Technology has made data integration a critical aspect of every industry because it provides a way to analyze data to make decisions. The idea of big data is new, but the data has always been present – there just wasn’t a way to collect it until technology evolved.
Once technology advanced enough to collect and save vast amounts of data, companies began to look for ways to cross-reference and review that data to improve their business processes.
Databases have long been designed to collect data specific to designated areas of a business. For example:
- employee data is typically managed in a database by Human Resources;
- finances are governed by the financial department; and
- customer details are collected in yet another database.
Even small companies have a lot of data to collect and manage. As companies become more reliant on computers and databases, there is an increasing need to integrate all the collected data.
Data integration ensures that departments and projects can see the same data to make decisions.
Data integration processes are what allow companies to be more productive and effective. It ensures that decisions are made based on all available data. Data integration also establishes processes and procedures that can apply across departments.
This guide covers what you need to know to understand data integration. It will also assist you in determining what tools you need and how to approach your data integration process.
What Is Data Integration?
Data integration is a process that allows a company, organization, or group to move together the data stored across its databases. This data is also joined with external databases so that all that data is accessible simultaneously.
Simply put, data integration is consolidating all relevant data into one place.
It’s important to note that data integration can be included from external sources. It does not have to be stored only on internal databases or systems to be integrated.
Accessing external data is essential for organizations that partner with others or rely on contractors. Data integration efforts allow these companies to complete projects and review lessons learned.
In some fields, it’s essential to access industry data and studies to stay current.
Given how varied data integration needs are, there is no single approach or method to integrate data successfully. There are simply too many variables to the process for a one-size-fits-all approach.
Fortunately, there are a few key elements to consider to ensure that the integration goes smoothly and includes all the desired data.
How Does Data Integration Work?
The data integration flow begins when a party submits a request to a master server for a specific set of data. Upon receipt of the request, the master server:
- compiles the information from all applicable sources;
- extracts it;
- consolidates it; and
- produces a cohesive set of data for the requester.
Usually, data is stored in a data warehouse as its centralized location. Data warehouses can hold vast amounts of data to support essential business activities.
Most notably, these data warehouses contain data about analytics and reporting. These data centers provide a single location to retrieve data from a wealth of sources.
The Purpose of Data Integration
At its most basic, integrating data provides a method of retrieving an overview of all available data. It gives people a way of compiling all relevant data to be readily accessible for analysis and interpretation.
Today, it is necessary for companies to have data integration capabilities due to their increase in popularity.
The more robust and mature the process, the greater the company’s advantages over the competition. This is ultimately the goal of data integration initiatives.
One of the most critical aspects of any business is its customers. Getting a 360 view of the customer base means targeting data about customers from various sources, both internal and external. These data sources include:
- sales and marketing efforts;
- customer reviews;
- CRM systems;
- online activity;
- web traffic; and
- internal software.
Customer data integration provides the tools to extract relevant information from these sources. This results in a much better understanding of what your customers think, what they want, and where you can improve.
The Importance of Data Integration
The data integration process offers the following benefits and advantages:
- It streamlines the process of combining data, allowing for a much more efficient and reliable approach to making decisions.
- Users gain better insight into the data through a more comprehensive compilation of the information.
- It offers a way to seamlessly transfer knowledge between different systems, partners, and sources.
- A robust data integration system has a high ROI.
- It provides higher quality data that can be automated based on established business rules.
- Integrating data can reduce or eliminate errors.
- It can improve the customer experience and improve relationships with partners.
Today, every industry can benefit from data integration because of how data drives decisions. Data integration can help determine details about critical decision making, such as:
- planning; and
- stock decisions.
Data integration is about better management of the business ecosystem. It helps to improve business intelligence by combining business analytics, data tools, and best practices.
Effective data integration assists companies in making more data-driven decisions and enhancing technical and business processes. When you have a wealth of data, data integration allows you to synthesize and understand the story that your data tells.
Common Problems Data Integration Can Solve
There are some struggles that all businesses and organizations experience. Data integration offers a way to solve many issues that have long plagued business owners, CEOs, managers, and department heads.
Having a lot of data available is only helpful if you can quickly access data and understand what that data is presenting. Computers and databases made it possible to collect data, but it is separated and segmented over many other types of equipment.
Equally intimidating for most decision-makers is the amount of data collected today. You never need to pull all available data at one time because that would be like trying to drink from a fire hose.
What you need is to be able to target the data that is pulled as well as other potentially valuable data.
The full set of data challenges can be summarized into five primary problems. Data integration provides a solution to all these issues.
Data access is limited across different groups, departments, and teams.
When groups have a limited amount of data, and that data is not consistent between them, the conclusions and decisions will be different.
Even in the best-case scenario – where everyone has access to the same information – different departments typically create their own processes and procedures.
If you ask five people to describe one person, and they are all looking at that person, you will get five different descriptions. If you narrow the focus of what they should describe, you will get a more concise and similar description.
Data integration works by providing that template so that data is filled into specific regions. At the same time, it makes all the same data accessible to everyone.
If groups want to focus on different aspects of the data, they will see the data presented in a way that facilitates analysis and understanding. That way, you know that everyone is getting the right picture, even if they are focusing on a specific part of that picture.
Ultimately, you want everyone to have the same big picture and a unified view. Data integration gives you the ability to create a complete data portrait for everyone.
Data appears in various databases and entirely different formats.
There are many ways to store and present data, and different software and databases take their own approach.
Trying to access and combine data from so many sources with such a large variety of formats can cause significant headaches. Even if all your data sources are internal, gathering data presented in so many different ways can make it difficult to analyze it.
Data integration pulls all that data, centralizes it, and then provides it in a consistent way. You can control what data is pulled and how it is presented.
There is too much data to manage.
The most often discussed problem with data is that there is simply too much of it. Once the technology became able to collect it, people quickly realized just how much data pertained to every single picture they needed to make decisions.
Since you never know what data you need or when you need it, you may think that you should pull everything – just in case.
More often than not, this approach buries the relevant data under a bunch of information you don’t need, at least not at the time of extraction. It’s similar to hoarding data hoping that you will have a use for it later.
Big data integration lets you pull data from many different sources, but you can specify what data you need when you need it. This allows you to manage and analyze just what you need.
You have the wrong tools to integrate your data.
Though it sounds relatively straightforward, data integration is a complex process. Even if you have tools, they may not be right for your data ecosystem, or you may not be using the tools correctly.
There are a lot of specialized tools in data integration, so you may be able to sync current data but fail to ensure historical data is the same. Or you may have tools that focus on pushing data in only one direction.
A better method would be to complete a full data processing between the different platforms.
Most of these tools are specific to a platform or software, so they largely support just those platforms.
Mature data integration tools can work with all the data to ensure it is synced, regardless of its age. These tools can also centralize it to get a better overview of all your data.
Managing large amounts of data from different systems makes it difficult to ensure data quality.
Since software and databases all have their formats, how data is entered is often less structured than it should be.
Data from disparate sources – or sources that are markedly different – can lead to defective or ambiguous data. Once entered, many systems don’t have the proper methods of maintaining the data, leading to many low-quality data.
This is as much the fault of the people managing the data as the system they use.
Data integration facilitates good practices for data entry and maintenance (which you should do). Besides this, it helps you review your data quality to determine when it is outdated, duplicated, or inaccurate.
As you learn how to work with and manage your data, you’ll be able to create better processes across the different teams, departments, and platforms.
The best benefit is that you will have integrated data that you can trust when you need to make decisions or understand different aspects of your business.
How to Avoid Data Integration Issues
You need to have a robust and thoroughly reviewed integration strategy to avoid data integration issues. This isn’t something you can do without a lot of planning and dedicated time.
Every business is different, so you will need to customize the data integration strategy to meet the unique data needs of your business. But there are a few key aspects to every successful integration strategy and a few considerations before you start.
Start your data clean up before you do anything else.
The cleaner your data before you start integrating it, the faster the process will be. The following are the steps you can take to get your data in better shape:
- Review and remove duplicates.
- Review data to determine what is invalid, outdated, or incomplete.
- Determine what channels you should be using to integrate your data.
These seem straightforward, but all three steps can be incredibly time-consuming. However, when you finish, you will have a much better understanding of your data needs and your data integration goals.
Create a data backup plan and put it into place before starting the process.
If you don’t already have a plan to backup existing data, you should set that up before you do anything else. This process needs to be repetitive and predictable so you can return to an earlier version without losing much data should there be a problem.
For example, say your company suffers a ransomware attack. If your data has been recently backed up, you may not need to worry about the malicious hackers harming your data, and you can quickly access your data without paying the hackers.
Create clear procedures and processes for how the data should be managed.
After you’ve cleaned up your data and made sure to back it up, you should create processes and procedures that ensure a standard for entering and maintaining data across all the platforms and software.
For external data that you can’t manage, you can set up other processes and procedures for managing that data.
If possible, you can establish standards with other companies. These standards are particularly relevant to business partners who have a vested interest in having clean data.
Review your options, so you find the right software and tools to support your integration process.
The correct data integration tool and software can automate the entire process once you have tested small parts of the process.
Keep in mind your unique needs, so you don’t buy something that either lacks the functions you need or something that has a lot of features that you don’t need.
Create a data management plan going forward.
Before you execute your implementation strategy, you should devise a plan to manage your data. It is much easier to maintain data after you’ve recently cleaned it than to do major data cleanup regularly.
After cleaning it up, you will have much more reliable data if you follow a data management plan and take steps to maintain the data.
How to Perform Data Integration Testing
Like data migration testing, you will need to perform data integration testing at the beginning of the process and as you work through it.
At its core, data integration is completed in three primary steps:
This process is called ETL. ETL testing completes several important steps within those primary steps:
- Validates the data
- Verifies the data
- Qualifies the data
- Eliminates duplicates
ETL testing needs to be conducted in eight stages to ensure that all these steps are completed.
- Identify and address the business requirements for the data, including the flow and reporting needs. You can create a data model to develop the scope for your integration and its testing.
- Conduct validation of your data sources. Compare the data columns against your data model to validate the data, remove duplicates, and identify inaccurate data.
- Design specific test cases, including mapping, SQL scripts, and rules for the integration. This process will also require validation.
- Begin the extraction based on the established business requirements. Review the data for problems and defects before moving into the larger integration.
- Verify that the data format matches the schema in the target location.
- Load the data into the target area once all the other steps have been completed.
- Create a report summarizing findings and elements out of scope for the current need.
- Complete the testing and file it (in case it is needed for later review).
What Are Data Integration Tools?
As mentioned earlier in the guide, having the right data integration tools is essential for managing data. The question is: what are the right tools?
Unfortunately, there is no one set of tools that will definitely work.
Instead of focusing on specific tools, look at tools’ functions to find the right ones for your particular needs:
- Connectors provide a bridge between the different sources and the centralized area. The more connectors you build into your process, the more efficient your data integration is.
- Look for how portable the tool is, and keep in mind that you will likely need tools that can work with hybrid data storage models.
- Create an open-source architecture with the greatest flexibility to change vendors if needed.
- Take the time to look at the interface of the tools and ensure that it is easy to use. If you can’t understand what different functions of the tool perform, it probably isn’t the right tool for you.
- Make sure you know the price of the tool. If the tool doesn’t have a transparent price model, don’t consider it. You don’t want to encounter hidden costs as you make your way through the process.
It is best to talk with data integration experts to ensure you have the right tools to make data integration easier when the time comes. You don’t want to find out you are missing something after you finish your planning.
Experts can help you create a solid plan that includes the right tools and data integration solutions.
Examples of Data Integration
The examples of data integration in business processes are endless. Here are a few examples that will probably be familiar to most readers.
- Retailers who implement data integration can track how they perform – both in traditional stores and online. This tracking includes managing inventory, employee schedules, and interpreting trends.
- Healthcare is one of the industries that can most benefit from data integration. This is due to the many variables inherent to the industry. The ability to quickly access data is essential to managing a practice, hospital or clinic. These organizations require data:
- to manage many ailments, insurance, treatments, and medications; and
- to properly treat and bill their patients.
- Marketing is another industry that significantly benefits from data integration – especially with the rise of social media. Marketers have virtually no control over much of the data they need to plan future campaigns. Data integration helps to pull data from many online sources and then synthesize it with internal data.
- Financial industries need data integration to provide greater security. It provides a way to better monitor and detect patterns that indicate fraud or identity theft, so it can be stopped as quickly as possible.
Every industry relies on integrated data to improve and move forward.
Take a minute to consider all the data you encounter every day and what data you can’t access.
It isn’t necessary to get clearance for a particular database if you can pull specific data into a data warehouse or other central repository. This would also allow you to leave classified or private information locked behind stricter access.
Your data integration solution doesn’t need to reinvent the wheel, but it should be specific to your needs.
For example, some companies require real time data integration to instantly transfer information and synchronize their data sources. For other companies, a periodic batch processing solution is sufficient.
The best way to manage big data systems is to take a focused approach so that you don’t take on more data than you need and don’t omit relevant data.
Whether you deal with legacy systems, business data, or heterogeneous data sources, data integration provides a way to easily manage those multiple source systems.
Speak to our specialists if you have reached this point and are ready to migrate your data. They can walk you through what you need and help you decide what your next move should be. We can provide you with the right data migration service to be successful.