Omni looks to take on Looker with its cloud-powered BI platform
Business intelligence (BI) solutions, or tools that evaluate and transform unprocessed data into information for use in decision-making, have proliferated in recent years. Even if investments in them are increasing, some survey data suggests that organisations are still having difficulty becoming “data-driven.” Less than half (47.4%) of chief data and analytics officers surveyed by NewVantage Partners in 2022 thought they were competing on the basis of data and analytics. Concerns about data ownership and privacy were listed alongside the excessive expansion of data as the main barriers, along with organisational culture.
Poor usability, according to Colin Zima, is a significant obstacle that companies using BI technologies must overcome. He is the co-founder and CEO of Omni, a business intelligence platform designed to make working with data easier for everyone in a company. Zima might not be entirely objective as a result. On the other hand, he has a lengthy history of involvement in the data analytics field, having held positions at Looker as the chief analytics officer and vice president of product as well as at Google, where he served on the Search quality team.
“Getting the fundamentals done is still much too hard in an era where every person is supposed to be a data user,” said Zima. “Looking up data across many different systems, waiting for the data team to fetch data, or being pushed to learn structured query language (SQL) to answer inquiries.” Reality: “Data teams need strong tools to manage that process and execute high-value work that complements basic reporting, and business users need terrific, straightforward tools to accomplish their tasks better.”
Early in 2022, Zima co-founded Omni with Jamie Davidson and Chris Merrick, who had previously worked at Looker and Stitch, respectively. The three co-founders were motivated by a shared desire to create a product that facilitated “high-value” work that complemented core business reporting processes for data teams, according to Zima.
When using a centralised platform, people must make some hard trade-offs since it is difficult to make adjustments. As a result, people supplement with analyst tools or other point solutions, and this fragmentation has only grown. As a result, there are trade-offs and conflict between business teams and data specialists, or between those who want to move rapidly and your board reporting, according to Zima. “While older BI technologies brought teams together around trustworthy, centralised data, a lengthy upfront data modelling process was still required. With Omni, we bridge the gap between analytics that can be used right away and those that have the stability and governance of established corporate BI.
Investors have committed $26.9 million to Omni, including a seed round with participation from Box Group, Quiet, and Scribble and a $17.5 million Series A financing headed by Redpoint with involvement from First Round and GV. Approximately $100 million is the post-money worth of Omni, according to a source with knowledge of the situation. As for the money, Zima stated that it will go into marketing initiatives because he believes Omni hasn’t yet used the initial money.
According to Zima, Omni is equivalent to current BI solutions like the aforementioned Looker and Tableau. The platform, however, also has the ability to simulate raw SQL, the language used to interact with databases. A “sandbox” data model is created using Omni’s built-in tools from SQL, allowing users to promote metrics to the official, shared model that the entire business may utilise. Beyond this, Omni uses “automatic aggregates” to speed up inquiries and control user fees (and their employers).
The sacrifice that the majority of businesses are obliged to make with centralised, monolithic BI solutions is that they impede employees’ and teams’ ability to operate beyond the main channels. This leaves two options: not utilising data or using shadow IT, such as Excel, or solitary analytical tools, to finish a workflow, according to Zima. To prove his claim, research indicates that about 50% of firms have trouble using and gaining access to high-quality data. Omni is creating a system that offers IT greater control by supporting controlled decentralisation rather than merely spinning up separate tools to tackle problems. This is done through bridging the gap between IT and business divisions.
In the end, this implies that all of that business logic and data control can be preserved, monitored, and carefully incorporated into core systems rather than left on islands by IT and data teams.
Although Zima claims that Omni was shielded in many respects due to its founders’ long-standing contacts with Omni’s backers, starting a business during a recession is difficult. Zima claims that employment and client acquisition would be the main priorities this year regardless of the state of the overall economy. Prior to today, which marks the platform’s public launch, Omni has only collaborated with five development partners. Currently, Omni employs roughly 16 people, and by 2023, it hopes to increase that figure by 25%.
Maintaining growth against rivals like Y42, Metabase, and MachEye—the last of which raised $4.6 million in seed funding two years ago—will be difficult. Pyramid Analytics, a corporate intelligence and analytics company that raised $120 million in May, is more powerful. Noogata, Fractal Analytics, Tredence, LatentView, and Mu Sigma are more options.
Zima, for his part, anticipates that as businesses attempt to combine their tools and “streamline their data stacks,” the downturn in the market would benefit Omni at the expense of competitors.
“[Omni] is the only BI platform that combines the independence of SQL with the consistency of a common data schema… “[And] permits this positive feedback loop between the governed model and one-off speed work,” Zima said. One of the primary tenets of our argument is that business intelligence still has a major issue in combining and addressing the full surface area, which is largely composed of point solutions. As the cloud data revolution takes hold, new, ambitious options like proactive performance optimization become possible.