What data management is.

  Data management is the process of collecting, storing, organizing, keeping, using and maintaining the data securely, efficiently, and cost-effectively so it can be analyzed for decision-making. Effective data management is a crucial piece of deploying the IT systems that run applications and provide analytical information.

  Managing digital data in an organization involves a broad range of tasks, policies, procedures, and practices. The work of data management has a scope, covering practices such as how to:

• Create, access, and update data;
• Store data across on premises and/or clouds;
• Provide high availability and recovery;
• Use data in apps, analytics, and algorithms
• Ensure data privacy and security
• Archive and destroy data in accordance with schedules and requirements.

  A formal data management strategy addresses the activity of users and administrators, the capabilities of data management technologies, the demands of regulatory requirements, and the needs of the organization to obtain value from its data. Today’s organizations need a data management solution that provides an efficient way to manage data. Data management systems are built on data management platforms and can include databases, data lakes and data warehouses, big data management systems, data analytics, etc.

The data management process involves a wide range of tasks, duties and skills. In smaller organizations with limited resources, individual workers may handle multiple roles. But in larger ones, data management teams commonly include data architects, data modelers, DBAs, database developers, data administrators, data quality analysts and engineers, and ETL developers. Another role that's being seen more often is the data warehouse analyst, who helps manage the data in a data warehouse and builds analytical data models for business users.

  Types of Data Management

  Data management plays several roles in an organization’s data environment, making essential functions easier and less time-intensive. These data management techniques include the following:

• Data preparation is used to clean and transform raw data into the right shape and format for analysis, including making corrections and combining data sets.
• Data pipelines enable the automated transfer of data from one system to another.
• ETLs (Extract, Transform, Load) are built to take the data from one system, transform it, and load it into the organization’s data warehouse.
• Data catalogs help manage metadata to create a complete picture of the data, providing a summary of its changes, locations, and quality while also making the data easy to find.
• Data warehouses are places to consolidate various data sources, contend with the many data types businesses store, and provide a clear route for data analysis.
• Data governance defines standards, processes, and policies to maintain data security and integrity.
• Data architecture provides a formal approach for creating and managing data flow.
• Data security protects data from unauthorized access and corruption.
• Data modeling documents the flow of data through an application or organization.

  Data management tools and techniques

  Developing a data architecture is usually the first step with lots of data to manage. A data architecture provides a blueprint for managing data and deploying databases and other data platforms, including specific technologies to fit individual applications.

  Databases are the most common platform used to hold data. They contain a collection of data that's organized so it can be accessed, updated and managed. They're used in transaction processing systems that create operational data, such as customer records and sales orders, and data warehouses, which store consolidated data sets from business systems for BI and analytics.

  That makes database administration a core data management function. Once databases have been set up, performance monitoring and tuning must be done to maintain acceptable response times on database queries that users run to get information from the data stored in them. Other administrative tasks include database design, configuration, installation and updates; data security; database backup and recovery; and application of software upgrades and security patches.

  Automated tools help database administrators (DBAs) automate many of the traditional management tasks, manual intervention is still often required because of the size and complexity of most database deployments. Whenever manual intervention is required, the chance for errors increases.

  The primary technology used to deploy and administer databases is a database management system (DBMS), which is software that acts as an interface between the databases it controls and the database administrators (DBAs), end users and applications that access them.

  The most prevalent type of DBMS is the relational database management system. Relational databases organize data into tables with rows and columns that contain database records. Related records in different tables can be connected through the using of primary and foreign keys, avoiding the need to create duplicate data entries. Relational databases are built around the SQL programming language and a rigid data model best suited to structured transaction data. That and their support for the ACID transaction properties -- atomicity, consistency, isolation and durability -- have made them the top database choice for transaction processing applications.

  However, other types of DBMS technologies have emerged as viable options for different kinds of data workloads. Most are categorized as NoSQL databases, which don't impose rigid requirements on data models and database schemas. As a result, they can store unstructured and semi structured data, such as sensor data, internet clickstream records and network, server and application logs, etc.

  Data integration. The most widely used data integration technique is extract, transform and load (ETL), which pulls data from source systems, converts it into a consistent format and then loads the integrated data into a data warehouse or other target system. However, data integration platforms now also support a variety of other integration methods. That includes extract, load and transform (ELT), a variation on ETL that leaves data in its original form when it's loaded into the target platform. ELT is a common choice for data integration in data lakes and other big data systems.

  ETL and ELT are batch integration processes that run at scheduled intervals. Data management teams can also do real-time data integration, using methods such as change data capture, which applies changes to the data in databases to a data warehouse or other repository, and streaming data integration, which integrates streams of real-time data on a continuous basis. Data virtualization is another integration option that uses an abstraction layer to create a virtual view of data from different systems for end users instead of physically loading the data into a data warehouse.

  We work with different DBMS such as PostgreSQL, MySQL, Oracle, MS SQL, MongoDB.

  Data analytics

  Data analytics is a discipline focused on extracting insights from data, including the analysis, collection, organization, and storage of data, as well as the tools and techniques to do so. It comprises the processes, tools and techniques of data analysis and management, including the collection, organization, and storage of data. The aim of data analytics is to apply statistical analysis and technologies on data to find trends and solve problems. Data analytics has become increasingly important in the enterprise as a means for analyzing and shaping business processes and improving decision-making and business results.

  Data analytics involves computer programming, mathematics, and statistics — to perform analysis on data.

  Analytics can be broken down broadly into four types:

• descriptive analytics, which attempts to describe what has transpired at a particular point in time;
• diagnostic analytics, which assesses why something has happened;
• predictive analytics, which ascertains the likelihood of something happening in the future; and
• prescriptive analytics, which provides recommended actions to take to achieve a desired outcome.

  Data analysts and others who work with analytics use a range of tools.

  While the terms data analytics and data analysis are frequently used interchangeably, data analysis is a subset of data analytics concerned with examining, cleansing, transforming, and modeling data to derive conclusions. Data analytics includes the tools and techniques used to perform data analysis.

  Business analytics is another subset of data analytics. Business analytics uses data analytics techniques, including statistical analysis, to drive better business decisions.

  We help you to create and support Database systems to get it working in the most efficient way. We provide solutions to create a new custom Databases based on the business requirements, or update/customize existing to improve it's functionality and meet your business requirements or personal goals.

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