Data management is an administrative and governance process for acquiring, validating, storing, protecting, and processing organizational data to guarantee access, accuracy, and timeliness for downstream data users. With the exponential growth of Big Data, enterprises of all sizes are generating and consuming vast quantities of data in order to generate business insights into trends, customer behavior, and new opportunities.
In the age of Big Data, it has become a core function for many businesses. In order to meet stakeholder demands for data accessibility, reliability, and timeliness, companies are more frequently turning to data management platforms and solutions to make essential tasks quicker and less error-prone and resource intensive.
Master Data Management (MDM), a related discipline, provides the data consolidation and organization needed to create an accurate and all-encompassing view of business data as well as a singular view of customer profiles.
Data Management Strategy
Data management strategy is the process of planning or creating strategies/plans for handling the data created, stored, managed and processed by an organization.
While data management is, at a high level, the practice of organizing and maintaining data across a department or organization, the strategy behind it involves mapping out the systems, procedures, workflows, and security needed to handle institutional data assets. It is a data governance project that is typically run by IT or specialized data professionals.
Sometimes referred to as the last mile of data analytics, the strategy should deliver a step-by-step road map for how when, and where data is collected, processed, and accessed. It provides a solid foundation for consistency, successful integration, and reaching business data objectives. A strategy helps companies avoid many of the pitfalls associated with data handling, including duplicate or missing data, poorly documented data sources, and low business value, resource-intensive processes or workloads.
Snowflake and Data Management
In the past, companies created data warehouses and separate physical data marts that allowed multiple groups of stakeholders and data consumers to store and analyze data from enterprise applications. Next came data lakes, driven by steady advancements in data science and a desire to store and examine non-relational data types. Today, organizations commonly mix data processing technologies and analytics techniques, but each of these methods provides limited insight from a unique slice of data.
Snowflake's platform helps businesses with cloud data management by providing a highly scalable and fully elastic platform in the Data Cloud. By offering cloud data warehousing, data lake, data sharing, data engineering, and data application development on one platform, businesses can avoid many data challenges from day one.
Snowflake offers a near-zero management foundation for running any workload, including data warehouses, data lakes, and many types of data engineering and data science applications. It includes a unified repository powered by a comprehensive layer of services for security, governance, data sharing, metadata management, and transaction management, bringing data consistency to all types of analytic projects. By enabling easy and secure data sharing, Snowflake can also reduce time-consuming and often error-prone ETL processes that can adversely impact MDM.
Snowflake also works with leading data management partners such as Informatica to help bring all critical organizational data together.