What Is Data Management?
Data-driven IT professionals are in high demand across all industries with positions in data management among the fastest-growing occupations. Whether you have interests in manufacturing, finance, healthcare or insurance, among others, big data needs data-driven, opportunity-focused professionals who have sharply developed analytical skill sets.
In this article, we will define data management, explore what data management systems involve and look at some best practices that address today's challenges with data management systems.
What is data management?
Data management deals with practices, plans, programs and policies that protect, control and enhance the value of information and data assets through their life cycles, which is the flow of information system's data from creation to initial storage to it becoming obsolete and eventually deleted.
Through procedures and policies, data management gives organizations control over the data of their business. With proper management of data, there are minimal risks for security breaches and reduced costs for legal and regulatory compliance. Data management allows companies to access accurate data wherever or whenever it is required, and it diminishes the likelihood of miscommunication.
Big data management systems deal with a ton of data that comes in a large variety of structures—more than traditional data. It is collected rapidly. Just imagine the rate at which data comes each minute from platforms, like social media sources. Big data is hugely beneficial to organizations because of the variety, amount and speed of data.
Big data management allows for the integration of different types of data so that administrators can transform information for human consumption. The data gets stored and processed and analyzed to uncover new insights with analytics, often with the help of artificial intelligence (AI) and machine learning.
Big data can accelerate product development, which is why many companies use it. It can also improve the customer experience, operational efficiency and security. Big data is growing, and so are the opportunities for data management.
Related: What Does a Data Manager Do?
What is the purpose of data management?
A data management system offers an efficient way to control data across a unified data tier—despite it being diverse. The data management platforms on which data management systems are built, include:
Data analytics: The science of taking raw data and using techniques to come up with conclusions or discover trends and metrics to help optimize and increase the overall efficiency of a business.
Databases: Organized collections of data that are electronically accessible from a computer system.
Big data management systems: Contain large volumes of unstructured and structured data to ensure the high-level quality of information for big data analytics and business intelligence.
Data warehouses: Repositories for filtered and structured data that has been processed for a definite purpose.
Data lakes: A large pool of raw data that has yet to be given a particular purpose.
These management components serve as the data utility needed for a company's apps and the algorithms and analytics that use data created by their apps. Manual intervention is still required, even though database administrators have tools that automate much of the traditional management tasks.
There is a chance that there will be errors when manual management is involved—putting autonomous databases at the forefront of new data management technology, which seeks to reduce the need for manual data management.
Related: Learn About Being a Data Analyst
What do data management systems do?
Data management involves a broad scope of practices, tasks, procedures and policies. It covers factors such as
Ensuring data security and data privacy
Creating, updating and accessing data over a diverse data tier
Using data in an increasing number of analytics, apps and algorithms
Storing data across a various number of premises and clouds
Providing disaster recovery and high availability
Archiving and erasing data following compliance requirements and retention schedules
The strategy used with formal data management deals with the activity of administrators and users, the demands of regulatory requirements, the capabilities of data management technologies and the needs of a company to get value from its data.
Best practices for data management systems
Over the last few years, the amount of data accumulated as grown a lot. Aside from data volume, there are other challenges organizations face when it comes to data management, like decentralization and distribution of data and security, among other concerns. It's necessary to approach the data management challenges with thoroughly thought out and comprehensive best practices.
In general, the industry a company is in and the type of data involved will determine specific best practices. However, the best practices below tackle a few of the main challenges companies face with data management:
Adopt a common query component to handle diverse and multiple forms of storage for data: With new tech, a standard query layer that covers the various kinds of data storage will enable analysts, data scientists and applications to have access to data eliminating the need for knowing its whereabouts and the need to transform it manually into a usable format.
Develop a discovery layer to identify data: Data can become more usable by data scientists and analysts with a discovery layer created on top of a company's data tier.
Discovery can keep organizations in compliance: With compliance requirements, there are new tools that use data discovery for identifying the chains of connections that need monitoring, tracking and detecting. Discovery allows for a review of data that, as global demands for compliance increase, will become all the more important to risk and security officers.
Maintain performance levels across the data tier with autonomous technology: As queries change, indexes will need optimization and autonomous data capabilities can use AI and machine learning to monitor database queries continuously. As a result, databases will keep rapid response times, so database administrators and data scientists won't have as many time-consuming tasks.
Repurpose data by developing a data science environment: As much as possible, data transformation work can be automated in a data science environment. Also, the process of creating and evaluating data models can be streamlined. By using specific tools, organizations can cut out the need for manual transformation of data, which can speed up the tasks of hypothesizing and testing new data models.