Intelligent Data Exploration

Predictive maintenance isn't getting the job done. To really get ahead, maintenance teams and their leaders need ways to consider all the relevant data.

Equipment availability is the difference between success and failure. If a resource is out of service for any reason it means lost revenue. Your data can help keep your equipment running smoothly if you can leverage it correctly, but just predicting failure rates isn’t enough to gain a competitive advantage.

Financial business leaders, listen up: your data analysts should be your best friends. Here's why.

If you feel like your data and analytics capabilities have fallen behind, you aren’t alone. A recent CIO survey found that 85% of organizations aren’t using tools designed to explore complex data. That means most companies have teams of analysts who are providing reports on the snippets of the past, rather than strategically recommending actions for the future.

That's why it's time to ditch your dashboard...or at least get something to level it up.

The latest episode of the Intelligent Exploration Podcast features Ana Garcia, Director of Data Science and Analytics at ZipRecruiter, as she reflects on the changing landscape of business decision-making as she sees teams shift from relying on presentation decks and bar graphs to developing interest in data-driven solutions, dashboards, and predictive models.

Transcript:

Caitlin Bigsby: Hi, and welcome to the Intelligent Exploration Podcast. I'm joined today by Anna Garcia. Can you tell us a little bit about yourself and your background and how you got to where you are today and working with analytics and AI?

Ana Garcia: Sure. I'm originally from Brazil and I did most of my career in Latin America, in Brazil, and in Mexico. And I started working back then with Microsoft Access and traditional databases in a consortium firm. Eventually one thing led to another sooner was MBI and traditional analytics and all the way into machine learning model, causal inference. And basically, what I do today, I am a director of Data Science at ZipRecruiter. ZipRecruiter is a jobs marketplace, so we connect job seekers and employers. And what I do there is manage a series of teams that support our product teams by doing product analytics, calls of inference, experimentation, and design to help us build and improve these products. Before that I was also at Uber and Lyft who had fantastic data science benches and I learned a lot. So this is a little bit about.

Caitlin Bigsby: Me that is great. Also, Zip Recruiter is also known for advertising on podcasts. That's where I first heard of them.

Ana Garcia: We'll get an ad from them soon. Yeah.

Caitlin Bigsby: So you and I met before and we had a little chat about areas of interest. And one of the things that came up was data literacy at organizations and how important increasing data literacy is for the creation of and the adoption of AI and analytics in general. What do we mean when we talk about data literacy? What does that mean to you and who do you think needs it most?

Ana Garcia: Yeah, absolutely. So when I started working many years ago, the typical business general manager or person would be very excited about PowerPoint decks and presentations and bar graphs and things like that. But they would also typically ask a consulting firm or maybe a specific department to build them for them. And then there was a slow turnaround time, but eventually they would get those graphs and reports and they would read these reports and make calls, right? So that was the typical business flow. And I think nowadays you have these businesses talking about the importance of making fast decisions, of moving fast and using data. And now these business people, they are interested in data solutions, they are interested in dashboards, they are interested in models, they are interested in predictions. So when I started, it was not super common to have a business executive asking for a model to predict or to infer the best price or asking questions like, we did this change in the product, did it increase sales or not?

Caitlin Bigsby: Right.

Ana Garcia: Typically they would just look at a bar graph and make a decision. Nowadays you get very exciting data requests from your business partners. So I want to see the data, I want to deep dive in the data, I want the raw data. I want to do the SQL myself. I actually want a model for this. I want artificial intelligence applied for this. Can we build a machine learning model?

Caitlin Bigsby: Right?

Ana Garcia: So I think it is very exciting to see that change happen. But in order for that change to happen, you need to educate people on what are these tools, what is the data world, what are the challenges of working with data? And I think data gives us so much power, and with great powers come great responsibilities. And it's ex

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There are two major questions we have to ask ourselves about Generative AI: Can we trust the answers engines generate, and how do we know that we can?

With the collapse of a third bank this year, and an increasingly volatile financial market, financial institutions don’t have a lot of room for error. What they do have is a lot of data, both internal and external, and data analysts doing their best to find signal to guide them in massive, complex datasets. When analysts can truly explore all the relevant data, and visualize it in constructive ways, they can guide teams to winning strategies...if they have the right tools.

The Virtualitics AI Platform is designed to save your analysts from an overwhelming avalanche of data in Snowflake and discover insight that can change your business.

On our latest podcast episode, Caitlin Bigsby, Virtualitics' Head of Product Marketing sits down with Shreshth Sharma, Senior Director of Strategy and Operations at Twilio, to talk about his journey into the world of data and analytics from management consulting. Drawn by the intersection of business, technology, and data analytics, Shreshth has built a great reputation in analytics.