Data tools have not fully addressed today’s requirements for the infrastructure layer of the data stack. The Modern Data Company (Modern) believes we’re entering the golden age of data science. Still, in order for enterprises to realize the full value of their data assets, they need the final infrastructure layer in place.

Today, most companies are using separate infrastructures to handle the same data. This makes no sense. They’re stitching together tools to create a semblance of infrastructure, but they end up with little data ecosystems that don’t talk to each other. Instead, they need an operational data layer that provides a common, consistent layer of data to integrate everything into one single view — introducing the data operating system.

 

Our data operating system disrupts nothing…and everything

The tech industry loves the term “disruption,” and for good reason. It heralds innovation. Often it means replacing an older, less efficient system with one designed to accomplish the same thing — maybe in less time, with fewer errors, or at lower cost, for example.

In business terms, disruption isn’t always the foundation for excitement. It can spell downtime, upheaval in operations, lost revenue, and potentially even lost people. Disruptive technology can be a boon for the enterprise, but the implementation approach could be the difference between failure and transformation.

A data operating system like Modern’s DataOS is a disruptive technology in terms of innovation. It creates the missing operational layer among data tools, assets, and infrastructures to unite data flow across the enterprise. Think about it like this.

Across history, humans have invented technologies that replaced traditional ways of doing things. For example, textiles used to be handmade, making clothing a laborious and challenging thing to create. Now, with large machinery to make even precious materials, everyone can have a closet full of clothing without investing their life savings.

Unfortunately, that meant a significant disruption in the textile industry. Where people previously made cloth by hand, only a few traditional artisanal shops remain. Although we now have productized clothing and material-making to ensure widespread accessibility and availability, the entire industry has changed in the face of the disruptive innovations that came over time.

Similarly, data has also exploded in terms of demand and volume. Companies have more data than ever before but struggle to scale solutions that can extract value. Current tools do not productize data and prevent companies from leveraging their data’s full power. A data operating system can disrupt this system by creating an entirely new layer within the data stack that ties current investments together without negatively disrupting how the current elements of the system operate individually.

 

Implement a full data operating system without bringing operations to a halt

Enterprises worried about what this disruption means for everyday operations can take note. Modern’s data operating system only disrupts the way the enterprise approaches data. It does not disrupt day-to-day operations.

DataOS works with whatever the enterprise is currently using. For example, if companies have already heavily invested in cloud data stores, DataOS can supply clean, powerful data. If companies are working with multiple legacy systems, DataOS offers a transparent view of all available data from a single dashboard. If companies have a mix of cloud and legacy systems, they can be brought together into a seamless view that enables faster analysis and easier data integration.

It’s an operating system that works with existing tools. Companies can use DataOS to:

• Unify existing tools and modernize legacy architecture without the risks inherent in a traditional rip and replace approach.
• Revamp governance strategies to free data for all stakeholders while ensuring the highest security compliance.
• Engage non-technical users with right to left engineering and user self-service.
• Engage technical users with a more traditional command-line interface for more complex requirements.
• Ensure transparency for all data dependencies and sources.

 

What does this look like in practice?

A data operating system offers substantial value for enterprises across industries. Some examples:

• Distribution: Digital channels post-pandemic became more critical for driving revenue. A food and beverage distributor can use DataOS to provide valuable marketing data to digital teams—relevant, high-quality, and precise data. Digital teams can then use this data to improve marketing campaigns at the local level without waiting months for permission to access the data.
• Life sciences: Product generation times can substantially affect a company’s bottom line. With a data operating system, a life sciences company can preconfigure data validation rules and create a single source of trusted data to improve product generation times.
• Retail: Creating real-time data pipelines for even non-technical users can help retail companies choose the best locations for new stores, target marketing spending at the granular level, and build a true Customer 360 model to nurture customer relationships—all while working with existing legacy systems acquired from competitor acquisitions.

These are some basic examples of what an enterprise can do with a data operating system. With composability built right in, a company can build many different types of data architecture to suit what they need right now and in the future.

 

Businesses can harness the right kind of disruption for data value in just weeks

Using a cloud-based data operating system, users can set up DataOS to integrate their current systems without the need for expensive pilot projects or IT back and forth. It enables users to access the data they need through simple Google-like search functions and drag and drop desired functions for right to left engineering. Complexity is abstracted, and all data users can get the answers they need by seeing what data is available.

A data operating system provides a connective overlay, uniting all data tools within the company’s architecture—no laborious training, extensive downtime, and uncertainty. Users begin building their own reports and queries right away while the administrators see precisely what is in use and where.