Join top executives in San Francisco on July 11-12, to hear how leaders are integrating and optimizing AI investments for success. Learn More

Before the pandemic, AI and analytics technology adoption was elusive in the enterprise, with Gartner estimating that it reached only 30% of employees in 2017. That changed — to an extent — after the health crises forced companies to digitize their operations, making data easier to access and analyze than before. Still, enterprises face hurdles in adopting analytics, with McKinsey reporting that fewer than 20% of companies have maximized the potential of analytics and achieved it at scale as of 2021.

According to IDC, one major reason for the poor success rate of data and analytics projects is the lack of stakeholder buy-in. Another is insufficient tools and data infrastructure. At least, that’s the assertion of Eric Sammer, the CEO of Decodable, which hosts a real-time data engineering platform. Decodable aims to provide data services including ingestion, integration, and analysis while reducing the need to write code.

Real-time data

A number of startups deliver solutions for real-time analytics, including Imply and Observable. There’s also Heap, an analytics vendor for digital brands, and real-time data streaming and analytics services provider StreamNative. Allied Market Research predicts that the global streaming analytics market — the processing and analyzing of data records continuously rather than in batches — will be worth $52 .19 billion by 2027.

Decodable arrived on the scene several months ago. Founded by Sammer, it connects to existing data infrastructure including operational and analytical databases, allowing users to define data streams just as they would tables in a database. Decodable can process data in real time by building pipelines in structured query language, the language used to manage data held in relational database management systems.


Transform 2023

Join us in San Francisco on July 11-12, where top executives will share how they have integrated and optimized AI investments for success and avoided common pitfalls.


Register Now

“The pandemic has created an explosion of real-time data due to the virtual and online demands … [B]ut the complexity of building and managing a real-time data platform built from discrete messaging and infrastructure components requires dedicated, specialist teams and skills, Sammer told VentureBeat via email. “Developers also rely on the data teams to provide access to data in a specific form to make their apps work. For many companies, this is still too high a bar to move off their batch-oriented … pipelines. Realistically, this level of investment and ongoing cost is only feasible for larger organizations who have the scale to justify dedicated teams — but even then, it’s slow and expensive, adding friction and delays to realizing business value from data and apps.”

Decodable can parse, filter, and route data, converting and reprocessing it to different formats as needed, Sammer explained. Built on Apache Flink, an open source unified stream processing and batch processing framework developed by the Apache Software Foundation, Decodable runs in the cloud and can optionally mask personally identifiable information data to create anonymized streams.

“Decodable makes real-time data available to a wide range of companies who, due to complexity and skills needed, weren’t able to take advantage of the technology and had to rely on traditional batch techniques,” Sammer told VentureBeat via email. “Because Decodable is a fully managed service, there’s no messaging platform to build, deploy and manage. Developers can just connect data sources and sinks and then use their … skills to transform data flowing in real time.”

Analytics challenges

Adopting real-time analytics — which can help to gather and draw conclusions from vast amounts of data — is highly desirable for businesses. According to a 2016 SingleStore survey, 80% of companies see real-time analytics as a top priority. A 2019 NewVantage report found that 55% are spending more than $50 million on it.

But, as mentioned at the top, analytics implementation remains a challenge. In the NewVantage survey, 77% of companies said that adoption of big data and AI initiatives poses technical and talent issues. Another recent poll found that complexity and a lack of experience are often the two greatest barriers to building streaming data pipelines.

“While most leaders across industries agree that real-time reporting is overwhelmingly advantageous, there are issues to be aware of when considering adopting real-time data reporting,” Salesforce — which has a horse in the race, it must be said, given its large analytics catalog — wrote in a blog post. “Cost, of course, will be a major concern for many businesses, but aside from cost, many businesses report resistance of management and employees to shift their mindsets, as well as having data quality concerns, and lack of a data strategy.

But Sammer argues that Decodable is positioned to ease the burden, despite the roadblocks. “We are working with a number of beta customers — any application that requires real-time data is essentially a potential customer for Decodable. Our current beta customers span from start-ups to large enterprises to government agencies,” he said. “We also work with the vast majority of messaging and data platform partners. In addition, we partner with real-time Analytics vendors to ensure a seamless approach to access of real-time data for further analysis.”

Twelve-employee Decodable today announced that it raised $20 million in series A funding led by Venrock and Bain Capital Ventures with participation from individual investors. The round follows a seed round of $5.5 million, led by Bain Capital Ventures, and brings the total funding to $25.5 million.

VentureBeat's mission is to be a digital town square for technical decision-makers to gain knowledge about transformative enterprise technology and transact. Discover our Briefings.