Data is fast becoming the cornerstone of competitive firms. And companies that want to obtain the most value from their data stores must find ways to ensure that their data is complete, accurate, up-to-date, and available to decision-makers. Data management is critical for firms that need to deliver clean, analytics-ready data to users across the enterprise. With the right data management tool in place, companies can improve the quality and accuracy of their information stores and make sure that users across the organization have current, clean, and correct data at their fingertips where and when they need it.
Data management consists of practices and tools used to ingest, store, organize, and maintain the data created and gathered by an organization. This includes tracking, validating, protecting, and processing data in order to provide consistent access to and delivery of reliable and timely data to end users. Data management is a foundational component of any data analytics initiative. This is because it helps in standardizing formats, eliminating redundancy, and preparing data for analysis. It helps ensure that an organization’s entire body of data is accurate, readily accessible, and properly secured.
Organizations that lack proper data management can find themselves struggling to integrate and coordinate incompatible data silos. Fragmented, inconsistent data limits a firm’s ability to run analytics applications and get accurate answers to their most pressing questions. Data management is increasingly important as data sources increase and diversify. Leveraging big data for analytics requires an effective data management environment in which data can be efficiently integrated and explored by users in search of actionable insights. By making it easier for users to quickly find and access the information they need to do their jobs, data management done right leads to increased productivity, more accurate reporting including KPI reporting, faster, smarter decision making, and a more agile and competitive enterprise.
Data management is composed of several key elements. These include:
*What is data modeling? It is a map showing the logical inter-relationships and data flow between different elements in a data set and is used to determine how data is stored and exposed to users.
Despite its vital importance for analytics initiatives, many firms still struggle to implement effective data management. Here are some best practices to consider before embarking on your next analytics journey:
Qlik Catalog® is a modern enterprise data management solution that simplifies and accelerates the delivery of trustworthy, analytics-ready data across the organization. Using built-in, high-performance loaders, you can onboard all your existing data—from a wide variety of source types and locations—and build a secure, enterprise-scale catalog faster than you may have imagined.
Qlik Catalog helps you to better prepare your data by automatically validating, profiling, and documenting the content, structure, and quality of your data, transforming raw data into business-ready data. Users can easily standardize, enhance, blend and filter data using simple drag-and-drop functionality—with no coding involved. Qlik Catalog enriches your information with technical, business, and operational metadata, building a smart data catalog that makes each information asset understandable and actionable.
An integrated, secure data catalog functions as a data marketplace, letting the user “shop” for the data they need and quickly export, share, or publish their datasets to Qlik Sense® and other BI tools of choice as well as cloud repositories. Enterprise-grade data governance and security protect your data assets while providing users with fast, on-demand access to accurate, complete, up-to-date data. With Qlik Catalog , users spend less time preparing data and more time using it to do their jobs better.
Without effective data management, an organization’s data stores, which are distributed across various applications and systems, can become incompatible data silos, and leave firms with fragmented, inconsistent, and inaccurate data sets. Big data environments can quickly become cumbersome and difficult to navigate. Proper data management helps organizations coordinate data across diverse systems, ensure the integrity and accessibility of their data assets, and provide users with the ability to find high-quality data they need to leverage powerful analytics capabilities including big data analytics.
The goal is to make sure that the data an organization creates, collects, and stores is consistent, accurate, properly secured, and readily accessible to users.
The most common technique is ETL, or extract, transform, and load. Using ETL, data is pulled from various source systems, converted into a consistent format, and loaded into a data warehouse or other type of system. When moving data into a data lake and other big data repository, ELT, or extract, load, and transform is often used. With ELT, data is left in its original form when loaded into the target system. Data management teams may also use real-time data integration methods such as change data capture or streaming data integration.
They are all repositories for managing analytics data. Data warehouses are used to store data that has been extracted from various operational systems and transformed, or pre-prepared for analysis. Data marts are basically a smaller version of a data warehouse and hold subsets of an organization’s data for use by specific groups of users. Data lakes, on the other hand, often contain raw or untransformed data and are used to store large quantities of structured and unstructured data for predictive analytics and other advanced analytics use cases.