By describing a set of architectural patterns, key concepts, and other re-usable artifacts, it intends to help your organization leveraging new disruptive big data solutions and setting up an associated data-centric strategy for an increased performance and competitiveness.
A good data governance platform is one that crosses the silos in your business and drives collaboration amongst the people who use the data, in line with your corporate strategy. Identify the MDM implementation style that best suits the needs of your organization. The incredible growth in data produced at the edge, data which must be processed, stored, and moved is driving the need to have a broader, more holistic innovation model.
Operational systems contain the data required for the day-to-day operations of your organization. A successful data management plan requires that the appropriate staffing resources are available and trained. As the strategy is implemented, it will help your organization provide fast and secure access to data whenever users require it. A data warehouse is an electronic system that gathers data from a wide range of sources within a company and uses the data to support management decision-making .
On which component of the data lake the data exists now and how it relates to the other data in the organization. Lets explore some key benefits as well as the steps you need to consider to achieve a modern data architecture in the cloud. Introducing a data lake to modernize your data architecture can be an effective way to continue leveraging existing investments, begin collecting new types of valuable data, and ultimately obtain insights faster.
Deploy anywhere with a modern, multi-cloud architecture, providing data on-demand to all your users with enterprise-grade security and governance. Leaders are looking for proven techniques to deliver accurate information timely and cost-effectively. It is focused squarely at the data integration efforts across your enterprise and is built from solid foundational concepts.
A data warehouse is a large repository of historical data that can be integrated for decision support. Architects realize business objectives by translating strategy from strategic themes into solutions. Successful data lakes require data and analytics leaders to develop a logical or physical separation of data acquisition, insight development, optimization and governance, and analytics consumption.
Data modernization is essential for successfully interfacing data across many different types of systems while ensuring highest quality standards in real time. However, to see these benefits, its important to understand how to structure your data lake architecture in the cloud, which is a bit different than a traditional on-premises architecture.
With rising expectations for service quality and speed from customers, data analytics has become essential for digitizing operations to improve business practices. Traditional data warehousing and analytical systems can be complex and slow to adapt. A data lake makes it easy to store, and run analytics on machine-generated IoT data to discover ways to reduce operational costs, and increase quality.
Want to check how your Data Lake Architecture Strategy Processes are performing? You don’t know what you don’t know. Find out with our Data Lake Architecture Strategy Self Assessment Toolkit: