Data management is the practice of collecting, storing and using data in a secure, efficient and economic manner. The purpose of data management is to help people, businesses and networked devices to make optimal use of their data in accordance with company policies and laws, so that the decisions and actions taken allow the company to achieve maximum benefit. An advanced data management strategy is now more important than ever as companies increasingly benefit from intangible resources.
Digital data management in an enterprise encompasses a wide range of tasks, rules, procedures and practices. Data management includes, among others, the following activities:
-Creating, accessing and updating data at different levels
-Data storage on multiple cloud and local platforms
-Ensure a high level of availability and disaster recovery
-Using data in an increasing number of applications, analyses and algorithms
-Ensuring privacy and data security
-Archiving and destruction of data according to storage schedules and compliance requirements
-The formal data management strategy refers to the activity of users and administrators, the capabilities of data management technologies, regulatory requirements and the needs of the enterprise to generate benefits from the collected data.
Modern data management systems
Today's businesses need effective data management solutions in a diverse but unified data layer. Data management systems are based on data platforms and can include databases, lakes and data warehouses, Big Data management systems, data analysis tools, etc.
All these components form a coherent mechanism of data processing in an enterprise for its applications and analyses and algorithms using data from these applications. Although current tools help database administrators (DBA) automate many traditional management tasks, manual intervention is often necessary due to the size and complexity of most implemented databases. Whenever manual intervention is required, the risk of errors increases. Reducing manual data management is the main objective of a new technology for data management - the so-called autonomous database.
Large Data Volume Management Systems (Big Data)
In a way, Big Data technology is exactly what its name suggests. It's simply technology that handles very large data sets. But Big Data also means a greater variety of data forms and a very high rate of data collection. Think of all the data generated daily or every minute by social media like Facebook. The amount, diversity and speed of this data generation makes it very valuable for businesses, but also its management is very complex.
More and more data comes from very different sources such as video cameras, social media, audio recordings and IoT devices (Internet of Things, Internet of Things), so special Big Data performance management systems had to be created. These systems specialise in three general areas:
-Big Data integration tools collect different types of data - from batch packs to streaming files - and transform them for later use.
-Big Data management tools store and process data in lakes or data warehouses in an efficient, secure and reliable way, often using object storage.
-Big Data Analysis Tools allow you to get new information through analysis and use machine learning and AI visualization to build models.
-Companies use Big Data technology to improve and accelerate product development, predictive maintenance, and to improve customer service, safety, operational efficiency and many other goals. As the number of Big Data increases, so do the opportunities.
Most of the challenges in today's data management are due to the faster pace of business and the increasing dissemination of data. The increasing diversity, speed and volume of data available to businesses is forcing them to seek more effective management tools to keep pace with change. These are some of the most important challenges businesses face:
-Businesses do not know what data they have. Data is collected and stored from an increasing number of sources such as sensors, smart devices, social media and video cameras. But none of this data is useful if a business does not know what data it has, where it is and how to use it.
-Businesses need to maintain high levels of performance despite the growth of the data layer. They capture, store and use more and more data all the time. To keep response times in this growing layer as short as possible, businesses need to constantly monitor the type of questions answered by the database and change indexes as the questions change - without affecting performance.
Enterprises must meet constantly changing compliance requirements. Compliance regulations are complex and subject to many jurisdictions and are constantly changing. Companies must be able to easily view their data and identify anything that is subject to new or changed requirements. In particular, this includes user-identifiable data (personal data) that must be detected and monitored for compliance with increasingly stringent global data protection laws.
Companies like https://www.enteros.com/are unsure how to modify data in order to use it for new purposes. The collection and identification of data alone does not provide any benefit - the company must process the data. If the conversion of data for analysis requires too much time and effort, the analysis will not take place. As a result, the company will lose the potential benefits of this data.
Companies must keep up with the changes in the data warehouse. In the new reality of data management, businesses are storing data in multiple systems, including data warehouses and unstructured data lakes, which store data in any format in a single repository. Corporate data analysts need a way to quickly and easily convert data from its original format to a format or model that allows for the widest possible analysis.
Solving data management problems requires a comprehensive, well thought-out set of best practices. Although specific best practices vary in terms of data type and industry, the following best practices address the main data management issues facing companies today:
-Create a detection layer to identify your company data. A detection layer located above all company data layers allows data analysts and other professionals to search and review data sets for later use.
-Create a data analysis environment to use the data efficiently. The data analysis environment automates most data transformation processes, improving the creation and evaluation of data models. A set of tools to eliminate the need for manual data transformation can speed up hypotheses and testing of new models.
-Use standalone technology to maintain the appropriate level of performance in an expanding layer of data. Stand-alone data processing functions use Artificial Intelligence and machine self-learning to continuously monitor database queries and optimize indexes as queries change. This allows the database to provide short response times and relieves database administrators and data analysts of time-consuming manual tasks.
-Use the discovery function to meet your compliance requirements. New tools use data detection techniques to view data and identify connection chains that need to be detected and monitored for compliance with multiple legal systems. As compliance requirements increase worldwide, this capability will become increasingly important for security and risk assessment professionals.
-Use a common query layer to manage many different forms of data storage. New technologies enable collaboration between repositories used to manage data, so that differences between them disappear. A common query layer covering multiple types of data storage allows data analysts and other professionals and data processing applications to access data without checking where it is stored and without having to manually convert it into a useful format.
Data management is evolving
Data is becoming the new capital of companies, so that companies increasingly see what the pioneers and innovators of the digital world have known for some time: data is a valuable resource to see trends, make decisions and gain competitive advantage. The new importance of data in the value chain allows companies to actively seek better ways to benefit from this new capital.
Companies are also changing the responsibilities of database administrators for data management - administrators no longer have to perform many tedious tasks and can focus on more strategic tasks and on managing sensitive data in cloud environments, including key tasks such as modelling and data security.