Roughly a few years ago, data integration was a relatively new part of operations for most organizations. However, today it plays a crucial role in connecting several systems and technologies within a modern work environment. Without data integration, businesses can not get accurate information and imagine the number of enterprises that would have to close down on this basis alone.
There are over 6 billion people around the world using smartphones, and with access to the internet today, data permeates every part of our activities, from health to transport to education and even governance.
We have also witnessed an astronomical increase in the SaaS industry from 2014 till now, reaching over 157 billion US dollars in size; all of this data indicates that the need to manage collected data increased as more large-scale innovations took place. Many businesses are now gathering large amounts of data from various sources such as blogs, social media, financial transactions, and videos at a high volume and speed that needs to be captured and analyzed quickly.
While it might seem simple, Data integration in actual practice is complicated as there are different techniques and varying case studies for when one needs to apply these techniques, each varying from organization to organization.
Furthermore, data integration has evolved.
This article explores how data integration has changed and enables businesses to collect and analyze large amounts of data from various sources to make more informed business decisions.
What is data integration?
Data integration is the process of gathering and combining data from multiple sources into a single, unified view.
By integrating data from various sources and formats within an organization, businesses can create a single, cohesive source of information. Data integration allows you to access all necessary company information in one place instead of spreading it across different platforms.
Once you achieve this, your businesses can make more informed decisions, improve collaboration among departments, increase revenue, and enhance customer experience.
While there are so many different approaches and techniques for integrating data, the process generally involves the following constants :
- One or more data sources
- A central server that connects to the database
- Users/ clients to access their data through their connection to the main server.
Benefits of data integration
As the global business environment grows, the number of data sources increases and the types of information available for use become more diverse.
One of the primary reasons businesses today are turning to data integration is because Integrated data can help organizations with numerous and dispersed data sources have a cohesive view of their operations. For example, new thriving industries like Cryptocurrency, Artificial Learning [AI], machine learning, and even the World Wide Web [Internet] would be impossible without data integration.
Businesses can also reduce the time and effort needed to access multiple scattered data sources and provide a single source of accurate data for real-time reporting, analytics and decision-making. For example, a company based in the United States could now use real-time data insights and combine views to understand its operations in Amsterdam or London.
But the benefits of Data integration don't end there, and here are a few more of these in detail.
Leads to increased success for businesses
Data without organization is useless. Data integration helps you make sense of all the information you have to generate revenue, expand into new markets, make better decisions, and provide stakeholders with an in-depth understanding of the company’s current operations and the opportunities and risks it faces in the market.
Increases employee efficiency & team productivity
Data integration will ensure your employees can access all the information they need in one place and in a convenient way for everyone who needs it.
For example, a SaaS company is trying to launch a new product. The marketing department might need data from the sales and product department to develop an effective personalized campaign depending on the target audience; they might even need to conduct customer interviews, have discussions, and study pre-existing analytics on user behavior to ensure proper execution. A good data integration strategy would ensure that this happens seamlessly and that both parties quickly get the information they require.
Employees wouldn’t need to resort to manual ways of sorting and organizing data from different systems, wasting time and resources. This increased efficiency would allow you to save costs and serve your customers better while also increasing employee satisfaction.
Better customer experience
An easy way to quickly become a customer favorite is by providing them with the information they need as soon as possible. By integrating your data, you can understand your customers completely, which will help you deliver personalized and relevant experiences to them.
Reduces the risk of errors
Imagine making an online purchase and receiving something entirely different because someone made an error processing the information you gave. These scenarios are usually very frustrating, even with a small-scale business; now imagine that for a larger company with tons of orders and customers daily.
Depending on the error's gravity, data errors can cost millions of dollars to correct. Even for a small business or startup, there is so much data to collect and a high risk of mistakes happening without data integration.
Data integration helps you avoid errors that lead to lousy output for your business, missed opportunities, a lack of coordination, and unhappy customers.
It aids collaboration
It is crucial to easily access and share enterprise data across platforms and boundaries in today's business environment. You might need to collaborate with team members and external organizations like suppliers, business partners, or government agencies.
By effectively integrating data, organizations can unlock the total value of their information and use it to drive better partnerships and agreements that lead to business success.
Data Integration techniques & how they work
Data integration gained prominence in the 1980s; businesses had been collecting data before then but now sought a better way to manage the large amounts of data they were receiving.
The ETL software was developed to address this need, allowing businesses to structure the data they were receiving and make analyses. This data was used for various business functions, such as financial reporting, monitoring sales dashboards, and understanding business analytics.
At that time, there were no smartphones, e-commerce, and Software as a Service (SaaS) did not exist. Over time, various challenges emerged, such as problems with the quality of data, how data was organized, data isolation, and so much more. Then came the big internet boom season of the early 2010s; companies like Amazon, Microsoft, Salesforce, and Facebook emerged with much more extensive data to handle.
Now there's no single way to execute data integration. However, from that time until now, various methods, both manual and automated, have been created and used for data integration, with many relying on the theory of the pioneer model, the ETL [Extract, Transform, and Load] model.
- ETL (Extract transform load): This process works by extracting data from the host environment, transforming it into the required format by cleansing it and removing inefficiencies, and then loading it into a system or application for use. ETL tools are designed to automate this process, making it efficient and scalable.
Other ways data is integrated:
- Data Warehouses: These are centralized data stores designed to query and analyze business information quickly.
- Data Virtualization: allows businesses to transform and integrate their separate data sources without moving the data by creating a virtual layer on top of multiple data sources. This enables users to access data from different sources as though they were stored in one place. This technique is helpful for companies that need data available for analytics and processing in real-time.
- Data Lakehouses: A data lake is a large, centralized repository of raw data stored in its original format. Data integration using this technique involves extracting data from various sources and loading it into a data lake, where it can be transformed and organized for analysis.
- API integration: API (application programming interface) integration involves using APIs to access and exchange data between different systems. This technique can help integrate data from web-based sources or integrate systems that do not have native integration capabilities.
With so many data integration techniques, the best approach will depend on your organization's specific needs and goals. Many organizations require more than one. It's essential to select a technique that is well-suited to your business requirements and that aligns with your overall data strategy
Steps to successfully integrate data
We've talked about Data integration and its benefits; now it's time to look at how to set up your business.
Here are seven steps to get you started to ensure a successful data integration process:
1. Define your goals
Before you begin the data integration process, it's essential to have a clear understanding of what you hope to achieve. Do you want to improve efficiency, reduce costs, or gain a competitive advantage? Defining your goals will help you determine the best approach to data integration and ensure that you get the most value out of the process.
Since you are looking to introduce a new set of operations for data integration, it is a good idea to create SOPs.
We wrote a detailed guide here to help you with the best way to make one as you prepare your data integration strategy.
You can also create an SOP with Scribe. It is simple; just click record on your device to capture any process.
2. Identify your data sources
The next step is to identify all the data sources you will be integrating. This may include databases, spreadsheets, API feeds, or manual inputs. It's essential to have a complete and accurate list of all data sources to ensure that you can integrate all relevant information.
3. Clean & prepare your data
Not all data is good data, and you want to avoid bringing in duplicated or inaccurate data. So before integrating your data, check that it is clean and organized. This may involve de-duplicating records, standardizing, formatting, and correcting errors. It's also a good idea to perform some fundamental data analysis to ensure that your data is consistent and reliable.
4. Choose a data integration tool
Many different tools and technologies are available for data integration, including ETL (extract, transform, load) tools, data warehouses, and data lakes. It's essential to choose a tool that is well-suited to your specific needs and goals. Some factors to consider when selecting a data integration tool include the type and volume of data you are working with, the complexity of your integration process, and your budget.
5. Transform & load your data
Once you have cleaned and prepared your data and chosen a data integration tool, it's time to perform it. This may involve extracting data from various sources, transforming it into a consistent format, and loading it into your target system or database. Depending on the complexity of your integration process, this may be a time-consuming step that requires careful planning and execution.
6. Monitor & maintain your data
Data integration is not a one-time process - it's essential to regularly monitor and maintain your integrated data to ensure that it is accurate and up-to-date. This may involve setting up automated processes to refresh your databases regularly and manually reviewing and correcting any errors or inconsistencies that may arise.
Following these steps can ensure a successful data integration process that significantly benefits your organization.
Challenges of data integration & best practices for managing them
As we've discussed so far, data integration might look simple but can be a complex and challenging process in practice, and there are several potential challenges that organizations may face. Some of the key challenges include:
- Complexity: Data integration often involves combining data from various sources, which can be complex and time-consuming. It is necessary to clean and prepare the data, map data elements to a standard format, and deal with issues such as missing or incorrect data beforehand. There are tools to help you with data cleansing to reduce the risk of human errors and inconsistencies.
- Data Quality: Ensuring the quality and accuracy of the integrated data can be challenging. This may involve identifying and correcting errors and inconsistencies and ensuring that the data is up-to-date and relevant. Data could come in multiple formats from different departments, and it could be challenging to transform them into your preferred form. Implementing data quality checks can be a critical step to address this challenge effectively.
- Integration with legacy systems: Integrating data from older or legacy systems can be particularly challenging, as these systems may not be designed for easy integration and may require custom solutions.
- Data security and privacy: Data integration often involves combining data from various sources, which can raise concerns about data security and privacy. If there is even a slight slip somewhere, a business can be at risk of cyber attacks, data leaks, data breaches, and data corruption. All of these lead to outcomes no company wants to deal with. It's vital to implement appropriate security measures and adhere to relevant regulations and compliance requirements to avoid being a victim.
- Resistance to change: Change can be difficult for some organizations, and introducing new data integration processes may be met with resistance from employees. It's essential to communicate the benefits of data integration and work to address any concerns or fears employees may have. Here is a guide to help you better manage any adoption challenges you might encounter with introducing new tools and procedures during the data integration phase.
By understanding and addressing these challenges beforehand, your organizations can be better prepared to overcome the difficulties of data integration and harness its full potential better
The role of documentation in data integration
Clear and regularly updated documentation is essential before, during and after the data integration process. You want to make sure your team is aware of many parts of the data integration process.
For the average user, conversations on data integration might seem incomprehensible and too bogus. And because of how vital data privacy issues are becoming today, more consumers want to know how their data is being managed and stored and what to trust when it comes to conversations around their data.
This makes it critical for businesses to invest in documentation to break the barrier. Not just for the paid user but the average CEO or team member who probably only has a faint idea of the actual technical part of the organization, like data integration but needs to be kept in the loop.
Scribe is excellent for handling situations like this effectively. Scribe is an easy-to-use tool that captures processes and turns them into instant tutorials or how-to guides.
Here’s a Scribe that shows you how to view your leads in Salesforce.
Like this example, more complex processes can be broken down for employees and clients to understand better.
The advantage this gives you is that you can document the entire data integration process for easy use by employees and customers.
- Employees can get training and orientation on using a new tool/software/technique through a scribe. You can loop two or more scribes covering the same topic together with Scribe pages and provide in-depth information to employees.
- This documentation can also be shared with all necessary stakeholders and include information about the integration process from an IT and business perspective.
Here’s what a verified Scribe User had to say on using Scribe to capture a training procedure.
Scribe is free to set up and you can start using it to capture any process once you install the extension on your device.
Finally, you want to make sure that you are not just investing in training processes with no results, so make sure to follow up with employee progress and the effectiveness of the training method you use to increase the success rate of the integration process.