What is Data Integration?

Defining Data Integration

You’ve probably heard people talk about data integration and its importance before, but you might not be completely sure what the term means. Don’t worry! It isn’t as complicated as it might seem. This term refers to the way data from multiple systems is combined into a single view.

The process of data integration begins with ingestion. It also includes cleansing the data, ETL mapping, and transforming it. Once the process is complete, analytics tools can be used to produce business intelligence. The data is all in one place and easy to make sense of, so important business insights can be gleaned from it.

Still confused? Let’s use an example. If customer data is being integrated, then all the information about each customer that comes from different departments such as sales, marketing, and customer service is brought together. Then, you have a single view of the customer.

Pulling data from numerous data sources together allows people to access all the information they need without having to search across different databases.

This can be used for many purposes. If you’ve ever called a customer service helpline and they already knew everything about your interactions with the business, then they probably use a data integration system! This is how data integration can seriously improve efficiency levels.

How Data Integration Helps Businesses Succeed

Data integration helps businesses in a number of ways. Read on to find out how collecting data from multiple sources in one location could streamline your business operations.

Collaborative Unification of Systems

Remote working recently came into the spotlight due to the pandemic. It was a great reminder that employees are often in disparate locations requiring access to company data. If they’re working in a cross-departmental project, they’ll also need data from different systems.

This poses a challenge for the IT department! How can employees access the data they need in a secure way? How can they access it themselves with minimal support and interference? Data integration is the answer!

Data is an active, dynamic entity in a business. Employees are constantly generating new data, and their colleagues need instant access to that data to work effectively. That’s why collaborative, unified data integration is crucial to a business’ success in 2021 – whether employees are in office or not!

Increased Data & Time Efficiency

Preparing and analysing data can be so time consuming. Companies often integrate their data to make this quicker and easier. When the unified view of the data is automated, there’s no need to manually gather it anymore. Rather than have employees build connections from scratch every time they run a report or build an application, they can simply access the unified view.

There are many tools that can be used alongside the data integration process. It doesn’t necessarily need to be hand-coded, which saves even more time! Your development team will thank you.

Now that you know how much more efficient your processes can be with data integration, only one question remains: what will your employees do with the time you’ve freed up? The potential is limitless!

Maybe they’ll have more time to analyse the data and make plans for improvement based on the insights they extract. Any subsequent success could be considered another positive side effect of data integration!

Reduced Errors

It’s easy to make errors when data is manually gathered. There’s so much to keep abreast of when it comes to data resources! Your employees in charge of this must know all the locations and accounts they may need to access to complete their data sets. They will also have to install a range of software to perform the task. What if a data repository is added during the process and your employee is unaware? Unfortunately, despite all their efforts, they’ll still have an incomplete data set.

Data integration solutions can automatically synchronize data. That means the old periodic reports that accounted for changes are no longer necessary. Instead, you can run a report and be confident that automated updates have ensured its accuracy. The report is run in real time, so errors are far less likely.

Increased Data Value

Data is one of your most valuable business assets, whether you recognise that or not! And the great news is that data integration will increase the value of this data. Over time, as your data is integrated, issues surrounding quality will be identified. Improvements can then be implemented that improve the accuracy of your data. Your analysis is only as good as your data, remember! This is how data integration is crucial to the production of quality, useful, insightful analysis.

Importance of Data Integration In Modern Business

Leveraging Big Data

Big data is another term that seems to be on everybody’s lips at the moment! Obvious examples of big data include the continuous, massive volume, worldwide information consumption that companies like Facebook and Google deal with. Can you imagine the amount of data that they have to process from their billions of users all around the world?

More and more big data enterprises are emerging, which means more data will become available. Businesses can leverage that, but first they need high quality data integration processes. In the future, we can assume that data integration will become an even more important element of business operations.

Creation Of Data Warehouses & Lakes

When data is integrated, data warehouses are often the end result. A data warehouse takes multiple data sources and combines them into one relational database. From a data warehouse, users are empowered to compile reports, run queries, produce analysis, and extract data in a consistent format.

Need a practical example? You’ve probably heard of companies using Microsoft Azure or AWS Redshift to generate intelligence. These are data warehouses! And they’re used to turn data into valuable business insights.

Data lakes are a little different. They consist of raw data, and this data doesn’t have a predetermined purpose. Warehouses are distinct because they contain structured data that’s already undergone processing. Both are used to store big data, though.

Simplified Business Intelligence (BI) For Analysts

Data integration makes the business intelligence analysis processes much easier. It achieves that by showing a unified view of all of the data it has gathered from disparate sources. Rather than search through multiple locations, organisations are able to view and understand available data sets much more easily. They can quickly obtain an overview of the current state of the business. This allows more information to be evaluated more efficiently. Your data analyst doesn’t have to be overwhelmed by the prospect of dealing with high volumes of data anymore!

Business intelligence is different from business analytics. How? Because it doesn’t use predictive analysis. Business analytics makes future projections, but business intelligence accurately describes the current situation to support effective decision-making and strategy-forming.

A data warehouse is helpful in this scenario. It allows an overview to be presented in an easily understood format.

What Are The Types Of Data Integration?

Now you know how helpful data integration can be, you’re probably wondering how it can be achieved. There are a few different methods available. The right one for you will depend on your available resources, the size of your business, and the need that this data will fulfill.

One way to integrate data is to do it manually. As you can imagine, this is a bit of a nightmare! One individual user is given responsibility for going between interfaces, cleaning up all of the data, and combining it in a warehouse. It’s far from the fastest way to achieve this task, especially if your organisation is large. The potential for human error is considerable too.

Another integration approach involves using middleware application. This application “normalises” the data, acting as a mediator between its original source and the master data pool. It’s almost like the adapter you might plug in to use obsolete electronic equipment that has old-fashioned connection points. Data stored in similarly old-fashioned applications can be accessed using middleware.

Software applications can be used during application-based integration to locate the data, retrieve it, and integrate it into the master data pool. The role of the software is to transform the data so it’s compatible with data from different systems. After all, this data needs to move between these multiple sources.

One type of data integration you might consider is uniform access integration. It creates a front end so that data looks consistent when accessed at different points. The data itself doesn’t change, though. It remains within the original source. The data isn’t uniform, but it takes on the appearance of uniformity thanks to object-oriented database management systems.

Finally, the most commonly used approach to data integration is common storage integration. This is when a copy of data is kept in the integrated system. It’s processed to create a unified view. Data warehousing is an ideal storage solution if you’re using this approach.

What Are The Challenges In Data Integration?

Data integration is a challenge unto itself! After all, it involves taking data from diverse sources and combining them to create a unified whole. Companies might look to pre-built processes to enjoy time and costs savings, but later they may find implementation to be more complicated than they expected!

Some of the challenges faced by organisations building their integration systems include:

Companies usually know what they want at the finish line, but they’re less clear on what will be required to get there. Data integration implementation required understanding of the types of data that will be collected and analysed. It’s important to know where the data comes from and which systems will use the data. They should also know the kinds of analysis that will be applied and how frequently reports will be updated.

Working with legacy systems. If data is stored in outdated systems, it could be missing markers that modern systems include as standard. This can make it complicated to integrate.

Working with new systems. As data evolves and new types of data are generated, you need to adapt your infrastructure. Of course, this is difficult when you’re experiencing continuous change when it comes to volume, speed, and emerging formats.

Working with external data. The data you take from external sources may not match the detail as the data from your internal sources. How can you examine it all with the same rigor?

Maintenance. You might think that once an integration system is in action, the job is done. Actually, you need to ensure you’re using best practice in accordance with regulation and your organisation’s demands. It’s ongoing work!

Achieve the Full Potential Of Your Integrated Data With Gravity

Do you want to make the most of your integrated data? You need to speak to Gravity Data. We’re a team of data engineers that can optimize your data integration processes and help you find the most suitable data storage solutions for your company’s specific needs.