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Vectice raises $12.6M to help enterprises document their data science assets

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Spurred by its revenue-boosting potential, companies are increasingly embracing AI technologies across their organizations. Harris Poll, working with Appen, found that 55% of businesses accelerated their AI strategies in 2020 due to the pandemic. But a data science skills gap threatens to stymie progress. In a recent O’Reilly report, a lack of skilled people topped the list of challenges in AI, with 19% of respondents citing it as a “significant” barrier.

The skills gap isn’t the only barrier standing in the way of AI deployment. Data scientists at companies don’t always have sufficient documentation, or a platform that makes their experiments reproducible and data assets discoverable. Likewise, managers sometimes lack a way to automate reporting or facilitate data science project reviews and processes. According to O’Reilly, holistic solutions for metadata creation and management, data provenance, and data lineage are uncommon even among companies implementing AI systems.

To address the challenges, Cyril Brignone and Gregory Haardt founded Vectice, a startup that allows data science knowledge to be automatically captured and translated into metrics for operational managers and executives. Following a proof of concept, the company today announced that it raised $12.6 million, bringing its total capital raised to $15.6 million.

Capturing AI knowledge

A number of startups offer data lineage products aimed at improving governance in the enterprise. For example, Datafold automates processing workflows to maintain a baseline measure of data quality, while Solidatus offers data management and modeling tools aimed at data scientists and engineers. But Brignone and Haardt assert Vectice is differentiated by its wider scope.

Vectice

Above: Vectice’s data management and tracking platform.

Image Credit: Vectice

Brignone, a serial entrepreneur and former research manager at Hewlett-Packard Laboratories, teamed up with Haardt to launch Vectice in 2020. Haardt was previously the CTO and VP of engineering at Lattice Engines and spent several years in product manager roles at Apigee and Salesforce.

“A handful of leading AI companies spent years developing internal solutions for their own data science knowledge and team management. Unfortunately, those solutions are custom and not available to the market. We built the same kind of solution but for all enterprises,” Brignone told VentureBeat via email. “In most organizations, AI project knowledge is locked within AI platforms. This knowledge is not accessible by the management and stakeholders and therefore not actionable. They also struggle with fragmented knowledge, almost all enterprises use multiple AI platforms and tools. [Vectice competes] with the status quo for those companies.”

Vectice’s platform plugs into existing systems and auto-captures the assets that data science teams create, including datasets, code, notebooks, models, and model training runs. It then generates documentation from business requirements to production deployments with version and lineage information, allowing users to retrieve any assets and learnings produced across multiple frameworks and libraries.

“We complement [existing project management systems] with several benefits,” Brignone said. “[Vectice] automatically captures and documents data science team knowledge on datasets, code, learnings, experiments, documentation, and projects. [It also helps to] onboard employees quickly and avoids tribal knowledge loss during project transfer or team member departure, [simplifying reviews by] defining repeatable best practices, establishing review processes, and promoting knowledge sharing. [This improves] alignment with business stakeholders by … showcasing impactful projects and successfully deployed models.”

Documenting data science

With data management remaining a major obstacle to AI expansion in the enterprise — in 2019, more than half of respondents to a Forrester report said that they simply didn’t know what their AI data needs were — tools like Vectice could help to smooth the path. As recently as 2018, only a third of companies in NewVantage Partners’ annual data analytics survey said that they’d succeeded in creating a data-driven culture.

Vectice

In its survey, Forrester recommends that firms adopting AI build a pipeline of consequential AI use cases and invest in growing their AI engineering teams. “Data scientists are central to turning data into intelligent AI models. However, an oft-heard complaint from data scientists and businesses alike is failure to operationalize AI models,” the coauthors write. “That’s because implementing transformative AI use cases requires a broader team — an AI engineering team — consisting of data scientists, business analysts, developers, operations professionals, and project managers.”

“The Vectice platform solves wasted spendings in AI by directly addressing two of the most common reasons for which AI projects fail,” Brignone continued. “Vectice creates a unified view of the data science initiatives across an organization. In one place, team members can discover previous artifacts, share domain knowledge, and communicate project progress to stakeholders. Vectice enables new, collaborative behaviors that increase team productivity, centralize project visibility, and reduce the common risks associated with failed AI projects.”

San Francisco, California-based Vectice, which has backing from Crosslink Capital and Sorenson Ventures (who co-led the series A announced today) in addition to Spider Capital, Global Founders Capital, and Silicon Valley Bank, claims it piloted its platform with 19 Fortune 2000 companies. Brignone says the new funds will be put toward increasing the size of Vectice’s customer-facing and R&D teams to “respectively scale up our client onboarding capabilities and support new product integrations.”

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Identity in the metaverse: Creating a global identity system

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With the advent of the metaverse, the need for a global identity system has become apparent. There are many different ways to create an identity in the metaverse, but no single system is universally accepted. 

The challenge is usually two-fold: first, how to create an identity that is accepted by all the different platforms and services in the metaverse, and second, how to keep track of all the different identities a person may have.

There are many proposed solutions to these challenges, but no clear consensus has emerged. Some believe that a single, global identity system is the only way to ensure interoperability between different platforms and services. Others believe that multiple identities are necessary to allow people to maintain their privacy and security.

The debate is ongoing, but it is clear that the need for a global identity system is becoming more urgent as the metaverse continues to grow.

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In this article, we will explore the various options for creating a global identity system in the metaverse. We will discuss the pros and cons of each option, and try to identify the best solution for the future.

Option 1: A single global identity

The simplest solution to the problem of identity in the metaverse is to create a single, global identity system. This would be a centralized system that would be responsible for managing all identities in the metaverse. 

The advantages of this approach are obvious: It would be much easier to keep track of identities, and there would be no need to worry about different platforms and services accepting different identities. In addition, a centralized identity system would allow for better security and privacy controls, as well as the ability to track identity theft and fraud.

However, this approach also has several disadvantages. First, it would be very difficult to create a global identity system that is accepted by everyone. Also, a centralized system would be vulnerable to attack and could be used to track people’s movements and activities. Third, it would be difficult to protect the privacy of users in a centralized system.

Option 2: Multiple identities

Another solution to the problem of identity in the metaverse is to allow each person to have multiple identities. This would mean that each person could have one or more identities that they use for different purposes. 

One of the main advantages of this approach is that it would allow people to maintain their privacy and security. Each person could choose which identity to use for each situation, and they would not have to worry about their entire identity being exposed. In addition, this approach would be more resilient to attack, as it would be much harder to take down multiple identities than a single one.

The limitations of such an approach would be that it could be difficult to keep track of all the different identities, and there would be no guarantee that different platforms and services would accept all of them. In addition, multiple identities could lead to confusion and could make it more difficult for people to build trust with others.

Option 3: A decentralized identity system

A third solution to the problem of identity in the metaverse is to create a decentralized identity system. This would be an identity system that is not controlled by any one centralized authority but rather is distributed among many different nodes. 

This might seem like the ideal approach, since decentralization is a common theme in the metaverse. However, there are still some challenges that need to be overcome. For instance, it would need to be ensured that all the different nodes in the system are properly synchronized and that the system as a whole is secure. In addition, it might be difficult to get people to adopt such a system if they are used to the more traditional centralized approach.

One solution would be to get the nodes in the system to be run by different organizations. This would help to decentralize the system and make it more secure. Another advantage of this approach is that it would allow different organizations to offer their own identity services, which could be more tailored to their needs.

Another would be to incorporate an edge computing solution into the system. This would allow for more decentralized processing of data and could help to improve performance. It would also make the system more resilient to attack since there would be no centralized point of failure.

The best solution for the future of identity in the metaverse is likely to be a combination of these approaches. A centralized system might be necessary to provide a basic level of identity services, but it should be supplemented by a decentralized system that is more secure and resilient. Ultimately, the goal should be to create an identity system that is both easy to use and secure.

The ideal identity standards of the metaverse

Now that we have explored the various options for identity in the metaverse, we can start to identify the ideal standards that should be met by any future global identity system. 

It is no easy task to create a global identity system that meets all of the criteria, but it is important to strive for an ideal solution. After all, the metaverse is still in its early stages, and the decisions made now will have a lasting impact on its future. 

Current iterations of the metaverse have used very traditional approaches to identity, but it is time to start thinking outside the box. The ideal solution will be one that is secure, private, decentralized, and easy to use. It will be a solution that allows people to maintain their privacy while still being able to interact with others in the metaverse. 

Most importantly, it will be a solution that can be accepted and used by everyone. Only then can we hope to create a truly global identity system for the metaverse.

The bottom line on identity in the metaverse

The question of identity in the metaverse is a complex one, but it is an important issue that needs to be addressed. 

The challenges associated with creating an implementation that is secure, private and decentralized are significant, but they are not insurmountable. For one, it will be important to get buy-in from organizations that have a vested interest in the metaverse. These organizations can help to promote and support the adoption of identity standards. 

It is also important to keep in mind that the metaverse is still evolving, and the solution that is ideal today might not be ideal tomorrow. As such, it will be critical to have a flexible identity system that can adapt as the needs of the metaverse change. 

Ultimately, the goal should be to create an identity system that is both easy to use and secure. Only then can we hope to create a truly global identity system for the metaverse.

Daniel Saito is CEO and cofounder of StrongNode

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How to Eliminate Scheduling Inefficiencies in Your Business

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What do salons, consultancies, and home service providers all have in common? This question may seem like the prime setup for a joke, but there’s no punchline to look forward…

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Why You Should Start a Business Only While You Have a Job

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Opinions expressed by Entrepreneur contributors are their own.

Many people that I meet tell me that they dream of starting their own . I always ask them, “Then why don’t you?” They typically respond by saying that they have so many financial and personal responsibilities, that they can’t just quit their job to start a company, etc. Then I tell them my story …



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Related: How to Use Your Current Job to Start Your Next Business

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