Furnishing a home can be a daunting task, especially if you’re living in a place with a few funny angles and oddly shaped nooks. IKEA, the furniture retailer known for their DIY kits, can provide you with some great easy-to-assemble pieces to fill your space, but for unique layouts, prefab furniture isn’t always going to be a perfect fit. These are the times when bringing in a custom or niche-focused solution delivers the perfect fit. When you finally have all your furniture, the result will be a blend of unique and off-the-shelf pieces that all work beautifully together.Similarly, every organization functions better when they have the right mix of IKEA-like DIY data analytics tools, such as self-service BI software, and custom solutions like AI-guided analytics that are capable of exploring complex data and discovering insight hiding in unusual places.Data exploration requires more than one toolThe applications used every day to run businesses create and capture thousands of data points every second. As a result, there is a deep treasure trove of information buried in these systems…but not a lot of resources or skills to analyze it all.Fortunately, there has been a ton of innovation in the BI technology space, making it easier for data consumers to now create their own reports and dashboards. This means they can get answers to some of their recurring questions without waiting for an inundated data scientist or analyst to find space in their project queue. In other words, they’ve now got their very own IKEA of business analytics at their fingertips.What’s also great about self-serve analytics is that it allows consumers to create their own reports within the boundaries set by experts. When data analysts are freed from creating and maintaining BI dashboards and spreadsheets for data consumers, they’re able to use their time and skills towards putting the correct guardrails in the self-serve software. This will minimize problems that come from using the wrong data, but it does limit the scope of inquiry…and that means some insights go unseen.This leads us to the custom solution that complements self-serve data work: AI-guided analytics. Platforms like Virtualitics give analysts the ability to dive deeper into data and find insights that will set your business apart. Deep exploration of complex data does require advanced analytic skills, but by leveraging AI-powered Intelligent Exploration solutions, data analysts can become stronger strategic advisors.

The answers to business problems, large and small, are there for the taking—right in your organization’s data. Yet, the quantity and types of data available for analysis have outpaced the tools most organizations have been using.A CIO.com survey found that 85% of companies are using inadequate tools to explore complex data sets. Furthermore, nearly two-thirds of data science leaders surveyed say that data exploration is held back by a lack of data science skills.While data scientists have been employing piecemeal AI techniques to wrangle their complex data for years, it’s only recently that AI innovation has resulted in technology that brings sophisticated data analysis within reach of analysts.

Early this month I moderated the panel “The Implications and Opportunities of Generative AI in FS'' at Corinium’s CDAO event in Boston with David Dietrich (VP, Advanced Analytics and Governance at Fidelity Investments) and Jake Katz (Head of RMBS Research and Data Science at the London Stock Exchange Group). This was a lively discussion with a really engaged audience and it really highlighted for me the squeeze that Data and Analytics leaders are facing right now between their business leaders demand to hop on the GenAI train and finding a practical application for it. Data science insiders have known about GenAI for a while but the launch of ChatGPT at the end of 2022 brought awareness of it into the public consciousness, including that of senior management. Where before AI seemed ephemeral and complicated, ChatGPT made it tangible and easy. It also made AI seem a little bit like magic. As David noted, this led leaders to demand this technology, dedicating significant resources to integrate it into a broad set of applications. But do leaders really understand how GenAI and large language models (LLMs) work and what they’re asking for?The consensus from the audience was a resounding ‘No’. It’s tempting to shrug at this situation–this is just the latest in a long line of new technologies that seem to get everyone excited and distracted. No doubt the hype will settle down, right? Indeed, CCS Insight predicts that this is exactly what will happen in 2024 as the cost to deploy GenAI and LLMs safely and responsibly overshadows the value of the realistic applications of the technology in many situations. Are there Generative AI Applications in Financial Services?Does this mean that GenAI has no potential use cases in FinServ? Not at all. It’s proving its mettle with use cases in customer support, content generation, and even coming up with potential business ideas. These are all areas that offer a lot of efficiency gains and are worth exploring. But that still leaves a lot of the business that’s not currently seeing gains. And this leads me to my next point. What’s happening to all the other data-based initiatives and AI use cases while resources are diverted to GenAI? They’re stalling; and they were struggling to begin with (a CIO.com report says that only 53% of projects were seeing results). I could see in the room the frustration with an audience pressured to take away their attention from problems that could be solved with applications of other, more practical forms of AI. Managing up is never easy, but I think senior leaders need to hear that GenAI, while exciting, is not the answer to every business challenge. But CDAOs have good ideas that could be valuable ideas, and it’s time to turn their attention back to solutions that make sense.Download the eBook Three steps to solving your biggest business challenges with data + AI to see where your data can take you.

Virtualitics delivers an AI platform and custom workflows for the federal government to drive mission readiness, and discover intelligent insights.

Unstructured data is produced in abundance by every business in some way. Whether it's images and videos, text-heavy emails, or sensor data, all of these have the potential to increase competitive advantage if meaningful, actionable insights can be extracted from them. But traditional analytics tools haven’t been optimized to pull from or make sense of unstructured data sources. This means organizations are excluding a huge cache of their data from all their analyses—and potentially leaving revenue-generating information on the table. Fortunately, advances in AI technology now enable businesses to leverage their unstructured data in exciting new ways. The question is, in the world of unstructured data, how can organizations extract key insights from this dataset—without the headache?

Approximately 2.5 quintillion bytes of data are produced every day (for reference, there are 18 zeros in that number!). Companies contribute an immense amount of data to those bytes and for a long time, BI dashboards were enough to make sense of all that information. But as datasets continue to grow and become more complex, the limitations of BI tools are leading to a mind-numbing phenomenon known as “Death by Dashboard.” Similar to the “Death by PowerPoint” meetings featuring decks with 100+ slides, BI dashboards are being packed with reports and data points until they resemble an abstract painting more than a tool for deriving valuable business insights. There’s simply too much information in them to be helpful or digestible. Teams who are using overpopulated dashboards are failing to deliver on the value within their treasure trove of data. But the answer isn’t to put less information in your dashboard either because this won’t give you an accurate picture of your business. Instead, to gain strategic business insights, you need advanced and AI-guided analytics tools that allow for deeper exploration of your data.

Organizations today recognize the potential of their data to drive business-changing insight. However, as data sets become more complex and robust, analyzing comprehensively using traditional methods has become difficult. Multidimensional data is one such data set, capable of helping analysts uncover critical information that leads to better strategic decision-making. But this complex data requires looking beyond two-dimensional structures, so not every company is able to take full advantage of it. Let's talk about why multidimensional data is so important to businesses and how your analysts can leverage it more regularly.

AI is being used and abused by criminals to expand the reach of cyber attacks. The good news? AI can also be used to fight against those same attacks.

Conversation with Jeff Vagg, Chief Data and Analytics officer at North American Bancard. Listen in as he discusses Gen AI--how to use it, what to be wary of-- the changing role of the analyst, and how they're planning to use Virtualitics!

We turned to the experts on our Scientific Advisory Board for their insights on the foundational skills necessary for data exploration, how network science and AI fit in, what ChatGPT will really replace in the world of work, and more.

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

Generative AI tools like ChatGPT have emerged as potentially powerful assets for businesses across various industries. However, not everything created by generative AI has value, and some of it carries significant risks. The question is, what do users need to do to find strategic and safe uses for generative AI?

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.