Source System: Understanding Its Role in Data Management

Data management is the practice of collecting, storing, organizing, and protecting all the data collected from a wide range of sources, known as source systems. A source system is any system or file that provides valuable data to a business. Often, professionals access and utilize multiple source systems at any given time, which makes data management a complex task.

Read on to learn more about source systems and the important role they play in data management.

What is a source system?

Source systems are any files or systems that capture or store data for importation into data warehouses. These might be legacy systems or ones purchased from a third-party data collector. Because source systems tend to come from many different places, it’s typical to need software that sorts this data into a standardized format. Companies often use ETL (extract, transform, load) software and processes to consolidate the data and load it into a data warehouse.

Types of source systems

Source systems come in a wide range of types to accommodate all the different data sources. Three common types of source systems include transactional systems, operational systems, and external data sources, as detailed below.

Transactional systems

Transactional systems are a type of database that tracks both a customer’s and a business’s data during a transaction. They support transactions that occur quickly and accurately, capturing the data from those actions and storing it for later analytical use.

Operational systems

Operational systems involve all of the hardware and software necessary to support a business’s daily tasks and interactions. This might include systems such as customer experience software or order processing software.

External data sources

External data sources are data sources that come from outside of a business. These might include government sources, social networks, job boards, or external websites.

Key components of a source system

Because source systems are responsible for collecting and storing massive amounts of data every day, they must have three key components to help them achieve that task efficiently and accurately: data storage, data processing, and data integration.

Data storage

Data storage is the optical, mechanical, or magnetic media that preserves and records digital information for future use. This information is either input data, provided by the customer, or output data, provided by the computer. Data storage allows this data to exist on something other than the computer, so even when the computer isn’t on, the data is retained.

Data processing

Data processing is the action of taking data and extracting insights and analysis that provide value to a business’s decision-making processes. The right data processing system makes data easier to use and helps you extract the most information from it.

Data integration

Data integration is the process of combining data from a wide range of source systems and standardizing it into a single format that makes it easier to search, interact with, and analyze.

Best practices for managing source systems

Source systems are the foundation of data management since they are the originators of the data. As such, it’s important to follow best practices for source system management to obtain more accurate and valuable data.

Data quality management

Data quality management is the process of examining collected data from source systems for timeliness, validity, uniqueness, consistency, accuracy, and completeness. If data is poor quality—meaning it’s inaccurate, duplicated, or has outliers—a business risks losing money due to decisions made based on this faulty data. High-quality data helps a business make better decisions, improve processes, and create positive customer experiences.

Regular audits

Regular audits help ensure that the data itself adheres to the correct guidelines and regulations and is accurate, secure, and consistent. Second, audits ensure that the business’s processes and practices keep the data’s integrity intact.

Documentation

Source documentation provides an important framework to ensure the raw data a source system collects is accurate and useful.

Who uses source systems?

You can find a wide variety of jobs that use source systems. These roles include:

Data analysts and scientists

Data analysts and scientists interact with the data collected from source systems as the main focus of their roles. They sort, analyze, and extrapolate from this data, then use those insights to create recommendations and reports that people in non-technical roles can read, understand, and implement. Data analysts and scientists use a wide range of reporting tools, such as data visualization software, to demonstrate insights and trends over time.

Business intelligence teams

Business intelligence teams use data to develop valuable insights and recommendations about their competitors and the marketplace they function within. They synthesize the data into reports that clients and stakeholders can use to determine strategic plans or next steps for the business to reach specific goals.

IT departments

IT departments, with professionals such as computer programmers, use source systems to collect, process, store, and analyze data as directed by the business’s leadership teams and strategic goals. Often, these professionals are responsible for creating the frameworks, code, or infrastructure to collect and store source system data.

Challenges in managing source systems

Because source systems almost always originate from a wide variety of websites or software, they tend to lack standardization when it comes to important aspects such as format, structure, or data types. This lack of cohesion can make it challenging for professionals to collect, sort, and analyze the data. It can also impact a business’s ability to scale, as more source systems may mean more money, employees, or infrastructure to support the accurate collection of data.

Future trends in source systems

One future trend in source systems is the desire for more data sovereignty. As businesses continue to rely on source systems to collect data from a wide range of places, professionals hope to move away from having the systems dictate how to interact and utilize the data and would rather have more flexibility and freedom in how they access the data.

Security has also become a major trend as businesses grow more cognizant of the need to protect incoming data and prevent it from compromising their software systems.

Learn more about data management on Coursera

Source systems are the building blocks of an effective data management system, as they are the originators of all the data. Learn more about the foundations of data management with courses and certificates on Coursera, such as IBM’s IBM Data Warehouse Engineer Professional Certificate, where you’ll learn how to design and populate data warehouses and analyze their data with Business Intelligence (BI) tools like Cognos Analytics.