“Without big data analytics, companies are blind and deaf, wandering out onto the web like deer on a freeway.” That’s according to the author and consultant Geoffrey Moore. It’s an unsettling thought given that data and analytics are shifting from a secondary activity to a core business function. So what can companies do to gather and harness all of this information? The below guide discusses data sources and their role in informing decision-making.
Table of Contents
- What is a Data Source?
- Why Are They Important?
- The Value of Systems Integration
- Where Does The Data Come From?
- Common Challenges in Managing Data Sources
- How Integrate.io Can Help
What is a Data Source?
A data source is a place where information is obtained. The source can be a database, a flat file, an XML file, or any other format that a system can read. The input is recorded as a collection of records that contain information used in the business process. That information can include customer details, accounting figures, sales, logistics, and more.
Why are they Important?
Knowledge can help businesses respond to changing market conditions, deal with logistics challenges or identify new ways to improve the customer experience. These details can provide you with a unique perspective of your business operations.
The Value of Systems Integration
Types of Data Sources
Systems integration enables businesses to merge input from multiple systems. Leaders can then use this knowledge to inform their business strategy. So where does this information come from? It can come from a variety of sources and can help provide rich insights into the business. The most common sources are:
A database is a collection of information organized in tables made of rows and columns. There are several types of databases:
Warehouse - Information collected from multiple sources and used for querying is stored in a warehouse.
Transactional - With a transactional database, input is organized by date or timestamp. As a result, these databases can roll back or undo an operation if a transaction fails.
Time Series - A time-series database handles real-time system and user logged activities. Stock exchange statistics would be an example of details stored in a time-series database.
Database - These types of databases store geographical details as coordinates and topology figures.
Input stored in flat files is not relational in the same way as a database. It is usually stored in text or binary form that can be extracted by analytics algorithms.
Where Does the Data From?
Figures used for business intelligence can come from a variety of sources.
Internal input is captured by the systems you use within your organization. It can be things such as
- Email marketing statistics
- Customer profiles
- Online activity
External input comes from sources outside the company. The information can include things such as market prices, the weather, or social media trends. Companies use it to analyze the economic, social, and environmental factors that affect their business.
Companies can optionally use facts and figures that are free and accessible to everyone to use. The downside is that it is often too high-level or not aggregated in a way that is useful or relevant to your organization.
Common Challenges in Managing Data Sources
The more systems your company uses, the more likely you are to face challenges in managing it. Users providing details via your website, sales reps entering leads, tech support creating tickets, and more each generates masses of information. With so much information, things can get complex.
When the same input is entered across multiple systems, you end up with redundancy. This usually happens from poor initial database design. It can also happen as the system evolves and organizations patch together fixes to implement new changes.
Inconsistency occurs when dealing with multiple tables in a source that contains the same figures. For example, this situation may arise when the same records exist multiple times in the same source system. The issue is compounded by redundancy when the same figures exist across multiple sources.
Every input you collect has a timestamp of when it was created. The input begins to age the moment it is captured, not when it is moved to the warehouse. Because companies rely on the warehouse for real-time analytics, the figures must reflect the most recent input. Stale figures lead to inaccuracies in reporting.
Manually transferring data can be time-consuming and is prone to human error. Integrate.io’s Extract, Transform and Load (ETL) tool can minimize these errors by automating the process.
Legacy systems present a unique challenge to IT teams. A legacy system is described as an old technology or program that is outdated. These systems are difficult to. Getting data out of these systems often requires special knowledge of the system and the programming language used.
How Integrate.io Can Help
So what is a data source? Simply put, it is a key element in your company’s arsenal to inform and support your business strategy.
The Integrate.io integration platform can help you consolidate the information from your systems. The low-code tool features a user-friendly dashboard and hundreds of pre-built integrations. Get in touch with our team and experience the Integrate.io platform for yourself.