Corporate data processing refers to the use of technology to collect, store, manipulate, and analyze large amounts of business data in a corporate setting.
Here are some of the key techniques used in corporate data processing:
Data Collection: The first step in corporate data processing is the collection of data from various sources such as internal databases in Excel spreadsheets, sites with such data as exchange rates, interest rates and so on, and third-party business software. The last item can include different types of tools, like enterprise performance management software, planning and budgeting software, resource planning software, S&OP (sales and operational planning) software and FA&P (financial analytics and planning) software. Data collection can be automated using tools such as web scraping or data integration software and tools, like REST API.
Data Storage: Once the data is collected, it needs to be stored in a secure and reliable manner. Companies can use different storage options, such as on-premise servers, cloud storage, or a combination of both. Choosing the right storage option is critical as it affects the accessibility, scalability, and security of the data. Read here how cloud storage can help to increase the efficiency of data processing.
Data Processing: After data is stored, it needs to be processed to derive insights and inform business decisions. Techniques such as data cleaning, data transformation, data consolidation and data enrichment are used to prepare the data for analysis.
Data Analysis: Once the data is processed, it is analyzed using statistical techniques such as regression analysis, clustering, and classification. Data visualization tools such as dashboards, reports or even ordinary tables in Excel templates are used to present the analysis results in a meaningful way.
Data Governance: Data governance is the process of managing the availability, usability, integrity, and security of the data used in an organization. It includes policies, procedures, and tools to ensure that data is used ethically, accurately, and consistently across the organization. As far as corporate data is usually confident, it’s important to have a granulated setting to manage users' rights. Read here about flexible security configurations.
Data Collection: The first step in corporate data processing is the collection of data from various sources such as internal databases in Excel spreadsheets, sites with such data as exchange rates, interest rates and so on, and third-party business software. The last item can include different types of tools, like enterprise performance management software, planning and budgeting software, resource planning software, S&OP (sales and operational planning) software and FA&P (financial analytics and planning) software. Data collection can be automated using tools such as web scraping or data integration software and tools, like REST API.
Data Storage: Once the data is collected, it needs to be stored in a secure and reliable manner. Companies can use different storage options, such as on-premise servers, cloud storage, or a combination of both. Choosing the right storage option is critical as it affects the accessibility, scalability, and security of the data. Read here how cloud storage can help to increase the efficiency of data processing.
Data Processing: After data is stored, it needs to be processed to derive insights and inform business decisions. Techniques such as data cleaning, data transformation, data consolidation and data enrichment are used to prepare the data for analysis.
Data Analysis: Once the data is processed, it is analyzed using statistical techniques such as regression analysis, clustering, and classification. Data visualization tools such as dashboards, reports or even ordinary tables in Excel templates are used to present the analysis results in a meaningful way.
Data Governance: Data governance is the process of managing the availability, usability, integrity, and security of the data used in an organization. It includes policies, procedures, and tools to ensure that data is used ethically, accurately, and consistently across the organization. As far as corporate data is usually confident, it’s important to have a granulated setting to manage users' rights. Read here about flexible security configurations.
Technology
Such business activities as planning and budgeting are base on a vast arrays of data, and it may be crucial to find the most suitable technology for business software to process such databases. There are many different technologies available for databases, each with its own strengths and weaknesses. Here are some of the most common database technologies:
Relational databases: These are the most common type of database and are based on the relational model of data. Data is stored in tables, with each table representing a different entity or concept, and relationships between tables are defined using foreign keys. Examples of relational database management systems (RDBMS) include MySQL, Oracle, and Microsoft SQL Server.
NoSQL databases: These databases are designed to handle unstructured or semi-structured data, such as social media data or sensor data. NoSQL databases do not use a strict schema, instead using flexible data models such as key-value pairs or document-based data structures. Examples of NoSQL databases include MongoDB, Cassandra, and Redis.
Graph databases: These databases are designed to handle data with complex relationships, such as social networks or recommendation engines. They use graph structures to represent data, with nodes representing entities and edges representing relationships between nodes. Examples of graph databases include Neo4j, OrientDB, and ArangoDB.
Object-oriented databases: These databases are designed to handle data that is closely related to object-oriented programming languages, such as Java or Python. Data is stored as objects, which can be manipulated using object-oriented programming techniques. Examples of object-oriented databases include db4o and Objectivity/DB.
In-memory databases: These databases store data in memory instead of on disk, allowing for faster access times and better performance. They are often used in applications that require real-time data processing, such as financial trading systems or gaming applications. Examples of in-memory databases include SAP HANA, Oracle TimesTen, and Redis. Read about how it is made in Spreadym.
Each of these database technologies has its own unique features and benefits, and the choice of database technology depends on the specific needs and requirements of the application.
Relational databases: These are the most common type of database and are based on the relational model of data. Data is stored in tables, with each table representing a different entity or concept, and relationships between tables are defined using foreign keys. Examples of relational database management systems (RDBMS) include MySQL, Oracle, and Microsoft SQL Server.
NoSQL databases: These databases are designed to handle unstructured or semi-structured data, such as social media data or sensor data. NoSQL databases do not use a strict schema, instead using flexible data models such as key-value pairs or document-based data structures. Examples of NoSQL databases include MongoDB, Cassandra, and Redis.
Graph databases: These databases are designed to handle data with complex relationships, such as social networks or recommendation engines. They use graph structures to represent data, with nodes representing entities and edges representing relationships between nodes. Examples of graph databases include Neo4j, OrientDB, and ArangoDB.
Object-oriented databases: These databases are designed to handle data that is closely related to object-oriented programming languages, such as Java or Python. Data is stored as objects, which can be manipulated using object-oriented programming techniques. Examples of object-oriented databases include db4o and Objectivity/DB.
In-memory databases: These databases store data in memory instead of on disk, allowing for faster access times and better performance. They are often used in applications that require real-time data processing, such as financial trading systems or gaming applications. Examples of in-memory databases include SAP HANA, Oracle TimesTen, and Redis. Read about how it is made in Spreadym.
Each of these database technologies has its own unique features and benefits, and the choice of database technology depends on the specific needs and requirements of the application.
