Overview of Data integration
Businesses need to analyze data at every step of the business process if they want to succeed. The procedure followed to combine data from multiple sources to create a unified view is also known as data integration.
The benefit of data integration is that it helps analytical tools provide actionable and effective data that can become part of business intelligence.
Such unified data assists businesses extract actionable information for correct evaluation of their business without getting overwhelmed by volume of data.
In the following sections we will look at:
- Necessity for Data Integration
- Data Integration and the Associated Challenges
- Common Data Integration Technologies
- What are the different data integration tools?
- Difference Between Application Integration and Data Integration.
Why data integration becomes necessary?
For any business, required data is available from different data sources. For example, data would be available in CRM system, marketing software, customer centric applications,
and partner integrations. Data integration helps to merge this data and present it in a manner that makes data analysis and reporting easier.
Benefit #1: Meet Operational/Analytical Needs
For, successful operational or analytical needs, businesses must integrate data from all these varied sources.
For example, to perform necessary analysis you need to access different accounts on multiple websites, copy data, format it as required, and then perform required analysis.
The benefit of data integration is that it allows all these functions to perform efficiently.
Benefit #2: Improve Efficiency and Save Time
When data integration performed in the right manner, it reduces time required to prepare and analyze data. There is no need to collect data manually, resulting in considerable time saving.
We can better use time saved for analysis of business operations and improving productivity.
Overtime, data integration initiatives help in enhancing value of data. We analyze data to check for quality issues so that required improvements can be done.
Benefit #3: Reduce Reworks and Errors
If data is to be collected manually, employees will have to know about all the accounts and locations are to be checked. In addition, it is to be ensured that required software is installed before we start data collection work. Moreover, employees need to make sure that data sets they extract are accurate and complete.
But, if a particular data repository is integrated and employees are not aware of it, then it will cause creation of incomplete and inaccurate data set.
Last, if we didn’t implement the data integration, then reports will not get automatically synchronized and need to be done manually regularly whenever any changes occur in the account, resulting in considerable loss of time.
Now that we have seen the major benefits of data integration, let us look at some challenges that organizations face during data integration.
Data Integration and the Associated Challenges
Data integration issues and challenges act as a hindrance towards getting unified view of data.
Challenge #1: Issues with Centralization of Data
The aim of data integration is to get all the data at one place, but sometimes achieving this task becomes a struggle. This problem arises when we try to manually curate data from various sources.
A better option is to utilize a data integration platform that will curate data from all required sources with relative ease.
Challenge #2: Data in Incorrect Format
If data is not in the desired format, then it will lose its value. Validating and formatting data manually can become a herculean task. Instead,
you need to utilize tools that can perform data transformation by examining the base language and making necessary changes according to the desired format.
Challenge #3: Duplication of Data
Presence of duplicate data is a major problem business have to face during data integration. To prevent such a thing from happening, it is important to standardize data and monitor data lineage.
Common Data Integration Technologies
In the earlier stages of development, ETL or Extract, Transform, Load was the solitary technology available for data integration. However, many technologies are available if you are looking for data integration. Let us delve into the details in this section.
ETL (Extract, Transform, Load)
It is the most common technology for data integration, where data is extracted from a specific source, transformed, and then uploaded into a targeted destination.
Data extraction occurs from an ERP solution, database, file system, or a cloud application and then transferred into another data repository or database after the necessary transformation.
Transformation that are performed depends upon specific use case. But some common transformations that are performed consist of data aggregation, data cleansing, and data reconciliation.
EII (Enterprise Information Integration)
This technology provides on-demand curated datasets. It also involves development of business view or virtual layer of the data sources.
The benefit of this layer is that it shields business users and consuming applications from varied complexities that arise when a connection is to be establish between disparate systems that have different semantics, interfaces, and formats.
Moreover, EII can handle real time integration so that businesses can use fresh data for reporting and analysis.
EDR (Enterprise Data Replication)
It is utilized as a data propagation method when data is moved between different storage systems. Here, datasets are move between databases with same schema.
But nowadays complexity of the process has increased and involves target databases and heterogeneous sources. In addition, data gets replicated regularly, sporadically or in real time, based upon business requirements.
In both, EDR and ETL, data is move back in bulk, but in EDR, data manipulation or transformation does not take place.
What are the different tools which can be used for data integration?
It is, when performed correctly, helps businesses manipulate and query data using a single interface, generate statistics, and perform analytics. However, to achieve such data integration it is necessary to use a tool that will perform these tasks.
Such tools can transform and map data as required and, in addition, monitor flow that includes tasks such as error handling. Nowadays, there are different types of tools that are available. Let us go through the details here.
Read Other article: Importance of Data Cleansing for Businesses
Tools Based on Local Data Sources
These tools are setup within a local network and integrate data from local data sources. Some examples of such tools include Oracle data service integrator, IBM InfoSphere, Microsoft SQL, etc.
Open Source Tools
If a business has in-house expertise, then open source options can be utlize. Business can avoid costly enterprise solutions by using these open source tools. It is also a good option when a business wants to maintain complete data control.
However, the business will have to manage privacy compliance and data security issues on its own. Some examples of open source tools are Skool, Pentaho, CloverETL, Pimcore, Myddleware, etc.
Cloud Based Tools
Many of these tools are iPaaS or Integration Platforms as a Service, which integrate data into a data warehouse (cloud based). These cloud based tools can manage streaming data from the web and also from common databases.
The good thing about these tools is that they can quickly integrate data sources using Webhooks, SDKs, and APIs. Some examples include Salesforce Connect, Jitterbit, SnapLogic, etc.
How to choose the most appropriate data integration tool?
There are different factors that you need to consider while selecting an integration tool.
Size of Your Business:
As your business expands and the need for data grows, you need to select an integration strategy accordingly. You need a tool that will grows and meet your growing data needs.
Meet Varying Needs:
The aim should be to find a solution that will connect with web-based and streaming data sources. You also need to be aware of the fact that many of the legacy tools cannot manage streaming data or even they do, we do poorly it.
Compliance and Security:
You need to check whether tool you want to select will meet your company’s compliance and security needs.
Availability of Connectors:
Because of presence of different applications and systems, it is necessary that the tool you select has required connectors for seamless and time saving.
Are application integration and data integration the same?
If there is a business process that involves several applications, it becomes necessary to move live data between applications.
For example, if a new employee joins an organization, his or her data move from HR system into the payroll application.
Application integration interface is to orchestrate movement of data between applications.
Similarly, when a customer places an order, the flow triggers in application integration, resulting in real-time availability of data in related applications.
Here, data group together from various sources to create a unified view of data. This data is then use by the business for reporting, business intelligence, and analysis.
Contrary to application integration, when data integration is perform, it does not need information related to specific business processes.
A Final Note
As we can see, there are several benefits of data integration. If you are looking for data integration services then call us, our experts will try to understand your requirement and suggest a solution that will be right for your business.