Cinchapi Releases Beta Version of Data Platform Featuring Machine Learning and a Natural Language Interface to Explore Any Data Source in Real-Time

The Cinchapi Data Platform allows data scientists and analysts to dispense with data prep. Makes data exploration and discovery conversational and actionable.

ATLANTA, GA. March 6, 2017 – Delivering on its promise to take enterprise data from cluster to clarity, Atlanta data startup, Cinchapi, today announced the beta launch of its flagship product, the Cinchapi Data Platform (CDP).  

The Cinchapi Data Platform is a real-time data discovery and analytics engine that automatically learns as humans interact with data and automates their workflows on-the-fly. Cinchapi’s data integration pipeline connects to disparate databases, APIs and IoT devices and streams information to the foundational Concourse Database in real time. Data analysts can then use the Impromptu application to perform ad hoc data exploration using a conversational interface.

The CDP’s analytics engine automatically derives additional context from data and presents the most interesting trends through beautiful visualizations that update in real-time. These visualizations can also be “rewound” to show how data looked in the past and evolved over time – even if the data has been deleted. The CDP’s automated machine intelligence empowers data analysts to immediately explore data using natural language and drill down by asking follow-up questions.

Compared to conventional data management, data teams can expect to shave 50% or more from their analytics tasks. Obstinately a data management platform, the CDP is ideal for anyone looking to explore decentralized or disparate data in search of previously hidden relationships. No matter the nature of the data source – be it any combination of unstructured IoT data, industry standard frameworks, proprietary data, or legacy sources – in just a few minutes, interesting relationships, patterns, or anomalies will be exposed.

Just as powerfully, the Cinchapi Data Platform’s underlying database, Concourse, writes and stores definitive data across time. Like a DVR for data, users can “rewind time” to specific points in the past. They can also can press play to watch as vivid visualizations illustrate how these newly discovered insights were created and how they evolved over time.

“From day one, the Cinchapi vision has been to deliver ‘computing without complexity’”, explains Cinchapi CEO and founder, Jeff Nelson. “I’ve worked with data my entire career and have been frustrated by how much of my time has been spent integrating and cleaning up disparate or decentralized data before being able to explore trends or to begin coding. We knew that by leveraging machine learning, the Cinchapi Data Platform would eliminate the drudgery of data prep. It then instantly exposes the most interesting and relevant data to use or to more fully investigate.”

The End of Data Prep and Cleanup

If asked, those who work with data will tell you that the greatest impediment to working with it is that there is too much of it, and that often, the data is messy. In other words, before an analyst can get insights from data, she has to sift through all of the data to see what she has. She has to determine what data is relevant to the task at hand, and then see how that might relate to other data points.  This data prep and cleanup process can add weeks or months to a project.

As Big Data grows ever larger with data generated by the Internet of Things, it’s a problem which will only increase in scale and complexity. By 2020, predicts that 24 billion IoT devices will be connected to the internet. That works out to about three IoT devices for every person on the planet.  Each of these devices will be generating “messy data”, as there is no standard for what IoT data should look like.

To solve this growing problem, the Cinchapi Data Platform uses machine intelligence to comprehend data, regardless of the source or the schema.  It then looks for relationships, patterns, or anomalies found between otherwise decentralized, disparate, data stores. The CDP was also purpose-built to not impose, nor to rely upon any specific data schema.

This makes the CDP the ideal platform when working with data sources which lack a coherent structure, like IoT data or undocumented legacy or proprietary data. Of course, the Cinchapi Data Platform can also work with industry standard databases like SQL, noSQL, and Oracle.

A Simple, Three Step Workflow

The Cinchapi Data Platform workflow consists of three simple steps: Ask, See, and Act.

Step One, ASK: Once connected to the desired data sources, the first step is to simply ask a question using common English phrases. There is no need to master cryptic data queries in an effort to “solve for x”. Instead, users can ask a question using everyday, conversational phrases. Should the user need a more specific answer, all that she needs to do is ask a follow-up question. With use, the CDP’s machine learning allows the platform to better understand the context of the question asked, further enhancing the user experience.

Step Two, SEE: After questions are asked, next comes the results. Built into the CDP is a powerful analytics engine which provides hidden insights and customized visualizations. This allows users to see relationships and connections which were previously obscured. Even better, with these new relationships now exposed, users can “rewind time” to see how these relationships have evolved and impacted operations in the past.

Step Three, ACT: With the results available, users can then act on the information presented. A data analyst can automate actions with just a few button clicks. A logistics company might find enhanced efficiencies in route planning which could be shared to the fleet in real-time. A CSO in a bank might use its automation capabilities to trigger alerts to a security team when potentially fraudulent activities are detected. Frankly, the possible use cases are endless.

CDP features include:

  • Concourse Database – An enterprise edition of the Cinchapi’s open source database warehouse for transactions, search and analytics across time. This is where streamed data is stored.
  • Sponge – A real-time change data capture and integration service for disparate data sources.
  • Impromptu – A real-time ad-hoc analytics engine that use machine intelligence for workflow automation.

About Cinchapi, Inc.

Atlanta-based Cinchapi is transforming how data scientists, analysts, and developers explore and work with data. The Cinchapi Data Platform (CDP) and its Ask, See, and Act workflow was purpose-built to simplify data preparation, exploration, and development. Its natural language interface combined with machine learning and an analytics engine make working with data conversational, efficient, and intuitive. Imposing no schema requirements, the CDP streams, comprehends, and stores definitive data generated in real-time by IoT devices as well as conventional, legacy, and proprietary databases. Learn more about the Cinchapi Data Platform and its #AskSeeAct workflow at