Guest: Russ Kennedy (LinkedIn)
Company: Nasuni (Twitter)
Show: Let’s Talk

Nasuni, a 12-year-old company headquartered in Boston, Massachusetts, offers a hybrid file data platform for customers who primarily want to consolidate their file data in the cloud and then leverage that data in any location their operations happen to be, i.e., in a physical office, data center or manufacturing facility. Wherever they need that file data, Nasuni provides access capability.

In this episode of TFiR: Let’s Talk, Nasuni Chief Product Officer Russ Kennedy shares his thoughts on data intelligence and how the company’s platform is helping companies leverage their data assets.

On data, data intelligence, data analytics, and AI:

• In today’s modern organizations, it’s all about capturing data, preserving data, protecting data, and then leveraging data to drive business outcomes.
• Data intelligence is all about understanding the data an organization has access to: How old is that data? When was that data created? How relevant is that data to the organization’s business and mission?
• Data analytics and artificial intelligence essentially leverage the data to produce some result, business outcome, product, technology, or capability.

Current state of organizations regarding data centricity:

• There are organizations that have been around for hundreds of years that are in the process of digital transformation. They’re collecting information that could go back years and want to use that information in some respect. In order to do that, it has to be digitized first, then collected, etc.
• There are organizations that are naturally data centric. They’re born in the cloud and their business is all about data. They leverage data very effectively.
• As organizations move along that spectrum and get closer and closer to becoming fully digitized, they start to take advantage of the tools and capabilities that exist in the market today.

Challenges companies face when trying to leverage data:

• Understanding what data they have access to, whether that’s data they created, public data, or a combination of those data assets.
• Capturing that data in a way that it can be used and leveraged. Most organizations today are distributed, i.e., different locations, different offices, different facilities, employees working in different locations. Most likely, their data is distributed as well. They need to be captured and sorted.
• Ensuring the data that they’re managing and using is protected and secured. If someone’s using AI, their organization’s specific data should not be exposed to a large language model or some other repository that others can access.

How Nasuni helps companies:

• Help customers coalesce their data in different locations, get that data into the cloud with our hybrid cloud platform, and then leverage those data assets for business outcomes.
• Case in point: A Nasuni customer works in the design infrastructure design space and wants to submit a proposal for a new hydrology project. They wanted to leverage all of the work they’d done in previous projects. First, they needed to understand where all of that data was located. Then, they took some of that hydrology data, incorporated it in an AI LLM, and used generative AI to produce proposals for the follow-on projects related to hydrology.
• Nasuni’s software-defined offering allows customers to manage their unstructured data in file format, in a hybrid fashion. It delivers the software and customers pay for the license on an annual subscription basis.

Myths/misconceptions about data intelligence:

• People often use the terms data intelligence, data analytics, and AI interchangeably, but they’re not necessarily the same.
• Organizations think that artificial intelligence is eventually going to replace the need for data intelligence, which is wrong. You need to curate that data, you need to make sure that the data that you’re passing to an AI solution is the right data. Data intelligence helps you with that process.
• Organizations believe that they need to have an army of data scientists in order to effectively use data intelligence and AI. That’s not necessarily true. You do need to educate your staff about these concepts and how to leverage these tools and capabilities most effectively. You still have to have data scientists to understand what the right data is and how it is being used, but you certainly don’t need an army of them.

Advice for companies looking to develop a data intelligence strategy:

• Define a specific set of objectives for your digital transformation or your data intelligence strategy. What are you trying to accomplish? What are your outcomes? What are your goals?
• Choose vendors and partners that can help you get there — partners that have the experience, the skills, the knowledge to help you.
• Don’t try to do too much all at once. Compartmentalize, divide and conquer, deliver short, productive wins to the organization and then build on that.
• Measure your progress against your original objectives. If you need to course-correct, do so quickly to maintain momentum.
• Be flexible and be able to adapt as the organization’s needs and technologies change.

This summary was written by Camille Gregory.