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What is Data Integration? The What, Why & How
This guide will help you understand what data integration is, why it’s important, and how you can do it successfully. Looking for a data expert to handle it for you? Explore our data management solutions for analytics.
Data integration is the process of combining data originating from various sources within an organisation. The objective is to construct a comprehensive, precise, and current dataset that serves as a foundation for business intelligence (BI), data analysis, and other crucial applications and operational procedures.
Data replication, ingestion, and transformation are performed in order to combine different types of data into standardised formats that can be stored in data warehouses or data lakes.
It’s different from adata migration, which is an end point that culminates in the transfer of all required information to a designated destination. A data integration can however be a continuous process with real-time data flowing into a new system.
Business benefits of data integration
Exploring the advantages of implementing data integration solutions for businesses. How data integration can lead to better decision-making, improved efficiency, and increased competitiveness.
Improve collaboration and unified your systems
The need for employees to have quick and easy access to company data for projects is very important in today's digital world. With team members working in different departments and even different physical locations, there is a requirement for data to be accessible, but it also needs to be secure.
Employees across multiple departments are also generating new data and improving existing data that can benefit the entire business, so it should be easily shareable and accessible by those who can use it.
Saves time and boosts efficiency
Effective data integration practices will help a business substantially reduce the time required for data preparation and analysis. The automation of data cleansing involves using software tools and algorithms to detect and correct errors and inconsistencies in the data in real time.
This eliminates the need to manually check data. It also saves your team time as they’re no longer required to build connections from the ground up each time they run reports or build applications.
Opting for appropriate tools, rather than manually coding the integration, also increases the amount of time saved which can be put to better uses. You’ll have more hours that can be used to analyse data to find key insights for your business.
Reduces errors and time spent reworking
When managing a company's data resources, there are lots of things to consider. When manually gathering data, employees must know where all data locations and accounts are located, and they also need the necessary software installed to guarantee the data sets are accurate.
If an employee doesn’t know a new data set has been added, they will be working with incomplete data. The absence of a synchronised data integration solution also means reporting is required to be redone periodically to account for any changes.
Finding valuable data
A business can improve their data value over time by focusing on improving their data integration. Having data within a centralised system can help identify quality issues and necessary improvements can be made, which leads to more accurate data to analyse. Data cleansing is a key part of data integration to ensure the data being moved over is accurate.
Increased data governance
It’s important that businesses understand data ownership, access and usage to ensure data compliance is maintained. Data governance can ensure data is not misused or accessed without authorisation.
Data integration technologies emerged in response to the widespread adoption of relational databases and the increased demand for efficient data movement among them. This usually involves data at rest. Application integration works slightly differently and manages the seamless integration of live, operational data in real time across at least two applications.
The main aim of application integration revolves around enabling independently created applications to operate together harmoniously. In order to achieve this, there needs to be consistency within each of the separate copies of data, the flow of tasks executed by different applications needs to be managed, and there needs to be one user interface or service that the data can be accessed through.
Cloud data integration is a popular solution for achieving application integration. This encompasses different tools and technologies that interconnect various applications, allowing for real time exchange of data and processes. It also enables a variety of devices to access this cloud based data over the internet or through a network.
Data integration should be approached based on distinct business requirements. Here are some common use cases for data integration tools:
Leveraging big data
Data lakes are usually very complex and have lots of data within them. Google and Twitter are two examples of companies that deal with a vast amount of data from billions of users. This is classed as big data.
With more organisations appearing that handle big data, there is an increasing reservoir of data becoming accessible for businesses to leverage. As a result, the need for sophisticated data integration becomes paramount for many of these organisations.
Creating data warehouses and data lakes
In large enterprises, data integration often leads to the creation of data warehouses. These warehouses bring together data from various sources into one database, allowing users to run queries, generate reports, perform analyses, and access data consistently.
To illustrate, numerous businesses depend on data warehouses such as Microsoft Azure and AWS Redshift as essential tools for deriving business intelligence insights from their data reservoirs.
Simplifying business intelligence (BI)
Data integration is able to simplify business intelligence (BI) by providing a cohesive representation of data derived from diverse sources. This simplification enables organisations to effortlessly access and promptly comprehend the array of available datasets, facilitating the extraction of actionable insights into the current business landscape.
Through data integration, analysts can consolidate a wealth of information, ensuring more precise evaluations without becoming overwhelmed by voluminous data sets.
In contrast to predictive analytics, which anticipates future trends, BI primarily focuses on the present and historical data to inform strategic decision-making. This application of data integration fits perfectly with data warehousing, presenting high-level overview information in an easily digestible format.
Unifying multiple data sources poses a variety of obstacles. Here are some of the main challenges that business regularly face when they attempt to create an integrated system:
- Businesses usually have a clear objective in mind when pursuing data integration. They’ll likely have a specific challenge they’re trying to resolve however, they tend to overlook the path required to achieve this. Those spearheading data integration must possess a clear understanding of the types of data that will be collected and analysed, the data’s origin, the systems that will interact with the data, what analysis will be performed, and how often data and reports will need updating.
- A data integration could involve data that is stored in a legacy system. This data may not include key information like dates and times which current systems include, which can cause consistency issues when uploading data.
- Modern systems generate different types of data from a variety of sources like videos, IoT devices and cloud based platforms. It includes both unstructured and real-time data, and can be difficult to figure out the most effective way to integrate all this different data into a new system. There are lots of valuable insights to gain from this data, but the volume, speed and format can make things difficult.
- External data that has been collected may not have the same amount of detail as data collected internally, impacting the value that can be taken during an analysis. There may also be restrictions set by the supplier to how you can share externally collected data across larger organisations.
- After the new integration system is set up, it’s vital that the data is regularly updated following best practices to ensure its quality is maintained to maximise its value for analysis.
Data integration techniques
There are different techniques that can be used when integrating data. You can consider the following:
- This involves an individual user manually gathering required data from multiple sources by directly accessing their interfaces. The collected data is then cleaned up where necessary and consolidated into a single warehouse. However, this is a very inefficient approach that lacks consistency and is only suitable for very small businesses that hold minimal data.
- This approach requires a middleware application to serve as an intermediary, aiding in the standardisation of data and its incorporation into the master data repository. Quite often, legacy applications are not compatible with modern systems however a middleware application is able to access data and help move it to the new system.
- This technique uses software applications to locate, retrieve and integrate data. While integrating the data, the software is required to unify data from different systems so that it’s all compatible and transferable to different sources.
- An approach that keeps data in its original source but focuses on building a front end that makes this data appear consistent no matter what source it’s viewed in. Object-oriented database management systems can be used to make unlike databases appear unified.
- The most popular method when integrating data. A copy of data from the original source is stored in the integrated system and processed to create a single customer view. Unlike uniformed access where data is kept in the original source, all data is stored in a new data warehouse.
Data integration tools
Data integration tools can significantly help ease this process. The most useful data integration tools include these features:
- A data integration tool with more pre-built connectors will help speed up the process.
- This feature will provide more flexibility and prevent a vendor lock-in.
- Being able to access your integrated data system from anywhere is essential as businesses have more employees working remotely and rely more on cloud based platforms.
- They should not be overly complicated to use. A simple interface will help users navigate them easily to find the data they need in an efficient manner.
- You should know exactly how much you are paying. Don’t get caught out by additional costs for added connectors or having more data.
- Data integration tools should support cloud based solutions whether it be a single cloud, multi-cloud or hybrid cloud setup.
Sagacity’s data management solutions are tailor-made to your exact needs. Our bespoke data management solutions and analytical capabilities can handle any type of data integration no matter the size of the organisation. We offer the right solution for every business’ needs. Contact us today to see how we can help your business address your current data challenges.
