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How to build and maintain strong data organizations—the Dummies way.
Data Governance For Dummies offers an accessible first step for decision makers into understanding how data governance works and how to apply it to an organization in a way that improves results and doesn't disrupt. Prep your organization to handle the data explosion (if you know, you know) and learn how to manage this valuable asset. Take full control of your organization’s data with all the info and how-tos you need. This book walks you through making accurate data readily available and maintaining it in a secure environment. It serves as your step-by-step guide to extracting every ounce of value from your data.
Big Data is structured and unstructured data that is so massive and complex in scale, that it’s difficult and often impossible to process via traditional data management techniques. One way to define and characterize big data is through these five Vs (Volume, Variety, Velocity, Variability, Veracity). Big data was a thing even before Android and Apple smartphones and apps started generating data. This was before we had connected billions of devices, called the Internet of Things (IoTs), which would eventually begin collecting all manner of data. Big Data even predates videos of cats published every day on social media platforms.
DevOps is a reimaging of how to build and deliver solutions quickly. It incorporates automation, collaboration, communication, feedback, and iterative development cycles. In a similar fashion, but on the premise that organizations were struggling with data volume and velocity, and the slow speed of deriving insights, it was observed that efficiencies could be gained in rethinking the lifecycle of data within the enterprise. Using the concepts and successes of DevOps, a new approach to data analytics emerged called DataOps. Some called it DevOps for Data Science.
Like DevOps, DataOps uses contemporary work approaches such as collaboration, tools, and automation to find efficiencies and deliver higher quality and quicker insights. You can think of DataOps as a way to kick data analytics into high gear. Central to DataOps is the emphasis on collaboration between participants in the data value chain. This includes data analysts, data engineers, IT team members, quality control, and data governance. In addition, like DevOps, DataOps proposes an agile approach to delivering data solutions. Instead of long periods of requirements analysis, design, and then development, work is broken into smaller chunks and priority is given to delivering value quickly and often. Cycle times are compressed, and business users get the data they need sooner.
DataGovOps is a new approach to extending automation and continuous governance (CG) to the data lifecycle. CG means automated processes running without interruption instead of being manually applied periodically. DataGovOps builds on the concepts and success of DataOps. It seeks to eliminate many of the complex and manual processes associated with ensuring data is managed well, quality and compliance is maintained, and risks are reduced. In other words, it’s an emerging approach to automating parts of data governance.
Cloud data governance: Today’s organization, in addition to storing and managing data on-premises, manages complex data pipelines that reach across the enterprise and in and out of a wide range of external entities such as providers and customers. What has also become a dominant architecture for many organizations in the past few years is storing, accessing, and managing data at cloud providers. Many businesses are choosing to use providers such as Amazon AWS, Microsoft Azure, Oracle Cloud, and Google Cloud. In addition, many of the external data sources consumed by an organization are cloud-based.
Identify the impact and value of data in your business
Design governance programs that fit your organization
Discover and adopt tools that measure performance and need
Address data needs and build a more data-centric business culture
This is the perfect handbook for professionals in the world of data analysis and business intelligence, plus the people who interact with data on a daily basis. And, as always, Dummies explains things in terms anyone can understand, making it easy to learn everything you need to know