As a fresh approach to data management, dataware has the tools required to transform analytics and software development. It aims to disconnect data from software to enable a new paradigm of application-independent data. In this article, Karanjot Jaswal, the co-founder and CTO of Cinchy, defines dataware, examines what it is and is not, and makes parallels with other approaches to data centralization. Read on to have a better understanding.
Enterprises today have various software packages to serve their customers and employees. Each app generates and maintains enormous amounts of data, frequently exchanged with other apps to keep them in sync. Companies also utilize this data for analytics, which integrates data from several sources to make decisions and foresee the future. However, moving the data demands too much integration. But is it possible to do away with the necessity for data integration? Most certainly, yes. Data integration is no longer necessary, thanks to a new architectural concept called “dataware,†which aims to redefine how data and applications are connected.
Speaking to Spiceworks, Karanjot JaswalOpens a new window , the co-founder and CTO of Cinchy, goes into further depth about dataware and takes us on a deep dive into how it differs from data warehouses, its security, use cases, and more.
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How Dataware Is Changing the Face of Data
What is dataware?
You can think of dataware as perhaps the first major advance since big data and analytics, having the same impact as hardware and software.
Dataware is a software category that enables organizations to connect and control the data within their ecosystem and use it to build new digital solutions in half the time and cost of traditional approaches by eliminating the need for solution-specific databases (data silos) or performing point-based data integration (copies).Â
Dataware is a platform technology that incorporates several advanced capabilities and concepts, including an operational data fabric, domain-centric governance, knowledge graphs, and active metadata.Â
Perhaps most importantly, dataware facilitates collaboration – real-time data editing by people and systems working in concert without conflict. This is made possible by the fact that dataware maintains a ‘zero copy’ environment, so all collaboration on data is protected by access controls that owners set at the data level. This way, the controls are not eroded by applications and are enforced universally. A user-friendly interface called a data browser also allows business users to directly access operational data and collaborate on large-scale, operational projects under the complete protection of the same access controls. Â
Going a level deeper, dataware also serves as the backend (aka persistence engine) for both operational applications and analytics workflows, supporting both transactional and analytical data.Â
How exactly does dataware differ from a data warehouse?
One way to view the difference between dataware and other data management technologies such as data warehouses is through the lens of simplicity. In short, dataware eliminates the need for a development team to stand up new app-specific databases and create point-to-point integrations, whereas data warehouses deliver neither of those outcomes and are intended mainly for analytics use cases.
Source: CinchyOpens a new window
The data warehouse – one of many options to emerge in the data management space over the past decade – is a structured central data repository that stores data in a predefined model for business intelligence. A data warehouse does not serve as a backend for unlimited applications. It does not eliminate the need for new database silos and data integration, as with dataware technology. Applications feed a data warehouse via pipelines, so instead of reducing the number of integrations needed, it requires more. In the long term, this is a huge disadvantage. Dataware is also intended for the entire organization, not only IT and analytics teams. This can be described as a far more democratic data management technology than data warehouses.
Top use cases of datawareÂ
Organizations use dataware primarily to save 50% or more of the time and cost associated with developing new technologies, including applications, dashboards, and real-time systems. There are also analytics use cases, particularly the generation of secure 360 views of customers, partners, facilities, and just about any business entity you can imagine.
But dataware is not a rip and replace technology.Â
It enables organizations to backup, unlock, and reuse their SaaS data (and decommission hundreds of SaaS seats in the process) while also modernizing existing apps with new and upgraded data.
Organizations also use dataware to give business users a single UI to manage data while providing functional teams with meaningful custodianship over the datasets within their specific domain, e.g., legal, marketing or finance. This is sometimes called the “data mesh†approach to data governance. Of course, all of this is done while simultaneously eliminating the use of spreadsheets for business-critical processes – an operational and compliance risk that has no place in the modern organization.
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What makes dataware stand out, and how secure is it?
Dataware revolutionizes how organizations use data by freeing it from applications – for the first time. This allows data to become central to a company’s everyday operations and empowers them to go to market faster with new solutions.
Dataware also stands out because it provides schema plasticity. Despite all the advances we see in technology, many painful issues haven’t changed a bit. For example, when a developer changes an application’s underlying schema or framework, it can disrupt the functionality. This may require changing the operating model entirely or breaking the underlying code. Decoupling data from applications rids developers of these issues and leaves them free to focus on valuable business functions. In dataware, this capability is referred to as “schema plasticity.†The result is that organizations can evolve and innovate with unprecedented agility without breaking pre-existing solutions (and code) at every turn.
To truly appreciate the major advanced dataware, let’s look at what’s been the status quo. For the last 40 years, data has been inextricably linked to applications, meaning every new application created requires its data silo for data integration. Making endless copies of data to connect these silos created headaches for IT, made data compliance a huge challenge, and hindered business agility. Before dataware, the industry solely focused on addressing integration symptoms with data lakes, data warehouses and more. Dataware is the first innovation addressing the root causes of data fragmentation.
The technology brings many standout benefits:
- For example, it changes the way we think about solutions development and increases the speed and velocity with which we deliver.Â
- It boosts productivity and efficiency and dramatically accelerates artificial intelligence and machine learning projects while increasing their accuracy at the same time.
- And it eliminates data integration, which can drain up to half the entire IT budget.
- In a broader sense, it fuels a dramatic transformation.Â
Dataware helps move us from the data foundation we have now, which is app-centric, to a data-centric environment. When data is set free, it functionally gets decoupled from the applications. This helps to create, collate and disseminate it. This way, we get better control and governance, and we can more easily create metadata (data about data). We gain a better view of the enterprise. With dataware and its accompanying benefits, data becomes the network, the application, and the product. By treating data in a way that recognizes its importance, we turbo-charge IT, development, and just about all business solutions. Â
The benefits of security are equally significant. In particular, collaboration happens without endless copying when data is removed from applications and exists in its ecosystem. This ensures a higher level of security and compliance than is possible now.Â
Key takeaway
It’s worth noting that while dataware is used to support transformation into a controlled, collaborative, and more data-centric organization, the process is incremental and happens project by project. This differs from the monolithic undertaking that data lakes and other legacy data management categories represent. Essentially, it supports a steady unwinding of IT and data pipeline complexity. The underlying data architecture of dataware, is network-based, a bit like a brain, so every project that connects data to the platform “pays it forward†to the next, resulting in compounding efficiencies. This is even more remarkable given that many organizations see a positive ROI on dataware technology from the first project.
Can dataware become a popular data centralization approach in the future? Let us know on LinkedInOpens a new window , Facebook,Opens a new window and TwitterOpens a new window . We would love to hear from you!