Seven intriguing enterprise startups for 2022

Despite the unreasonable rejoicing around questionable innovations such as nonfungible tokens and metaverse, areas of continuous change remain in enterprise information technology.

I beat the bushes for some intriguing vendors and I thought of seven startups with real stories to tell. Not surprisingly, some common themes emerged in my discussions.

Observation – especially based on OpenTelemetry standards – is probably the most popular theme. Machine learning is a close second. Data, however, is the common thread that draws these innovative companies together, even though they all have different approaches to their offers.

Here are seven companies that have made the deduction:

Security and management changes

Privacera Inc .: Balancing data availability with ‘need to know’ access controls

Privacera is one of the pioneers in the relatively new data access governance market. The company is carving out access control from the broader data management space by focusing on data discovery, distributed policy enforcement, auditing and reporting across multiple cloud-based data sources , as well as fine access control.

The company helps its customers implement distributed security and policy enforcement across storage, computing and query federations for all major cloud providers and cloud-based data warehouses and data lakes. Given the enormity of sensitive data in organizations today, Privacera’s ability to establish “need to know” access controls without interfering with users ’day-to-day work is an important measure of the value of tool.

What intrigues Privacera: For more mature customers, Privacera has expanded its functionality beyond traditional centralized policy enforcement to a delegated implementation model that supports multiple departments with separate data domains.

Deepfence Inc .: Cloud-native security observation

Cloud-native computing extends beyond containerized Kubernetes deployments to build virtual machines, serverless and even bare-metal servers. Securing such a combination of environments presents a modern challenge that Deepfence focuses on.

The company offers telemetry-based observability tools that give data security professionals the insights they need to protect modern, dynamic cloud-native environments, both during development. as well as in production.

Why Deepfence is intriguing: Deepfence’s underlying technology is the extended Berkeley Packet Filter, a kernel -level Linux technology (with some support for Windows) that provides deep visibility to Deepfence at the packet level. Thus, EBPF enables the company to offer a level of security surveillance that differentiates them in the marketplace.

Torii Labs Ltd .: Automated SaaS management

Software-as-a-service applications dominate the application portfolio of companies of all sizes. SaaS products include market -leading offerings from Inc., Microsoft Corp., ServiceNow Inc., Workday Inc. and more, as well as thousands of specialty applications that fill everyone’s smartphone screens.

This abundance of SaaS applications presents a management nightmare to IT organizations responsible for dealing with compliance and security as well as software budgets. Torii addresses this SaaS management challenge through an end-to-end offering that detects the SaaS apps used in an organization and then applies hundreds of automated workflows that address those needs. issues from software license compliance to implementation of provisioning and deprovisioning policies to cloud spend management.

What intrigues Torii: Given the vastness of SaaS apps in organizations today, you’d think SaaS management would be at the top of every CIO’s shopping list – but it’s not. Torii offers the service that most organizations need but few know yet.

Bringing machine learning to the masses

Aporia Inc .: Tracking machine learning that brings AI to everyone

Machine learning has reached many businesses, but up to this point, getting value from this technology requires a special set of skills-skills more rare than chicken teeth in the tight market. of technology today.

Aporia helps address this problem with an MLOps tool that is surprisingly easy to use. At Aporia, working in machine learning is now as straightforward as working with any of today’s mature review tools. On the other hand, Aporia also provides a pane of glass for hard -to -find data scientists as well.

What intrigues Aporia: It makes machine learning a democracy so that anyone can use it to extract new value from their organization’s data.

Monte Carlo Inc .: Machine learning -driven data observation

In 1999, NASA’s Mars Climate Orbiter famously experienced a mission completion failure due to a simple mixing between metric and English units. In other words, NASA had a deadly problem with data reliability.

Data reliability is centered on questions such as whether your data is up-to-date, complete, within expected scope, conforms to expected schemas and other important considerations. An answer of “no” to any of these questions could mean that your data sets are corrupt.

Monte Carlo provides the observations necessary for data professionals to measure their data reliability and to take action in case their data sets are corrupted. The product works with data warehouses, data lakes, business intelligence tools and traditional extract/transform/load or ETL data sources, inside and outside the cloud.

What is intriguing about Monte Carlo: The company uses machine learning to discover data reliability issues in various data sets – including data sets whose purpose is training models. This case of using “machine learning for machine learning” is still new but is likely to be an established best practice in machine learning.

Observation for engineers

Cortex: Observations for better engineering collaboration

Today’s bubbly observability market is primarily focused on the needs of IT operators and system reliability engineers. Traditional wisdom says that software developers don’t need the insights that software telemetry can provide, because once they’ve thrown the code on the wall, well, it’s an ops problem now.

Innovative software development practices wholeheartedly reject supposed wisdom, because engineers are responsible for making sure their production code works properly. However, their observation requirements are different in ops.

Cortex addresses this need through observability tooling that gives engineers valuable visibility into their services, within the context of all the popular tools and current best practices for software development that they already take advantage of.

What is intriguing about Cortex: It supports modern deployment practices such as GitOps and shift-right techniques such as feature flagging and canary deployment, thus reducing the risk inherent in rapidly moving development cadences.

Aspect Inc .: Observations for developers of distributed services

Software development tools generally focus on the components of the software itself. For complex distributed environments, including most Toilet -based deployments, software is only part of the problem. The bigger challenge: the connections between software.

Aspect provides OpenTelemetry-based observability that helps engineers address such distributed computing issues by providing visibility and insight into message brokers, message queues, Kafka streams and more. The company’s service puts relevant telemetry into an interdependency database that engineers can query to conduct impact evaluations and discover the roots of issues affecting their code.

What intrigues Aspecto: By placing OpenTelemetry data in a graph database, Aspect exposes the full power of graph-centered analysis to engineers as they build complex distributed interactions.

Vendors struggle for categorization and recognition

A common characteristic that applies to the seven companies in this article is that they are all difficult to categorize.

In many cases, a company falls into two separate market categories – security and observability for Deepfence, for example, or the observability of Aspect for software development.

In other situations, the primary vendor value proposition does not fall into an enterprise’s IT budget. How many chief information officers have a line item for SaaS management (Torii) or machine learning monitoring (Aporia)?

These challenges are why articles like this are important. Don’t let conventional in-the-box thinking stop you from connecting the dots between a serious pain point and available solutions to your problems.

Jason Bloomberg is founder and president of Intellyx, which advises business leaders and technology vendors on their digital innovation strategies. He wrote this article for SiliconANGLE. At the time of writing, ServiceNow is an Intellyx customer. None of the other vendors mentioned in this article are an Intellyx customer.

Photo: Bru-nO/Pixabay

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