As the volume of data that people and businesses produce continues to grow exponentially, it goes without saying that data-driven approaches are critical for tech companies and startups across all industries. But our conversations with customers, as well as numerous industry commentaries, reiterate that managing data and extracting value from it remains difficult, especially with scale. 

Numerous factors underpin the challenges, including access to and storage of data, inconsistent tools, new and evolving data sources and formats, compliance concerns, and security considerations. To help you identify and solve these challenges, we’ve created a new whitepaper, “The future of data will be unified, flexible, and accessible,” which explores many of the most common reasons our customers tell us they’re choosing Google Cloud to get the most out of their data.

For example, you might need to combine data in legacy systems with new technologies. Does this mean moving all your data to the cloud? Should it be in one cloud or distributed across several? How do you extract real value from all of this data without creating more silos?

You might also be limited to analyzing your data in batch instead of processing it in real-time, adding complexity to your architecture and necessitating expensive maintenance to combat latency. Or you might be struggling with unstructured data, with no scalable way to analyze and manage it. Again, the factors are numerous—but many of them accrue to inadequate access to data, often exacerbated by silos, and insufficient ability to process and understand it. 

The modern tech stack should be a streaming stack that scales with your data, provides real-time analytics, incorporates and understands different types of data, and lets you use AI/ML to predictively derive insights and operationalize processes. These requirements mean that to effectively leverage your data assets:

  • Data should be unified across your entire company, even across suppliers, partners, and platforms., eliminating organizational and technology silos.
  • Unstructured data should be unlocked and leveraged in your analytics strategy. 
  • The technology stack should be unified and flexible enough to support use cases ranging from analysis of offline data to real-time streaming and application of ML without maintaining multiple bespoke tech stacks. 
  • The technology stack should be accessible on-demand, with support for different platforms, programming languages, tools, and open standards compatible with your employees’ existing skill sets. 

With these requirements met, you’ll be equipped to maximize your data, whether that means discerning and adapting to changing customer expectations or understanding and optimizing how your data engineers and data scientists spend their time.