Updated: Feb 24
The era of real-time analytics is now.
But the inspiration to tackle the challenge of building powerful real-time analytics has been 10 years in the making. It started in September 2012 with a frantic customer-service call when I was the VP of Engineering for SAP BusinessObjects.
“Ting, we’ve got a burning issue this time! Our customer desperately needs immediate feedback on delivery orders, but is getting stuck in data queries and literally can’t make any decisions now!”
The customer, a leading New York-based fresh-food delivery company, had built an operations center to track and monitor deliveries effectively. To the client, the promise of real-time analytics was mission-critical to ensure top-tier customer service and to maximize efficiency. Their operations team worked hard to collect various live events and data from traffic, fleet, route, e-commerce, and membership system to gain complete real-time visibility for each delivery.
However, implementing such “latency-sensitive” analytics effectively in 2012 was a real challenge. Mainstream products focused on transaction data and related management, reporting and analysis. Reliable event hubs for collecting streaming data effectively - Kafka, Pulsar and Kinesis - weren’t yet well established. Purpose-built real-time data infrastructure basically didn’t exist. To build those capabilities, you needed to put together a bespoke solution. And even then, systems struggled with high failure rates. Lapses in performance were frequent and unpredictable. Traditional infrastructure made real-time analytics inefficient, glitchy and expensive. The end result was hitting a wall, like my fresh-food project.
When I moved to Splunk in 2013, I once again had the opportunity to work on operational analytics. But I again found that many vendors’ approaches to real-time analytics were hybrid and compromised - still very much dependent on a traditional database-centric approach. Solutions with (what I saw as) glaring structural flaws wouldn’t be able to solve these “first-order” problems in enterprise software. There was a huge opportunity to build an innovative product to solve big, complex infrastructure problems. I was fascinated.
Fast forward to 2022, and the imperative for real-time analytics has only become more compelling. Data creation and replication have grown exponentially, driven by the proliferation of IoT, Hybrid Cloud, and Core-Edge-Endpoint. IDC predicts that by 2025 the “Global Datasphere” will grow from 64.2ZB in 2020 to 181ZB (CAGR 23%), and real-time data will account for a whopping 30% of all data. Gartner forecasts that by 2022, more than 50% of enterprise scenarios will require real-time data analysis to improve the efficiency of decision-making operations. At the same time, leading technology platforms have improved their ability to collect and route streaming events by various event hubs and gateways. Visionary leaders like Netflix and Uber have invested large amounts of time and capital to develop sophisticated real-time functionality, resulting in radically improved customer experience, clearer operational visibility and increased processing throughput. (1)(2)
But not all enterprises have the resources of Netflix. And despite clear benefits, we’ve found the vast majority of enterprises struggle to launch effective real-time capabilities within their organizations. Underlying data stacks are often still built for general-purpose and batch-based historical analytics. The whole data process - from ingestion to analytics - just isn’t designed for real-time streaming. It’s hard to deploy real-time functionality on top of existing platforms, and as a result adaption of effective continuous data intelligence remains low. So while the business logic for real-time analytics is strong, many enterprises find it increasingly difficult to keep pace with growing data volumes and business needs.
That’s why we developed Timeplus.
During our initial customer discussions, we heard consistently how business units wanted real-time capabilities, but it was challenging to do effectively especially within budget constraints. The deeper we dug, the messier we saw things were! To solve the problem, we really needed to rework the entire analytics stack to optimize for real-time. We saw what enterprises really needed is a turnkey solution that is easy to implement, while still providing powerful analytic capabilities. So that’s what we’ve built.
Fast + Powerful Real-time Analytics Made Intuitive
Timeplus is a purpose-built streaming analytics platform that solves enterprises' need for easy-to-implement real-time analytics. Timeplus adapts a streaming-first architecture to redesign real-time analytics from ingestion to action, helping enterprises analyze massive sets of streaming data faster. We provide a dynamic schema for real-time analytics, bringing unprecedented flexibility to data querying and processing. This empowers enterprises to extract substantial value from data before it goes obsolete. Timeplus is unique in its features and functionality, enabling users to make real-time analytics:
Fast: Users can run lightning-fast analytics with ultra-low latency, while ensuring extremely high EPS (events-per-second), both with ingestion and query simultaneously. Our testing demonstrates highly compelling results: Timeplus can achieve 4 millisecond end-to-end latency, and 10 million + EPS benchmark even in a single commodity machine.
Powerful: Users can quickly analyze real-time streaming data, while simultaneously connecting to historical data assets. We use a converged multi-tier computation engine, which reduces data redundancy while substantially lowering costs. We’ve developed powerful real-time streaming analytics that enable functionality such as windowing/non-windowing, late event, downsampling and streaming predictive analytics. All from one SQL query.
Intuitive: Users get speed, ease-of-use, and advanced analytics functions, both in the cloud or at the edge, and can quickly act on data simultaneously as it arrives. Once various data sources are connected, users can immediately explore streaming patterns via query and visualization, and create real-time multi-channel notifications, or send insights or aggregated data to the downstream systems. Powered by ultra-low latency of streaming processing, HFR (High-Frame-Rate) charts and dashboards can automatically update at real-time, so users can say goodbye to refresh and reload, and other slow and cumbersome user experiences.
Purpose-built to tackle industries where real-time analytics make all the difference
In the not-so-distant future, all enterprises will need to be “real-time enterprises” to compete. For now, we are focusing on solving problems where “there are high value processes or parts moving extremely fast”, as our investor Rory Sexton puts it. In other words, we’re focusing on solving problems where high latency is very expensive. We are current working on use cases that address:
Logistics, Delivery and Manufacturing: Timeplus makes it frictionless to conduct predictive maintenance and vehicle diagnostics on transportation fleets. By providing powerful analytics at the edge, companies can monitor delivery schedules in real-time and improve logistics visibility.
Financial Services: Timeplus helps financial organizations build robust fraud detection and prevention infrastructure. With our real-time fraud detection solution, companies can vet transactions instantly, detecting anomalies that signal fraud and stopping fraudulent transactions before they occur.
Observability: With Timeplus, real-time streaming data becomes the heart of observability. Say goodbye to the long process lags from ingestion, processing, storage and indexing of traditional log-based analytics. With Timeplus, teams can instantly identify anomalous behaviors or suspicious activities and flag them for immediate investigation so they can solve issues and minimize damage.
Sales & Marketing: We enable users to integrate historical customer engagement data with live streams of customer actions seamlessly. Marketers can create personalized recommendations in real time, delivering targeted customer experiences across multiple channels in milliseconds.
Who We Are
Our vision is to create an analytics platform where time is treated as a first-class citizen in variable prioritization. So “time” felt like a natural foundation for our name. With time as our fundamental focus, we will continuously build out additional functionality to better serve enterprises as they compete. Time plus speed. Time plus intelligence. Time plus results. Timeplus.
I am excited to have assembled an outstanding team of engineers, executives, investors and advisors. My co-founders Will Plummer (COO), Gang Tao (CTO), Ken Chen (Architect), Jove Zhong (Product) and James Hao (Engineering) have a great blend of enterprise-scale cloud and big data experience from companies like AWS, Splunk, and SAP. I am also grateful for the support of some of the world’s best venture capitalists and individual technologists, having raised an initial US$4m from an amazing group including Jeremy Kranz, Head of TMT investing for GIC, Rory Sexton, formerly VP of Apple, who was responsible for building Apple’s global supply chain capabilities over the last 20 years, and Richard Tibbetts, a renowned pioneer of real-time streaming database technologies. In addition, we are honored to welcome Margaret Lee, SVP at BMC, GM of Digital Service and Operations Management, as our advisor.
I’m so proud of our team and the effort they’ve put into building our initial product. We’re thrilled to be releasing our initial beta to a broader set of customers today. We really look forward to understanding what features are most important to you, and learning how we can work with you to improve our product's usefulness and functionality.
If you’d like to learn more, we’d welcome you to check out our product beta announcement and sign up for our beta at www.timeplus.com