As we continue to invest and deep dive into fast-moving frontiers such as artificial intelligence and enterprise data, we subscribe to the CEO of LinkedIn, Jeff Weiner’s proclamation that “Data really powers everything that we do.”
We are therefore excited to share our investment in the Seed round of UK-based, Streamkap, a platform to enable businesses to leverage their data in real time. Streamkap’s world-class founding team are seasoned, serial entrepreneurs with deep expertise in data infrastructure.
Market opportunity
As the volume of data created and replicated worldwide is increasing exponentially the success of modern businesses, from small-to-medium companies to Fortune 500 enterprises, is increasingly tied to their ability to extract valuable, timely insights and create superior user experiences from their data.
In a world where the demand for instant information is becoming non-negotiable for companies and their customers, core to this ability is access to consistently lower latency (fast) and lower cost data.
Problem
Streamkap offers an alternative to slow and expensive traditional methods of processing data periodically in batches (”batch ETL”). Batch data processing describes the concept of integrating data in batches at fixed intervals and can be used for things like processing last week’s sales data to compile a departmental report. Major companies have been built in this space, that have simplified the batch process e.g. Fivetran and Airbyte. However, in the realm of data, batch processing is still akin to waiting for the daily newspaper to be printed and delivered, whilst real-time stream data processing is like having instant newsfeeds, where information is delivered live for instant usage. Furthermore, the cost of batch ETL does not scale well with increased volumes of data, becoming cost-prohibitive for many businesses at large volumes.
For large-scale enterprises, open-source platforms such as Apache Kafka and Pulsar, and even companies like Confluent, have historically provided some of the underlying infrastructure and tooling for companies to set up real-time data pipelines. Examples might include Netflix/Spotify (e.g. streaming data to feed content recommendation engines in real-time) or Walmart (e.g. streaming data to track inventory in real-time so it has enough stock to meet demand). However, they often require long, complex, expensive technical integrations and ongoing maintenance.
However, the vast majority of businesses don’t have millions of dollars, and huge data teams to implement and maintain their own streaming data pipelines.
Vision & Solution
Enter Streamkap. Streamkap removes the currently complex process of achieving lower latency and lower data costs with a real-time ‘streaming’ data SaaS platform. In combining the simplicity of batch processing with the lightning-fast speed of streaming, Streamkap empowers businesses with an economic alternative to implement a streaming data solution and enable smarter decisions, faster.
In just a few hours, Streamkap customers can set up their data streaming pipeline (converting raw data from diverse sources into a comprehensible format so that it can be stored, analysed, and utilised). Real-time data means realtime app interactions for customers, real-time ranking and recommendations (enabling businesses to respond to instant virality), real-time business adjustments, and real-time machine learning. This can mean access to data that can be ~30x faster rate and are 5-10x cheaper than current batch options.
Why we invested:
Exceptional mix of technical and commercial founding team:
- These founders are seasoned software operators, and multi-time founders with valuable learnings from prior companies. In fact, having built and sold their previous company, a solution focused on the batch data space, the Streamkap founders have a deep technical and commercial understanding of batch processing, including its limitations and how slow and expensive to scale it can be. All they kept hearing from their prior company’s customers was that they wanted their data faster and cheaper - they have since gone on to found Streamkap.
- Paul has exceptional pedigree in the SaaS space, having spent 17 years as a software sales exec, spending 8 years at BrightEdge (backed by Insight Partners / Battery Ventures) where he led the sales team from their early days to over $100M in revenue during his time with the company.
- Co-Founder and CTO, Ricky Thomas, is a seasoned CTO, having spent 20 years working in technical / data teams, having previously served as CTO of IG Index, part of IG Group (GBP 2.5B market cap trading / investment platform).
Large market tailwinds
- Global organisational spending on big data analytics and infrastructure has topped more than $180 billion and continues to grow every year.
- 90% of business professionals and enterprise analytics teams say data and analytics are key to their organisation's digital transformation initiatives.
- Furthermore the global streaming analytics market size is expected to grow from ~$20.3 billion in 2022 to ~$222.6 billion by 2032, growing at a CAGR of 27.04%.
- A key driver of market growth is the increased implementation of new technologies such as big data, the Internet of Things (IoT), and AI, where we’ve been spending much more time as an investment team.
Abstracting complexity away from data engineering
- Despite the growing awareness of how important data is to their businesses, there is still significant friction within organisations to leverage data for their needs.
- Data scientists and business users will often wait days to weeks for data engineers to build the right pipelines to be able to operationalise their data. Even when these pipelines are developed, they require maintenance to ensure stability and scalability.
- Distilling down the complexity, enabling real-time data integrations and empowering data teams to focus on their core capabilities of analysing and utilising real-time data is an exciting opportunity for Streamkap.
Pick and shovel SaaS as part of AI trend
- AI model training has historically been trained by batch, however we have observed online training for Artificial Intelligence and LLMs is becoming a significant trend, with a push to make models more “realtime” - with low latency, low cost, frictionless data ingestion to be a core driver to that.
- Data quality in most datasets is also correlated to ephemerality: i.e. recent is better, as more recent data is generally better at predicting the future.
- As a SaaS platform that enables customers to get their data streaming pipeline up and running fast, we believe the Streamkap platform has the potential to provide the opportunity for businesses to truly leverage their data in real-time, and capitalise on the advances and tangible benefits of AI.
We are delighted to partner with this seasoned team of operators early in their journey. With strong tailwinds behind real-time data streaming, and good validation from early customers, we feel there is a great opportunity ahead for this team who have a unique insight into simplification in data infrastructure, for the ultimate benefit of the customer.
Safe Travels!
Seamus and the TEN13 Team