This week we feature Io-Tahoe LLC, a startup pioneering machine learning-driven smart data discovery solutions that span data lakes and relational databases.
Io-Tahoe is a smart data discovery product which enables organizations to discover data in lakes and relational data instances, helping enterprises trace data elements across their systems in spite of outdated metadata definitions. The product utilizes machine learning algorithms to dramatically increase the accuracy, intelligence and speed of learning of complex data elements and data relationships throughout the entire business environment.
The venture started as an internal innovation created for a business need: to help unlock valuable data insights from more than 2 petabytes of customer data. Solutions from Io-Tahoe analyze up to billions of rows of data with 90 percent accuracy, using our machine learning solution that spans data lakes and relational databases.
The company acquired the Rokitt Astra technology, adding a deep understanding of data and its complexities plus machine learning algorithmic analysis of data relationships, to increase the accuracy, intelligence and speed of data discovery dramatically.
Software Modernization and Data analytics
The need for businesses to drive competitive advantage and innovation through rapid, technology-driven transformation and software modernization has never been greater. This imperative has moved the CTO role and enterprise IT from a supporting department to a critical C-level function. Daniel Burrus, a leading technology advisor to Fortune 500 companies, eloquently summed this up in a Harvard Business Review article:
The CTO will need to oversee the transformation of every business process, including how you sell, market, communicate, collaborate, and innovate. That means the CTO’s role will shift from aligning technology to applying technology to accelerate business strategy, from communicating technology plans to the executive team to integrating a transformation imperative and applying the process to all executive-level planning. That’s a huge shift
Io-Tahoe solution aims to simplify data discovery, enabling enterprises to find and make sense of structured, unstructured and hidden data with ease, throughout their entire business environment.
Founders: Oksana Sokolovsky (CEO) and Rohit Mahajan (CTO).
Competitors: Companies and startups focused on data discovery and/or data management.
Startup stage and funding: Acquired by Centrica’s subsidiary in May 2017.
Q&A with Oksana Sokolovsky, CEO, Io-Tahoe, New York, US
How did you get started with the idea?
I spent over 20 years working in IT at some of the world’s largest investment banks, where I was responsible for driving transformation. This required a solid perspective on the organization’s data landscape.
In 2014, with my colleague at the time – now my business partner and CTO of Io-Tahoe, we started to make our vision a reality – to develop a product that could address the data discovery and data management challenges for large enterprises. Together, we started Rokitt Inc., and built the product Rokitt Astra.
In May 2017, we were acquired by Centrica’s subsidiary, Io-Tahoe, and in August, we re-entered the market under a new brand, Io-Tahoe.
The idea sounds unique. Who are your main competitors?
Io-Tahoe is a smart data discovery product which enables organizations to discover data in lakes and relational data instances. We help enterprises trace data elements across their systems in spite of outdated or incomplete metadata definitions. The product utilizes machine learning algorithms to dramatically increase the accuracy, intelligence and speed of learning of complex data elements and data relationships throughout the entire business environment.
We work in the data management and data governance space spanning relationship discovery, data flow mapping and data protection.
What makes us unique is our approach to discovering data relationships, by looking at the data itself, and not just metadata. Through a complex set of machine learning algorithms and unique feature sets that we developed, we look at the data elements to extract implied data relationships and enable organizations to see a better picture of data dependencies and relationships across the whole enterprise. We work across relational data instances and data lakes, uniquely bridging these two data worlds and paint an accurate picture of the complex data landscape.
Our competition may include companies that focus on data discovery and/or data management.
Who are the main customers of Io-Tahoe? (Please describe the customer demographics and cities covered by Io-Tahoe.)
Our customer base is primarily made up of major financial services companies. We are executing on plans to expand our clientele to other industries, including energy and utilities, healthcare, telecommunications and retail.
Our customers are located across USA, but our reach is expanding into Canada, Europe and the Middle East.
How do you plan to address practical challenges. For example, marketing and outreach is a challenge most startups face? Are there other challenges you plan to address?
Running a startup will always present challenges, but we run a lean operation which gives us the flexibility to adapt quickly. We test, learn and apply the findings. We actively engage with the market and build this feedback into our product roadmap. We are agile. We listen to customers and analysts and adapt where needed to continuously improve our product.
What are your plans to go global?
Our team is located in North America, the UK, Europe and India. With a presence in the North American marketplace, we are focusing on expanding our reach to Europe and Asia. We see many opportunities to enable customers globally.
Tell us about your business model. Where do you see yourselves in a year?
We see ourselves not only expanding our reach to more customers within North America and EMEA, but also positioning ourselves as a thought leader in the data industry. Our innovative approach, our passion for solving complex data challenges and our product allow us to not only solve the problems enterprises have been facing for years, but also to influence data management and data discovery disciplines.
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