Innovation is essential to stay one step ahead of your competition. Without new ways of working, new products or new digital services, companies will fall behind their willing competitors. For example, EY’s research in the technology, media and telecommunications sectors found that 87% of companies identified as leaders believe that their success depends on maintaining their investment levels.
Bryan Kirshner, Vice President Strategy, DataStax.
But these investments are also tricky. Doing something completely new or different can lead to failure. According to the same EY report, 63% of respondents admitted they didn’t achieve the projected and planned returns. The focus is on how companies plan with foresight to implement their experiments in everyday processes.
In addition, our own research into the State of The Data Race found key attributes of “data leaders” – companies that excel at using data to add value to their customers and most likely generate at least 20% of their revenue from data and analytics – Including the way these companies use combinations of open source software to build their stacks. Open source plays an important role in how these organizations move beyond small data and analytics initiatives that hold promise, move them into production, and continue to deliver.
Innovation and Open Source
Open source makes it easy to innovate and experiment with applications and data. There are mutliple reasons for this.
The first is lower deployment costs. Since there are no licensing costs for open source projects, it’s easier for companies to try them out. Additionally, around each project, the community has experience putting different projects together to meet specific needs and create larger deployments. This experience can help you advance your own experiments and figure out how to use data more efficiently.
Similarly, the second element is how outside pressure can help fuel innovation. The global pandemic caused by COVID-19 has led many companies to look for new ways of working. The pandemic has shown a lot in terms of human determination and kindness, but it has also proven that companies can reinvent many core business processes in days instead of weeks or months. When it comes to getting started quickly, open source makes a big contribution.
Finally, projects to modernize applications are also gaining in importance. IDC found that 86% of respondents said they had modernized more than 50% of their legacy applications this year, up from 65% previously. These projects represent a great opportunity to use open source as a way to update existing applications, reduce costs, and find opportunities for innovation. The open source ecosystem offers technologies that range from de facto standards to best-of-breed for modern, cloud-native applications.
Each of these areas can be an incentive for innovation in itself. What we understand better now is that companies that put corporate leaders in charge of their data tend to get more out of this priceless asset. It might sound a little contrived to say that “information is power,” but data that drives insight and knowledge is really how we empower people to make positive change. Open source makes building these frameworks easier, but also retains control over how this will work in the future.
Open source also makes it easier to democratize the way teams can innovate around data. Information and insight projects usually start with the IT function, as this department is used to handling data. However, this approach should also be placed in the hands of the non-tech savvy to support innovation in terms of applied use cases. This data can be presented through abstractions and visualization tools, making it easier to interact with this data over time, but also making it easier for these non-technical teams to express themselves on what they want to discover.
In the past, we have all suffered from too much top-down development driven by corporate roadmaps and what the C-Suite believes is market-driven goals. We can now examine where the barriers to democratizing innovation lie and open up opportunities to encourage more bottom-up innovation as well.
Put that into action
There are so many ways to innovate more with data, but we also have to face the challenges that will follow. As we all work more broadly with data and related services, it is important to make sure we keep an eye on the operational side as well, as there is a risk of data getting out of hand and creating separate data silos.
To put this in context, developers can now build data-driven applications that use microservices and containers to create each element of service. Each of these services is encapsulated so that each service can be changed or updated without affecting the others, while new services can be added to the mix as well. Where we may have started our architecture journey earlier by asking ourselves what we need to build “most”, we can now ask what we now need to build “least” in order to function. Over time, the data infrastructure can be added to support new innovations.
For innovation to work, we need to make the data stack work in practice. This includes helping companies make data accessible and reusable, but also introducing processes in which data structures can be clearly defined and easily integrated into other services across the company. This makes it easier for us to scale this data over time. In essence, before we begin, we need to think about the journey, understand the path ahead, and then think about how we can make this journey easier.
Open source obviously plays a central role at every level of the infrastructure involved. For the database tier, options such as Apache Cassandra can provide distributed data support for services that can scale and support hundreds of thousands to millions of customers per second. Services such as Apache Pulsar can act as a message bus to manage event streaming between application components and services. Under all of this, Kubernetes can act as a central orchestration platform that manages the infrastructure and scales it as needed. This gives us the opportunity to approach the holy trinity of speed, resilience and size with a new attitude.
There will still be challenges along the way, and some migrations to new cloud-centric data advantages may seem like “heavy lift” jobs at the beginning. Most of these barriers, however, are caused by perceptual, process, or cultural issues, not the technology itself. Working on the human element to circumvent this is the most effective route to long-term success and ensures that developers and other employees are in the entire company can develop new services and innovations around data. The fact that many of the components that can be used are open source should help overcome these hurdles as they can best be used to meet specific team goals and ambitions for the wider business.
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