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Top Digital Transformation Trends That Will Impact How You Manage Data in 2020

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A winning digital transformation strategy requires a winning data strategy. Learn how DataOps will impact enterprise data management for automation, DevOps, and cloud-native technologies.

Global leaders have access to more digital tools than ever before to deliver greater business value faster. In a world where the fast eat the slow, AI technology is empowering companies to make faster and smarter decisions, while cloud technology is allowing companies to dramatically reduce IT costs and gain more scalability and flexibility to build better applications rapidly. 

At the same time, data is quickly growing in size and complexity. Data is stored in a variety of formats across many data centers and clouds, making it exceedingly difficult to get a full picture of customers and the business. While many organizations are in the midst of defining and optimizing their enterprise initiatives, a winning digital transformation strategy requires a winning data strategy. 

Enterprises must rethink their data strategy to support the increasing reliance on intelligent toolchains and algorithms to run efficiently. Here are the top digital transformation trends that will impact how you manage data in 2020 and beyond. 

1. Digital Automation is Changing the Nature of Work

Digital companies continuously have to improve, transform, and automate their business processes, and digital automation platforms (DAP) make it easy for anyone to automate their own processes and workflows using tools, such as robotic process automation or machine learning for intelligent decision-making. DAPs can drive a number of business outcomes, including enhancing productivity, optimizing business operations, and building better customer experiences.

Central to this process is data. Companies need quality data that mirrors production data to power digital automation platforms in development and testing environments. Teams need access to fast and secure data to combat malicious attacks and prevent system downtime. Without it, enterprises put their transformation initiatives in jeopardy and inhibit their ability to keep up with competitors. 

2. DevOps Will Move to Hyperscale Public Clouds 

The maturity of DevOps practices has coincided with the growth of the public cloud over recent years. As enterprise DevOps implementations move beyond on-premises and private cloud environments, the hyperscale public cloud providers will become a primary and priority venue for DevOps. Top vendors like AWS, Azure, and Google Cloud have invested heavily in their cloud computing infrastructure to match the enterprise adoption of DevOps. Organizations need an effective method to replicate data from disparate databases to their preferred cloud provider while protecting the data in transit, so DevOps workloads can seamlessly be moved to the public cloud for elastic and efficient growth. 

However, one of the biggest challenges of cloud migration arises from the complexity of moving data across the boundaries of private data centers to public cloud locations. Without a platform that enables the secure rapid, automated, and secure management of data, DevOps toolchains will not produce accurate results and will give a false sense of system readiness. Companies require fast data delivery in their delivery pipeline, and the need to acquire data competency becomes more urgent as software competency grows and matures.

Similar to DevOps, the DataOps discipline is centered around the strategic use of data as data management becomes just as critical as software development for digital transformation success. 

3. Cloud-Native Crossover Will Deepen With Data 

Cloud-native technology and methodology—including containers, Kubernetes, serverless, and service mesh—will cross over more deeply with adjacent trends, especially DevOps and data services. The proliferation of data in organizations introduces the challenge of harnessing and managing it. Cloud-native technologies are designed to support microservices and dovetails with the solutions to operate data at scale. The data management strategy should include self-service data controls for end users, virtualization for storage savings, and replication for migration between on-premises data centers and the cloud; not to mention, data masking to ensure all data in downstream environments are secured. 

Closing Thoughts

The march towards more automation and the increasing usage of artificial intelligence will move inevitably forward in the business world in 2020. Enterprises need to proactively redesign their data practices to align with industry trends. DataOps, a practice that focuses on the end-to-end delivery of data, can enable organizations to lead in today’s application-driven economy by helping software teams release new features faster in a safe manner for core business applications. Otherwise, businesses can count on drowning in a deluge of data and failing to put digital transformation into action. 

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