Delivering secure, production-quality data at the speed and scale of the cloud is one of the biggest challenges for enterprise data teams today.
For many organizations, the cloud has become a foundational enabler of their digital transformation initiatives. While cloud has made it possible for enterprises to scale and meet the demands of the business, it’s also increased the velocity of running workflows and upped the risk of data breaches. In other words, businesses will either be bogged down in their cloud adoption and migration plans or risk exposing their sensitive data to bad actors without having the ability to deliver secure, production-quality data at the speed and scale of the cloud.
LogicMonitor’s Cloud Vision survey predicts that 83 percent of enterprise workloads will be in the cloud by 2020. As more organizations adopt and migrate their data into cloud environments, software teams will need to find a balance between ensuring business innovation while protecting data privacy because at the end of the day, the key is to deliver secure, production-quality data to their software development life cycle to accelerate their application development.
There are two major data-related bottlenecks in cloud environments:
Long data provisioning cycles. Manual workflows prevent developers, testers, business analysts, and data scientists — those who are tasked with drawing meaningful insights or building new products and services for the business — from accessing fast, secure data for these initiatives. In addition, enterprise workload migrations can be complex with data ingestion and data source heterogeneity.
Risk of sensitive data exposure and violation of regulatory policies. Without the right technology that irreversibly obfuscates data, it can take weeks or months to mask sensitive information and create copies. As a result, it puts the organization at risk of non-compliance and slippage on project deadlines.
To address the manual IT workflows in traditional software development, the DevOps movement arose to add automation to key parts of the delivery pipeline. But even though DevOps has played an integral role in automating infrastructure and the SDLC, organizations have yet to figure out how to automate data delivery. Data is vital to an enterprise’s health and survival as we see companies, like Amazon and Google, turn data into a competitive advantage. For instance, retail firms match inventory data with customer order data to satisfy user whims for goods. Banks use customer transaction data to facilitate payments, and hospitals leverage patient data to deliver timely, quality care. Without harnessing the power of fast, secure data, organizations cannot fulfill their missions.
While the emergence of cloud technologies has introduced the ability to access data on-demand, it’s added complexity to data governance as information is spread across on-premise and cloud environments. DataOps takes an innovative and effective approach to how data is provisioned, accessed, and controlled in the following ways:
By rethinking how data is copied and stored. Enterprises should be able to immediately provision environments from virtualized copies in the cloud without the need to backup, restore, setup, and validate.
By rearchitecting how data environments are provided, DataOps give users in the organization independence and freedom to work with data as they please. Enterprises should be able to deliver secure copies of data from heterogeneous data sources that are running on-premise or in the cloud with self-service access and control.
By incorporating a masking technique that provides data security and protects sensitive information.
DataOps enables the rapid, automated, and secure management of data in an agile manner that is consistent with the continuous integration and continuous delivery software development model. Organizations that fail to embrace DataOps will struggle and face the threat of extinction under the deluge of their data.
In today’s fast-changing, digital world, companies are faced with driving faster innovation and staying in compliance with the array of data privacy regulations around the world, including the General Data Protection Regulation in the EU, California Consumer Privacy Act, New York Privacy Act, and more. Businesses can’t drive data-driven transformation while neglecting data security; simply put, you can’t succeed in one without the other.