Data Integration: Your Go-to Guide to Know More!

Data Integration: Your Go-to Guide to Know More!

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  • Post category:Data Integration
  • Post last modified:April 26, 2022
  • Reading time:8 mins read

Data sharing and storing by companies became part of the routine, it’s something that they do every day, despite the issues that can be found along the way. Most businesses deal with several data management systems, which automatically translates into a variety of data formats that need to co-exist, and to do that, they use data integration to combine the data types and formats into one single location.
Data integration technologies make the job of making all those different data formats co-exist in a single place, commonly called a data warehouse, in which the company will be able to use the data to gather insights, support the decision-making and help to fix any possible issues.

Your company can also benefit from robust data integration processes as they can be applied in almost all industries to help you unify all your data and have access to the information in an organized dataset that will be easier to search, manage and analyze.
Data must be accessible in order to be valuable, there’s no worth in data if it’s not being used to generate insights and powerful communication between departments to solve your company’s issues or find information that will help the company grow. Data increases communication, operations, better decision-making, marketing, sales and overall productivity. 
To build a digital business, your operations and decisions need to be built around data and algorithms, in order to extract the maximum value from them. So having a process that will make the data gathering and analysis easier will only make the internal departments and all the key decisions flow seamlessly.

What is Data Integration?

Data integration is the process of gathering and moving data from several sources and creating a unified view of them for the users. The premise is to make the data easier to be processed by systems as they are seen in the same format, and at the same time, it gets easier for users to research and analyze them. 
This single unified view is usually a data warehouse with a simple dashboard that will combine this data coming from multiple sources – and sometimes those datasets are massive – so everything can be seen and managed in real-time. 
There is no one size fits all approach to data integration, but all the solutions involve the sources or a network of sources, a master server, and users accessing data at those masters’ servers.
Inside data integration, there are various internal processes, but ETL is the most important one. ETL (Extract, Transform and Load), dates back to 1970 when companies started to use multiple databases and data sources, but only in the 1990s, did the process become this traditional part of data integration.
ETL is a simple but crucial process in which the data is extracted from a source, transformed into the format the company needs, and loaded into the data warehouse or needed system.

The Evolution of Data Integration

The first data silos issues that could be solved by a combination of data sources are dated from the end of 1970 and the beginning of 1980. Computer scientists started to design a data integration system compatible with several formats that could extract, transform and load data from heterogeneous sources into a unified view.
The scope of data integration changed and evolved since then, especially as it became such an important process. Nowadays, the capacity of producing, processing and leveraging data is bigger than ever, and data integration followed this trend. 
Being able to leverage how much information the company can analyze, is as essential as the service they provide. SaaS, applications, all the data they contain and all the possible combinations the company can create with them will give users the power to find unique business insights, and create new rules that can make those applications respond ever faster to new data. 

How Does Data Integration Work?

Companies that deal with data face the challenge of trying to make sense of the information they have access to in order to deeply understand their own situation and the market or environment they are operating. 
Your company probably captures huge amounts of data every day, in different formats and from several sources, but to capture the value from this data, users and employees need to have access to relevant and organized information, so they can support the company with reports, insights and business processes. 
But as data gets distributed across applications, data warehouses, databases, in the cloud, IoT services, third-party providers, or other sources, it’s clear that one master database is no longer enough to do the job and the data will not be structured as you need.
The traditional way of making data integration work is through the physical integration approach, in which there’s a physical movement of data from its source to an area where it will be cleaned, mapped, transformed into the needed format, and then it will be transferred to a data warehouse. The ETL (extract, transform and load) technique is what makes this process work.
The other approach is the virtualization one, in which a virtualization layer will be used to connect to physical data stores. This method created a virtualized view of the physical movements, but there’s no physical environment and the data doesn’t move. 

Essential Steps In the Data Integration Process

As your business is restless, looking for data solutions that will work and solve your main problems, it’s important to determine what you need and how you’re going to apply it. It’s no different when it comes to data integration, it’s not just extracting and moving data, there are several steps inside the whole process that must be completed in order to make the process successful.
It all starts with data requirements, and at the end of them, you’ll know well which data to source and store.

  • Gather the business requirements
  • Define data quality and rules
  • Understand the data sources to see if both the sources and the destination systems will work with the defined rules
  • Perform robust data quality assessments 
  • Define if there will be a tolerated gap between the data that is available and its quality versus what you had requested
  • Revise expectations, costs and the best data solution
  • Model the data storage if needed

After working on the requirements, it’s time to move to the next category of making data integration work:

  1. Data preparation: it’s the part in which the data is gathered, consolidated, transformed, cleaned and stored. Data quality rules must be applied at the beginning of this flow, so if any inconsistencies are found between one system transaction to the other, it can be solved at the place and don’t affect the next system.
  2. Data franchising: in this process, the data gets reconstructed into valuable information that your company can use for insights and analysis. In this stage, the data will already be in the warehouse to be filtered, aggregated, summarized, and accessed by your employees and data users by business intelligence tools. 


How Does Data Integration Add Value to Your Company?

Data integration will give your company advantages over your competition by increasing the operational efficiency, as the need to manually work on the data to ensure that they are all in the same format will be reduced. 

It’s also best to ensure data quality with an automated data transformation that will change the data according to your rules, while making all the insights easier and better to be done with a single unified view. 

Data integration also creates data virtualization, in which all the users and employees from your company can access and manipulate data without needing access to the actual data storage location. The back-end structure needs to be in place in order for data virtualization to work. 

Under the umbrella of data integration is also the possibility of using business intelligence and related technologies that will help with data analysis and improve all business decisions that need to be made based on data.