The COVID-19 pandemic revealed the value of data and analytics.
With economic conditions suddenly in flux, organizations needed to find a way to both react quickly and also act preemptively whenever possible by anticipating the next change.
Analytics was that way.
As the pandemic progressed, that value of data and analytics began to translate to the financial markets. And as a result, one of the major analytics trends that emerged during the latter half of the pandemic, stretching from late 2020 through the first six months of 2021, has been the often surprising valuations the financial markets have placed on a variety of data and analytics vendors.
Stock prices have soared, and funding totals have exploded.
"A trend that has emerged and been confirmed is the trend around data," said David Menninger, senior vice president and research director at Ventana Research. "If you look at [valuations], it really highlights the importance of data and its continued evolution beyond just traditional, relational data."
But a broad recognition of the value of data and analytics isn't the only trend that emerged over the latter part of 2020 and first six months of 2021.
Self-service BI has been around for years but become more vital than ever due to the increase in remote work during the pandemic. And similarly, though augmented intelligence capabilities have been part of BI and analytics platforms a while, the importance of AI has been amplified with more business analysts working from home.
Cloud data warehouse and analytics platform vendor Snowflake filed for its initial public stock offering in June 2020, estimating an opening price of between $75 and $85 per share.
Demand, however, was so intense that it wound up going public at $120 per share in September 2020, and the $3.4 billion the data cloud vendor raised in its IPO shattered the previous record for tech companies.
Snowflake's stock eventually reached a high of $429 per share, and is now trading at about $240, double the price of its IPO.
Just recently, in late June 2021, Confluent, a vendor specializing in streaming data management, raised $800 million by going public at $36 per share -- above its expected range of $29 to $33 per share -- and its stock price quickly rose to $57.99 per share before settling back around $43.
Databricks could be the next tech company to launch a huge IPO. In the meantime, however, it continues to raise private equity funding, and in June 2021 the augmented analytics and machine learning specialist raised $1 billion in Series G financing.
Meanwhile, among the few publicly traded independent analytics vendors, MicroStrategy's stock price is up from $110.33 per share on March 13, 2020 to over $600, and Domo's spiked dramatically as well.
David MenningerSenior vice president and research director, Ventana Research
"If you look at the Databricks funding, the Confluent IPO and the Snowflake success, it points to and confirms that data is an important asset for nearly every organization, and that the nature and form of that data is changing," Menninger said.
The way data is changing, he continued, is that the lines between BI vendors and data management vendors is blurring. The same goes for analytics vendors and AI and machine learning (ML) specialists.
Now, vendors are offering a wider variety of analytics capabilities, attempting to add value by enabling users to navigate the entire analytics process from data capture all the way through insight.
Snowflake, for example, continues to expand beyond its warehouse capabilities and now calls itself a data cloud vendor, and ThoughtSpot, with a strategic shift away from its on-premises clientele, now bills itself as an analytics cloud.
Like Menninger, Wayne Eckerson, founder and principal consultant of Eckerson Group, said that the market valuations of Snowflake and Databricks have been a surprise, and reveal the recognition of the value of data and analytics.
"Snowflake's continued momentum is pretty darn impressive," he said. "They're plowing a huge trough through the industry and putting all their competitors on their heels. Databricks looks like it might be a formidable competitor."
Eckerson added that Alteryx is another vendor blurring the lines between its historical data management platform and broader capabilities. The vendor's stock, however, has not increased in value.
"All this stuff is coming together at Alteryx," Eckerson said. "They're essentially trying to be a unified data and analytics platform and having some success with it while they're moving to the cloud."
While the pandemic revealed the value of data and analytics, it also intensified the need for self-service capabilities.
Conceptually, self-service analytics is nothing new. Vendors have long attempted to enable users of all skill levels -- not just those formally trained in data science and data analysis -- to use data and analytics in their roles.
Executing that, however, has been a challenge.
But with centralized offices shut down for months during the first half of the pandemic and many still not fully open -- and many workers now choosing to remain remote -- end users needed to be more autonomous than when they could simply ask the person sitting next to them, or the IT department down the hall, for help.
In response, the addition of new features to enable self-service analytics has been common among vendors through the first half of 2021.
GoodData, for example, unveiled a complete platform overhaul in April 2021 with self-service capabilities built as a series of cloud-based microservices at its core. In May, Logi Analytics rolled out the latest version of Composer, its flagship platform for application development, and expanded self-service capabilities featured prominently.
And in June, Tableau, one of the leaders of the self-service movement, unveiled enhancements to Ask Data and Explain Data -- AI-fueled capabilities that make it easy to query and get insights from data -- in its 2021.2 platform update that made the tools easier for business analysts to use.
"I think the pandemic really forced organizations' hand in making sure self-service was available and being used -- they didn't have a choice, to some extent," said Mike Leone, senior analyst at Enterprise Strategy Group. "I think, especially over the last year, the hype around self-service was forced to become a reality."
Self-service, Leone continued, has become so important during the pandemic that if vendors don't give end users the tools to use data and analytics in their roles, they risk being passed over in favor of other vendors who are making self-service a priority.
"If you don't have self-service tied to your product from a marketing standpoint, it's not even on the consideration list," he said. "It's about empowering as many people as possible. Self-service really needs to be there on the list of capabilities."
Rise of the machines
Despite its increased importance as a result of the pandemic, self-service analytics doesn't exist in a vacuum.
Something has to enable self-service capabilities. Something has to remove the barriers that have traditionally prevented untrained users from working with data and lower the level of expertise needed to realize the value of analytics.
That something is AI.
AI is what enables Logi's no-code application development platform, and Tableau's query and insight capabilities. It's what fueled Yellowfin's July update of Stories, a data storytelling tool that automatically develops narratives about data to help untrained business analysts understand their data and subsequently act on that data.
Better AI leads to more autonomy, and one of the goals of just about every analytics vendor to simplify the process of working with data.
"What I've seen continue throughout the first half of this year is the automatic application of AI and ML to data," Menninger said. "Some vendors call it insights, others call it driver analysis, but it's the notion that you don't need data scientists to get some analysis of your data."
Beyond self-service, improved AI capabilities are enabling more data science, according to analysts.
The proliferation of automated machine learning (AutoML) tools -- for example, Alteryx and Domo added such capabilities in 2021 -- are helping both trained data scientists and untrained users.
With AutoML, some vendors are enabling users to develop predictive models without having to write code. Meanwhile, data scientists are able to build models more quickly without having to write code, and with business users doing some of the modeling work themselves, the data scientists have more time to concentrate on their own projects.
"We're starting to see these AutoML tools," Eckerson said. "Data scientists use them to help point them in the right direction and try out a bunch of models quickly and give them a head start on a project, and a citizen data scientist can use it for more simple types of projects."
Meanwhile, in order to quickly add AI and ML capabilities and enable users to get more value out of their analytics operations, vendors are acquiring AI companies.
ThoughtSpot, for example, acquired SeekWell and Diyotta during the spring, Logi bought Izenda in April (after getting acquired by InsightSoftware) and Qlik has been acquiring companies to add AI capabilities for a few years now.
Ultimately, adding AI and ML capabilities comes down to assisting users. The more vendors can automate, and the more they can eliminate barriers such as the need to code, the more potential users there will be.
"I don't anticipate that AI and ML will become entirely automatic, but in the near term this is about assisting people with AI and ML in their analysis and manipulation of data," Menninger said.
Beyond the increase in money flowing into analytics, a renewed emphasis on self-service capabilities resulting from the pandemic and the continued improvement of AI and ML capabilities, a host of other trends continue to influence BI and analytics vendors.
SaaS adoption and real-time analytics are leading trends, according to Dan Sommer, senior director and market intelligence lead at Qlik, speaking at the vendor's virtual user conference in May.
Andrew Beers, CTO at Tableau, said during a virtual user conference in June that a single interface for working with data, embedded analytics and the use of AI and ML for data science are key trends.
And embedded analytics indeed remains a driving force, according to Leone.
"That's a big one going forward," he said. "It's tied to self-service, but it incorporates some different personas. You start involving operations teams, developers, and it's really about the empowerment of different business units and being able to deliver not just a data visualization but advanced analytics capabilities to a broad organization."
Menninger, meanwhile, echoed Sommer and noted that real-time BI remains an important means of increasing the value of analytics.
"The whole notion of data in motion will continue to be a trend," he said. "Whether you call it streaming data, event data processing, data in motion, real-time data -- whatever you want to call it -- we're not done yet transitioning to a new world where data in motion is the primary and initial data."
Menninger added that data governance is always crucial. And along those lines, Eckerson said that the rise of data operations (DataOps) has been an important trend as organizations attempt to get the most value out of their analytics programs.
"The whole DataOps movement is very compelling," he said. "What DataOps means is we're going to move from the Wild West and an artisanal approach to building data management solutions, data pipelines, to a more industrial approach where we can scale up teams instead of just having individuals build stuff. That's very promising."
Enterprise Strategy Group is a division of TechTarget.