The Data & Analytics Landscape is Shifting
Enterprises are now starting to adopt a more data-intensive approach to business, one that is supported by a number of emerging technologies. The modern enterprise is increasingly facing digital disruption as new competitors emerge and existing ones scramble to adjust their strategies for the future. New technologies enable enterprises to gain a clearer understanding of customer behaviors and preferences while also giving them the tools necessary to respond faster.
Ronald van Loon is an HPE partner and recently caught up with Matt Maccaux, the global field CTO of the Ezmeral Enterprise Software BU at Hewlett-Packard Enterprise, who provided meaningful insights on key shifts, key technologies and the right steps companies need to take to embrace a new phase in the enterprise data and analytics landscape.
A considerable amount of data is generated every day, and data volumes are expected to increase significantly within the next 5 years. According to Statista, the volume of digital information generated in 2020 was estimated at 64.2 zettabytes — a figure that will grow to 180 zettabytes by 2025.
With such an enormous deluge of raw information, enterprises need solid technology platforms and infrastructures capable of handling it. This is where the concept of big data comes in, with tools such as Hadoop under its open-source Apache license to help companies scale their operations.
Companies are undergoing a new phase of digital transformation that focuses heavily on data estates as a critical asset and driver of competitive advantage. From this perspective, data is not simply an extension to existing IT architectures but rather as the central nervous system for new business areas, such as customer experience management or product innovation.
“As part of this digital transformation — organizations are taking a hard look at their traditional data sources, data streams, and data processes and data exploitation systems,” Matt says about these new macro-trends in digital transformation wherein the data estate is really what is in focus for companies.
A few key trends in the digital transformation area include:
- Increasing emphasis on embedding analytics in the daily operation of businesses.
- Increased focus on artificial intelligence.
- The move to cloud-native platforms and microservices.
- The build-out of a hybrid data architecture.
The shift to a more digital approach in terms of business strategy has increased demand for technologies that can help process, mine, and manage large amounts of data.
Cloud is setting the pattern for this digital transformation. Cloud technologies, with their ability to support a large number of users across different geographical locations and time zones at an affordable price, are also redefining how data is being managed and processed. As a result, companies looking to achieve digital transformation need to develop solutions that are robust enough for continuous use by many people and can handle various types of data.
Cloud-native applications are part of an approach that embraces a cloud-first and agile development strategy, which results in more efficient solutions. Cloud-native applications run on the same infrastructure as any other type of software application and are inherently less complex to deploy, manage and upgrade. They have smaller resource footprints than their traditional counterparts, thanks to microservices architectures with components that can either be spun up or shut down depending on needs.
Cloud also provides flexibility that allows companies to run multiple data centers rather than one large, central facility, known as multi-cloud. Additionally, cloud platforms enable organizations to easily copy their real-world workload into the cloud for disaster recovery purposes.
Cloud platforms also support various advanced analytics tools for data management and processing, such as self-service business intelligence and machine learning. Artificial intelligence is another area in which cloud technologies make a significant impact by providing more flexible, cost-effective ways to deliver AI solutions.
Speed of Data Centers To Bring Out Cloud Principles
Traditionally, cloud-native principles could not be fully achieved by systems because of a lack of compute power and speed from data centers, which resulted in enterprises relying on more traditional hardware systems. However, that is changing with the speed of data centers, which have evolved to handle massive amounts of information at a high speed.
The emergence of fast and efficient microservices frameworks is another key trend that’s enabling cloud-native principles. Microservices, as the name suggests, are self-contained units that do one thing well. Each microservice has a specific function, which can be updated and replaced without disrupting the overall functionality of the system.
Microservices architectures allow enterprises to rapidly respond to changing business needs with smaller development teams. It’s estimated that companies like Netflix have saved a considerable amount in annual infrastructure costs by implementing microservice-based architectures.
Microservices architecture also allows for increased flexibility in terms of deployment. Rather than having a large and expensive system that’s up 24/7, companies can now optimize the value they get from their data by ensuring it’s only available during business hours when people are likely to be using it.
The main cloud-principle that is now being unlocked because of the higher availability of compute power is the concept of containerization. Containers allow for a single logical entity to run on many different machines. As the container is self-contained and independent from other systems, it can be easily moved between data centers and managed using cloud-native tools.
Key Technologies For Data & Analytics Adoption
The following are the key data and analytics technologies that will help enterprises achieve digital transformation:
One of the most important data and analytics technologies that will drive digital transformation is Kubernetes. This technology provides a way to efficiently manage containers in cloud environments, which can scale automatically based on demand. For example, if an enterprise has a lot of different projects going on simultaneously, it needs to ensure each one of them has access to resources based on their needs – which is achievable with Kubernetes.
Kubernetes plays an important role in helping companies deliver and support multiple applications with high availability. This technology helps enterprises provide reliable access to data for their users. An effective Kubernetes deployment involves making sure the apps are highly available on bare metal resources and also implements self-healing and auto-scaling features.
- Apache Spark
Another technology that is increasingly popular in the data and analytics space is Apache Spark. This framework provides fast analytics for large amounts of unstructured data.
Spark comes with its own cluster manager, management console, and integrated tools to help enterprises collaborate across multiple users. It simplifies the ability for business analysts to combine their work on different projects.
Steps To Manage Cloud-Native Data and Real-Time Analytics
The only thing left, of course, is putting it all together. With the right combination of cloud-based computing power and new technologies, companies can overcome some of the challenges they face.
- The first important consideration is finding as many open-sourced solutions as possible to avoid lock-in.
- The next step is to streamline business processes and data management so employees spend less time on routine tasks and can redirect more of their efforts to strategic initiatives.
- Finally, enterprises should choose cloud computing partners who offer real-time data capabilities through a more collaborative approach rather than a full-stack approach.
By adopting these tactics, companies can effectively manage their data and analytics projects. While every organization is different, the main commonality is the need to manage large quantities of data efficiently.
Ready for the Paradigm Shift?
Digital transformation is a never-ending process that continuously changes. The key to getting started on digital transformation and driving it forward is having strong partners and the capabilities to build your own technical resources to ensure you’re able to manage all of your data efficiently. By adopting cloud-native technologies and utilizing open-source projects, companies can more effectively manage data and analytics environments.
By Ronald van Loon