Toronto-based Cinchy announced today it has raised $14.5 million in Series B financing round led by Forgepoint Capital. Cinchy’s customers, including National Bank, TD Ameritrade, and Colliers International, use its technology to manage their data more effectively by eliminating the cost and delay of data integration. Cinchy says its “dataware” platform also helps extend legacy systems, accelerate the delivery of new applications, establish universal data access controls, and enable unlimited collaboration on quality data across the entire organization.
Dan DeMers, CEO and co-founder of Cinchy said in a statement: “Our mission is to liberate and harness the power of data, giving it back to teams and organizations to accelerate digital transformation and growth… This latest round of funding enables us to further expand our team and release new offerings that include pre-built dataware solutions designed to help organizations instantly liberate both trapped data and siloed SaaS applications.”
Over 100,000 exabytes (100,000 billion gigabytes) of data will be generated in 2022, according to IDC, up from 33,000 exabytes only four years ago. IDC forecasts that 221,000 exabytes will be created, captured and replicated in 2026.
“Replicated” data represents a big chunk of all this tsunami of exabytes washing over enterprises around the world. IT departments and the business users of data create multiple copies for activities that do not involve production systems, including reporting, analysis and test and development. Copy data grows sometimes by as much as 10 or 20 times more than production data.
Sharing data between different departments, functions, and applications in the enterprise enables collaboration and increased productivity. Arguably, the value of data increases the more it is shared among the people that need to use it in their work. This is data’s “Network Effect” or what I called 10 years ago “The First Law of Big Data” (and in 1999, I called it “Ruettgers Law,” after the CEO of EMC Corporation where I worked at the time).
But who controls the multitude of copies? How do you ascertain that the data in the copy you are using is valid at that point-in-time? How do you make sure the copy is not a fake? And what about the constantly increasing cost of storing multiple copies of the same data and of “data integration,” the process of making a copy of the data available for a specific application?
Dan DeMers “spent over a decade as an IT executive with the most complex global financial institutions, and created Cinchy after realizing that half of all IT resources were wasted on integration,” reads DeMers’ short bio.
The market Cinchy entered five years ago is the multi-billion data management market, a growing segment of the computer industry, replete with challenges, opportunities, startups, and emerging technologies. Says Gartner in “Hype Cycle for Data Management, 2022”:
“Today, data is more distributed than ever in multicloud, intercloud and hybrid architectures. The need for skills and technologies to manage this complexity and financial governance across the cloud infrastructure is creating opportunities for innovation.”
Internal enterprise data residing in the cloud, public internet data that can be used to answer specific business needs, the relatively new practice of combining internal and external data to gain business insights—all contribute to data being in so many different places. But it’s not just data, but also its governance, control and ownership that has increasingly become distributed, with domain and subject matter experts located in different places, departments, even organizations.
Over the last decade or so, technologies and products aiming at answering the challenges of distributed data have been grouped under the category of “data fabric.” According to Gartner, data fabric supports “the design, deployment and utilization of integrated and reusable data across all environments, including hybrid and multi-cloud platforms. Data fabric leverages both human and machine capabilities to access data in place or support its consolidation where appropriate.”
That concept served well Cinchy’s focus on eliminating data integrations and the myriad of copies they produce. In 2019, however, “data mesh” was introduced as the solution to the challenges of distributed data, “distracting the market,” and slowing “adoption for the data fabric,” according to Gartner.
While “data fabric” is more technology-oriented and “data mesh” is more organizational architecture-oriented, they attack similar issues and can be used in complementary fashion. Both concepts are popular in data management circles and Cinchy said today that its new funding will allow it “to further capitalize on the surge in global demand for data fabric and data mesh solutions.”
Today’s press release, however, also describes Cinchy as “the pioneer of dataware.” In response to my request for clarification, DeMers answered (via email): “Let’s acknowledge the dynamic nature of this arena—new technologies emerge much faster than the ability to constantly create new analyst categories. We believe dataware is no different–it offers undeniable and far-reaching benefits, and will inevitably draw other developers to the field. With enough development and innovation, it will become its own category.”
The decision to create a new market category comes at the point in the life of a startup (and even an established company) when market confusion slows adoption. Or when a new approach—in this case, to data management—needs a larger vision and a label that will brand it as the new new thing, the next evolutionary leap.
This is what Cinchy has done with “dataware,” which according to the company is the next stage in the evolution of the industry from hardware and software, putting data at the center of the IT infrastructure, reversing the relationships between applications and their data.
When you promote a new market category, you enlist “thought leaders” to help explain it, among other marketing and PR activities. A few months ago, the Eckerson Group published a white paper titled “The Rise of Dataware,” putting the new category in the context of all the data management technologies, products, and concepts that came before it, including data warehouse, data lake, lakehouse, data fabric and data mesh.
For example, “the data lakehouse focuses on analytics use cases. It cannot serve as the backend for an operational application in the way dataware does.” And “A traditional data fabric will always require applications to have their own databases that connect to the network, however, while dataware allows developers to build new applications without dedicated operational databases.”
The Eckerson Group acknowledges that the dataware approach is “in its early days.” Specifically, “until the wider community of software vendors adopts dataware-friendly architectures, off-the-shelf applications will only be able to interact with dataware as a data fabric.”
Still, “Dataware is a bold reimaging of the relationship between data and applications. …If dataware can overcome the initial hurdles of adoption and gain widespread acceptance, it will flip the data technology industry on its head. So many of the tools we use today focus on bringing data together from multiple applications, but, with dataware, the data is never separated to begin with.”
Says DeMers: “One day, dataware will likely be its own category which we expect to be similar in breadth and importance to data management by incorporating and enabling pioneering concepts and innovations… But new categories take time to be created. When something truly different comes to market, there is a normal lag between the pioneers and others following with the same approach or adopting the same model of attack… It took a while for the ‘smartphone’ category to emerge. Dataware is in a similar position.”
Data has taken over from hardware and software as the center of everything “computing,” the lifeblood of tech companies. And increasingly, the lifeblood of any type of business. Data is eating the world. It’s already at the center of the business of many enterprises and it’s time to put it at the center of the IT infrastructure.