenario where you're a data product manager or data steward. How many times do you actually interact with your business user? Usually, it’s just during meetings or when someone requests something. Most business users don’t care about or know the details of your architecture. And that’s where the opportunity lies. Traditionally, catalogs and governance tools have focused only on the data team. But the business users, who are actually using and working with data daily, live in a different world. We need to make sure everyone has a part to play and meet users where they are.

Seyi: Absolutely. For example, we have customers who tell us their data teams spend 30-40% of their time answering the same types of questions over and over again. We’ve built a platform where any user can ask a question in natural language, and the answer is generated based on the truth inside the catalog. This way, the data team doesn’t have to keep answering the same questions, and business users can get the information they need without having to understand or get trained on the catalog.

Desi: That’s a game-changer. Let’s talk about collaborative data governance frameworks and how they can be implemented effectively at organizations. Jess, can you prompt survey question number three? Does your organization currently use a collaborative approach to data governance: yes, no, considering it, or not sure?

Desi: It’s a tie between yes and considering it. Seyi, Kirit, can you talk a little about the key benefits, feedback loops, implementation steps, and supporting tools?

Seyi: Sure. A collaborative data governance framework involves all stakeholders in managing, using, and improving data assets within the organization. It's not just an IT function; it's a companywide responsibility. The key benefits include higher customer acquisition, retention, and profitability. You need to involve all stakeholders, democratize access to data, and establish community-driven standards by consensus rather than mandates. It's also important to have continuous feedback loops for communication about data quality, usage, and needs, and to adapt policies to different units within the organization.

Kirit: Let me show you an implementation of what Seyi is talking about. Imagine you're a business user using a dashboard and you have questions. You can ask your question directly within the platform without switching contexts. On the other side, the business analyst receives the question and can see the context of the data, including the lineage and any associated issues. This collaboration is built into the natural flow of work, making it easy for both technical and non-technical users to interact and resolve issues.

Desi: So many great insights. What immediate steps do you recommend our audience take to start implementing this today?

Kirit: Start by thinking about the first principles of the problem you’re trying to solve. Are you trying to solve a governance problem with a catalog, or is there a better way? Think about how you can meet your users where they are and give them a modern, simple user experience. The best catalog is the one you don’t even notice is there.

Seyi: Exactly. You want to create an environment where the catalog fades into the background, and users can seamlessly get to the value they need without having to think about the process. The system should understand who you are, what’s important to you, and provide you with the right data and contacts automatically.

Desi: We have a question from Perry: How do you prevent AI from giving users incorrect answers?

Kirit: Trust AI, but verify. AI is still immature, so we try really hard to minimize hallucinations by training it on data that exists and is published within the catalog. If the AI doesn’t recognize something, it won’t create an answer out of thin air.

Seyi: We also focus on user experience. If the AI gives an answer, it should cite its sources so users can verify the information. We’ve built in a lot of constraints to ensure the AI is grounded in your data, and if it doesn’t have an answer, it says so.

Desi: One more question: How can we help create documentation when everyone hates doing documentation?

Kirit: AI shines in generating documentation with the right prompts. We have features where you can summarize conversations and automatically generate documentation, making it easy to preserve institutional knowledge with just one click.

Desi: We actually had a webinar exactly on this, where we showcased these capabilities. I’ll paste the link so everyone can access it. Thank you so much to both of you for your insights today, and thank you to everyone who joined us. If you’re interested in learning more, you can find us on social media, our website, or LinkedIn. See you soon!

‍

About Metaphor

The Metaphor Metadata Platform represents the next evolution of the Data Catalog - it combines best in class Technical Metadata (learnt from building DataHub at LinkedIn) with Behavioral and Social Metadata. It supercharges an organization’s ability to democratize data with state of the art capabilities for Data Governance, Data Literacy and Data Enablement, and provides an extremely intuitive user interface that turns even the most non-technical user into a fan of the catalog. See Metaphor in action today!