Despite the irrational exuberance around questionable innovations such as nonfungible tokens and the metaverse, there remain areas of steady innovation in enterprise information technology.

I’ve beaten the bushes for some intriguing vendors and came up with seven startups with real stories to tell. Unsurprisingly, a few common themes emerged in my discussions.

Observability – especially based on the OpenTelemetry standard – is perhaps the most prominent theme. Machine learning is a close second. Data, however, is the common thread that pulls these innovative companies together, even though they all take a differentiated approach to their offerings.

Here are the seven companies that made the cut:

Innovations in security and management

Privacera Inc.: Balancing data usability 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 governance space by focusing on data discovery, distributed policy enforcement, auditing and reporting across multiple cloud-based data sources, as well as fine-grained access control.

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

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

Deepfence Inc.: Cloud-native security observability

Cloud-native computing extends well beyond Kubernetes containerized deployments to comprise virtual machines, serverless and even bare-metal servers. Securing such a mélange of environments presents a modern challenge that Deepfence tackles head on.

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

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

Torii Labs Ltd.: Automated SaaS management

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

This profusion 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 with an end-to-end offering that discovers SaaS apps in use in an organization and then applies hundreds of automated workflows that deal with issues ranging from software license compliance to enforcing provisioning and deprovisioning policies to managing cloud spend.

What makes Torii intriguing: Given the preponderance of SaaS apps in today’s organizations, you’d think that SaaS management would be at the top of every CIO’s shopping list – but it’s not. Torii offers a service that most organizations need but few are as yet aware of.

Bringing machine learning to the masses

Aporia Inc.: Machine learning monitoring that brings AI to everyone

Machine learning has taken many enterprises by storm, but up until this point, extracting value from this technology has required a specialized skill set – skills that are rarer than hen’s teeth in today’s tight tech market.

Aporia helps address this predicament with an MLOps tool that is surprisingly easy to use. With Aporia, working with machine learning is now as straightforward as working with any of today’s mature analysis tools. On the flip side, Aporia provides a single pane of glass for those hard-to-find data scientists as well.

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

Monte Carlo Inc.: Machine learning-driven data observability

In 1999, NASA’s Mars Climate Orbiter famously experienced a mission-ending failure because of a simple mix-up between metric and English units. In other words, NASA had a fatal data reliability problem.

Data reliability centers on questions like whether your data are up to date, complete, within expected ranges, conform to expected schemas and other important considerations. A “no” answer to any of these questions can mean your data sets are broken.

Monte Carlo provides the observability necessary for data professionals to gauge their data reliability and to take action should their data sets break. The product works across data warehouses, data lakes, business intelligence tools and traditional extract/transform/load or ETL data sources, in and out of the cloud.

What makes Monte Carlo intriguing: The company leverages machine learning to uncover data reliability issues with a variety of different data sets – including those data sets whose purpose is training models. This “machine learning for machine learning” use case is still novel but is likely to become an established machine learning best practice.

Observability for engineers

Cortex: Observability for better engineering collaboration

Today’s frothy observability market focuses primarily on the needs of IT operators and system reliability engineers. Conventional wisdom states that software developers don’t need the insights that software telemetry can provide, because once they throw the code over the wall, well, it’s ops’ problem now.

Modern software development practices reject this supposed wisdom wholeheartedly, as engineers are responsible for ensuring their code works properly in production. Their observability requirements, however, differ from those of ops.

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

What makes Cortex intriguing: It supports modern deployment practices such as GitOps and shift-right techniques such as feature flagging and canary deployments, thus lowering the risk inherent in fast-moving development cadences.

Aspecto Inc.: Observability for developers of distributed services

Software development tooling generally focuses on the software components themselves. For complex distributed environments , including most Kubernetes-based deployments, the software is only part of the problem. The greater challenge: the connections among the software.

Aspecto provides OpenTelemetry-based observability that helps engineers deal with such distributed computing issues by providing visibility and insights 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 analyses and uncover root causes of issues impacting their code.

What makes Aspecto intriguing: By placing OpenTelemetry data into a graph database, Aspecto exposes the full power of graph-centric analysis to engineers as they assemble complex distributed interactions.

Vendors struggle for categorization and recognition

One 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 Aspecto’s observability for software development.

In other situations, the vendor’s core value proposition doesn’t neatly 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 one are important. Don’t let conventional in-the-box thinking prevent 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 transformation strategies. He wrote this article for SiliconANGLE. As of the time of writing, ServiceNow is an Intellyx customer. None of the other vendors mentioned in the article is an Intellyx customer.

Image: Bru-nO/Pixabay