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.

Why data management is critical for data-driven firms

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.

The fundamentals of data management

Data management is composed of several key elements. These include:

• Data governance, which involves planning the many aspects of data management including the development of data definitions and usage policies to ensure the integrity, availability, and security of data
• Data architecture, which is an evolving documentation of the rules and policies governing the overall structure of an organization’s data, or how data is collected, stored, transformed, distributed, and accessed
• Data modeling, which involves the creation of data models* diagramming the workflows and relationships between data elements in data sets to ensure that business processing and analytics needs are met
• Data security, which involves the definition and enforcing of rules and procedures to protect data and ensure the privacy, confidentiality, and appropriate access to sensitive data
• Data integration, which refers to the processes used to extract, transform, move, deliver, replicate, and federate data, or load and combine data from various sources into a repository or information system
• Data warehousing, which involves the storage and management of data used for analytics, business intelligence, reporting, and decision support systems in data warehouses, data marts, and data lakes
• Data quality, which refers to practices and techniques like data profiling that are used to identify and resolve errors and inconsistencies in datasets and monitor and maintain the integrity of data

*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.

Best practices for effective data management

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:

• Involve executives and business users in the process. Consult the people that require access to the data to ensure that their needs are met.
• Create a data catalog or data discovery layer that allows users to search for the datasets they need, making data access simple.
• Share metadata across data management and analytics teams to promote collaboration and make it easier to deploy models.
• Automate data processing work. Use tools that eliminate the need for manual processing of data to streamline the analytics process.

How Qlik makes data management simpler and more effective

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.

FAQs

Why is data management important?

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.

What is the goal of data management?

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.

How is data integration done?

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.

What is the difference between a data warehouse, a data mart, and a data lake?

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.

Learn more about Qlik’s modern enterprise data management solution.

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