Analytics startups with staying power bring something new to the market.

Some of these promising newcomers include: Avo, Bigeye, DataKitchen, Deepnote, Dhiva.ai, Hex Technologies, Promethium and Rupert.

A decade or so ago, Domo and ThoughtSpot were analytics startups. Domo was built for the cloud right from the start, which was unique at the time. And ThoughtSpot developed its platform around natural language query technology (NLQ), also unique at the time.

Now, both are established vendors, competing for customers with bigger vendors such as Tableau, Qlik and Microsoft Power BI, among others.

A handful of years ago, Toucan Toco was just getting started, and a few years after that, Sisu emerged from stealth.

Toucan Toco specialized in automated data storytelling, which was not yet common. Sisu hit the market in 2019 with a platform using augmented intelligence and machine learning to monitor key business metrics, detect anomalies in real time and automatically explain why they happened in ways no one else was yet doing.

Now, they too are more established, with Toucan Toco building out its capabilities to become a full-fledged BI platform and Sisu adding new ways of examining why things are happening and enabling users to discover what to do next.

As Domo and ThoughtSpot move beyond the startup phase, and Toucan Toco and Sisu mature, a new generation of analytics startups is emerging as 2021 winds down and 2022 approaches, each of them doing things a little differently than most before them.

 

 

Here's a look at the eight analytics startups to watch in 2022, according to industry insiders, including analysts, a consultant and a venture capital investor.

Avo: Founded in 2018 and based in Walnut, Calif., Avo develops a platform that uses analytics to improve, well, analytics.

The startup bills itself as an analytics governance tool with a tracking plan interface that uses AI to enable the standardization of event schemas, workflows and peer reviews to keep an organization's data stakeholders informed of their operations.

While most analytics platforms aim to provide a better view of business processes -- including finances, healthcare, sales and marketing -- Avo's platform is aimed at product managers, application developers and data teams to enable them to plan, track and govern their organization's product analysis.

"They are using AI to analyze product development and enable you to do better product development," said Donald Farmer, founder and principal of TreeHive Strategy. "It's product analytics, but with AI it's taking it a step further, and it's not about marketing but it's about developing a product. It's about product design and product specification and based on analysis of data. I think it's fascinating."

He added that it's taken some time for Avo to mature, but after raising $3 million in funding in September 2020 to bring its total funding to $4.5 million, it's now starting to reach its potential.

"[Avo has] been around for a few years and are really starting to get traction," Farmer said. "They have reached a tipping point where they're realizing their promise."

 

Bigeye: Founded in 2019 and based in San Francisco, Bigeye focuses on DataOps.

DataOps -- data operations -- is an offshoot of DevOps, and is aimed at making the data-driven decision-making process more accurate and agile.

The analytics startup, which has already raised $66 million in funding, offers a platform built to address the reliability of data so those analyzing the data can trust what they see and feel confident in the insights and actions that result from their data.

Bigeye automates data observability, automatically detects anomalies and speeds up root cause analysis, all as part of that process of building trust in data.

"They're right in that space of making things happen over and over again," said David Menninger, an analyst at Ventana Research. "We've created this world where we have all this data, we get it quickly, and we want to be agile with that information, but when there's a change in a business model or process, the infrastructure isn't in place to modify our analytics in response."

DataOps -- and platform such as Bigeye -- provide that operational infrastructure that ultimately leads to agility.

"It's really important from the aspect of both governance and agility," Menninger said. "You want to be able to do things and you want to be able to do them in an automated way, and you don't want to end up in the spreadsheet world again where everything produces an error. That's what these folks are trying to address."

 

DataKitchen: Founded in 2013 and based in Cambridge, Mass., DataKitchen isn't a recent analytics startup, but its area of expertise is gaining momentum.

Like BigEye, DataKitchen focuses on DataOps. Its platform automates data workflows to attempt to simplify complex processes while enabling users to quickly test and deliver reports, dashboards and data models that lead to insight and action.

"DataKitchen looks pretty interesting," Menninger said. "They're really focused on DataOps, but that should span analytics ops as well, and they see that. That whole notion of making analytics more agile still requires more work on the part of the industry [and they address that]."

Now, as DataOps gains momentum and analytics ops develops, new personas such as DataOps engineers are emerging within organizations.

 

 

Deepnote and Hex Technologies: Founded in 2019 and based in San Francisco, Deepnote and Hex Technologies are notebook vendors.

Notebooks are tools traditionally used by data scientists as they experiment with and explore data. Using notebooks, data scientists collaborate as they write and execute code, view the results of their queries and share insights.

But as BI and analytics platforms have advanced and platforms have enabled non-technical users to explore and curate data with augmented intelligence tools -- some, like automated machine learning (autoML), even enabling data science -- notebooks have become less popular.

However, Rajko Radovanović, a venture investor at New Enterprise Associates, a venture capital investment firm, said that as some business users become more proficient and pick up more of the skills of trained data scientists and data analysts, including some coding capabilities, a renewed interest in notebooks is developing.

Deepnote and Hex Technologies are taking advantage of that trend.

"As the average enterprise business and data analysts become more advanced data users and more code-proficient, we believe we will see a renaissance of data notebooks within the data analytics context, not just in the data science world where they have reigned to date."

The new data notebooks, however, will have to be a bit different to cater to new data worker roles, Radovanović continued. They'll need to be easier to use than their predecessors, among other advancements.

"Notebooks will require better … hosting, environment management, collaborative features and versioning, connectivity to enterprise data stores, SQL editors and better integrated visualization capabilities," he said. "Hex and Deepnote are two examples of next-gen notebooks we are excited about."

 

Dhiva.ai: Founded in 2017 and based in Pittsburgh, Dhiva.ai offers a broad set of analytics capabilities built on automation and AI.

Dhi is a Sanskrit word meaning intelligence, and the company's name translates to "intelligent virtual analyst," according to the vendor's website.

Like traditional BI vendors, it offers data visualization capabilities. But beyond traditional BI capabilities, it adds automated data storytelling, automated text summarization, embedded machine learning model capabilities and natural language query with a tool called Ask Dhiva.

Using Ask Dhiva, customers can ask both "what" and "why" questions of their data, and the tool returns answers within seconds.

"One startup that really peaked my interest was Dhiva.ai," said Mike Leone, an analyst at Enterprise Strategy Group. "They are focused on delivering a next-generation, AI-centric BI platform that incorporates no-code and self-service."

Enabling self-service analytics without requiring users to know how to write code has the potential to broaden the use of analytics within organizations.

Depending on the source, it's estimated that fewer than a third of employees at most organizations use analytics in their jobs, and technologies such as NLQ and automated data storytelling might be the means to significantly increasing that number.

 

Promethium: Founded in 2018 and based in Menlo Park, Calif, Promethium is a data management vendor that also enables analytics with AI capabilities.

Using the analytics startup's platform, customers can automate the data preparation process and then query and analyze data using natural language rather than code. The aim of the platform, according to Promethium, is to reduce the complexity of data management and analytics to improve productivity and shorten the time needed to reach insights that lead to data-driven decisions.

NLQ technology, meanwhile, is gaining momentum.

ThoughtSpot was a pioneer in this area, and vendors such as Tableau with its Ask Data feature and Qlik have offered some NLQ capabilities for a few years. Recently, a flurry of vendors have added new NLQ tools, including AWS with the introduction of QuickSight Q to its QuickSight BI platform in September and Yellowfin with the addition of Guided Natural Language Query on Dec. 4.

"Promethium is a no-code data discovery tool, and no-code/low-code is becoming really important," Farmer said. "There's no one quite like them."

Beyond its no-code/low-code capabilities, Promethium is also tapping into the growing trend surrounding automation of analytics processes.

Qlik, Tableau and Alteryx are among the analytics vendors who have expanded their automation capabilities with new tools and partnerships with robotic process automation vendors such as UiPath, while data management vendor Alteryx has made automation a pillar of its entire platform. 

 

Rupert: Founded in 2019 and based in New York, the vendor is yet another analytics startup whose platform uses AI and automation to help users reach insights.

Rupert, however, is highly focused on not only making sure users are able to access data and get insights quickly and easily, but also doing so at the right time. That means working with data within their workflows rather than in a BI platform's environment, and it means accessing data related to that workflow.

For example, users can be collaborating with colleagues in Slack, come up with a question, and, using natural language, query their data and receive relevant data assets in return that enable them to come up with answers to their question and make data-driven decisions.

In addition, because it doesn't require users to write code to query data, like Dhiva.ai, Rupert attempts to broaden the use of analytics within organizations.

"Driving business user engagement with data and analytics within the enterprise remains one the most persistent and high value problems in analytics," Radovanović said. "Rupert is addressing this with a highly unique and novel approach that enables business users to ask questions in normal text, and in return get pointed to the most relevant existing data assets within their company."

 

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