Data management is the process of capturing, storing, arranging and maintaining the data generated and gathered by a company. Effective data management plays an important role in IT systems operating business apps and offering information on which decisions are made correctly and strategies are planned well by company executives, business managers as well as other users.
The data management process comes with a suite of various functions whose major purpose is to guarantee that the data in corporate systems is correct, available and can be accessed when needed. Most of the necessary work is carried out by IT and data management teams, yet business users also join some tasks in the process to make sure the data adapts to their requirements.
In the following post, you will be guided to data management, particularly what it is and what it includes and some data management tools as well as techniques.
The importance of data management
Nowadays, data is considered to be a business asset that can be taken advantage to generate business decisions in a more informed way, advance marketing campaigns, optimize business work and eliminate unnecessary spending, all of which contribute to increasing revenue and profits. However, the lack of a suitable data management strategy can lead the companies to such problems as data silos, inconsistent data sets and data quality challenges that will prevent them from operating business intelligence and analytics apps, even faulty findings.
Due to the fact that companies have to operate under a lot of regulatory compliance requirements such as data privacy and protection laws, data management has now become more important than ever. Apart from that, businesses are also capturing larger amounts of data and many different data types. Without good data management, it will be hard to navigate.
Types of data management functions
The different disciplines that belong to the whole data management process come with various steps, including data processing and storage to data governance as well as how data is used in operational and analytical systems. Development of a data system is normally the first step, especially when it comes to big companies with a huge amount of data to control.
Data bases are the most widely used platform to keep data of the business, in which there is a collection of data organized for being accessed, updated and managed. They are taken advantage in both transaction and processing systems generating operational data, like customer records and orders, along with data warehouses in which data sets from business systems are consolidated.
Database administration is a main function of data management. When databases have already been established, performance monitoring and tuning will be carried out in order to maintain the suitable response times for database demands that users operate in order to capture information from the data inside them. Other administrative tasks can be listed as database design, configuration, installation and updates, data security, data backup, data recovery, software upgrades and so on.
The major technology needed to manage databases is called database management system, which is a type of software working as an interface between the databases it manages and the database admins, users and apps access. Other data platforms for databases are file systems and cloud storage services, in which data is stored is less structured manner in comparison with main stream databases, which provides more flexibility on data types stored and how it is formatted.
Other important data management disciplines include data modeling, which diagrams the relationships between data parts and how it flows through the systems; data integration, which combines data from various data sources for analytic uses; data governance setting policies and procedures to make sure the data consistency in the company and data quality management, which is aimed at adjusting the errors and inconsistencies found in data The final one is data management, creating a set of reference data on such aspects as customers and products.
Data management tools and techniques
There are a lot of technologies, tools and techniques used in the data management process. Some of them are clarified in the following section.
First and foremost, the most prevalent kind of database management system is the relational database management system. Relational databases will arrange data into different tables with rows and columns coming with database records. Related records in those tables can be linked together by using primary and foreign keys. Relational databases are set up around the SQL programming language along with a rigid data model to suit the structured transaction data.
On the other hand, there are some other types of database management technologies have been released as viable option for a wide variety of data workloads. Most are clarified to be NoSQL databases, which do not impose rigid demands on data models and database systems so that they can store both unstructured and semi-structured data.
There are four major types of NoSQL systems, including document databases storing data in document structures, key databases connecting unique keys and their related values, wide column stores with tables coming with many columns and graph databases connecting related data elements in a graph format.
Some other database and database management options are in-memory databases that keep data in a server’s memory rather than the disk in order to boost I/O performance. Hierarchical databases which operate on mainframes are also available for being used. Users can also deploy databases both in premises or on cloud. What is more, different database providers will provide different managed cloud database services that they can deal with database deployment and administration.
In addition, NoSQL databases are often taken advantage in big data deployments due to their capability of storing and managing a wide variety of data types. Big data environments are also constructed with such open source technologies as Hadoop, HBase database, the Spark processing engine and the Kafka, Flink along with Storm stream processing platforms. Currently, more and more big data systems are built in the cloud with such option as Amazon Simple Storage Service.