Data Integration – Everything you need to know
In the highly competitive business world where even a tiny piece of data can become a game-changer for any business, data integration plays an important role. Hence, marketers are collecting data from various platforms, including Facebook, Twitter, Google Ads & So many others, and they are transforming that data in marketing leverage.
Integrating the different data generated from multiple platforms and bringing them on a data integration platform to convert into useful information can help different businesses to work efficiently on their projects. With data consolidation, an organization can improve data accessibility, enhance coordination between teams, and can get reports of work in real-time.
What is Data Integration?
Data Integration involves a set of operations that are used to combine data from diverse sources and then store it in an enterprise’s data warehouse where the marketing managers perform data analyses to convert that data into valuable information.
As we all know that technological advancements and responsibilities go closely associated. Breaking down the data silos can help different businesses to welcome more and more growth opportunities. Therefore, it has become the need of the hour for every business to start using data integration tools to stay ahead of their competition.
Data Integration Tools
For integrating all your data on a single platform, you will need to require to use data integration tools that will handle all your data needs of data. Here, we are going to describe. Using data integration tools can provide you the following benefits:
- Ability to process data from several sources like spreadsheets, enterprise applications, mainframes among a few others.
- Ability to process data from web pages, email, social media, and other platforms.
- Elimination of duplicate, incorrect, and inaccurately formatted data.
- Syntactic checks to make sure that the data is in accordance with the business policies and rules.
- Metadata support.
- Realtime data Integration.
Different Types of Data Integration Tools:
On-premise data integration tools: These tools help in integrating data from several on-premise data sources. The tools are installed in the private cloud or local network and include native connectors for batch loading from multiple common data sources. They are ideal for large databases.
Cloud-based data integration tools: Cloud data integration tools are integration platforms as a service that integrates data from multiple sources into a cloud-based data warehouse that provides real-time visibility of data to the users. These tools facilitate more efficient use of data.
Open-source data integration tools: The open-source data integration tools help you to have full control over your data in-house. They are the perfect and cost-effective solutions for all your in-house data consolidation needs and also handle your data security and compliance needs.
There are different types of big data integration tools available from which you can choose the one that meets your open-source, on-premise, and cloud-based data consolidation requirements. Choosing the right tool plays an important role in the overall data integration process. To help you choose the right data integration tool, we are listing down some factors that you can consider:
- Size of the Enterprise: Before putting your hands on a data integration tool, evaluate the size of your enterprise as the utilization of the tool differs from organization to organization. Select a tool that can grow with your requirements for data consolidation.
- Integration use-case: Choose a tool that can synchronize data between on-premises systems, IoT devices, and cloud applications or exchange data between internal business processes or applications across different organizations.
- Source systems: If you have multiple CRM or sales processing applications, then you will need additional storage space. Therefore, select a tool that will provide you a solution to connect to various new streaming and web-based data sources.
- Security and compliance: The most important factor to consider is security and compliance. Select a tool that secures your sensitive organizational data.
Data Integration API
An application programming interface (API) is a building block of programming that ensures the flawless functioning of enterprise systems. It connects different devices and programs to facilitate data sharing between them. With an API, an organization can create a channel to sell its products and services online. It enables access to services by adding codes to applications.
The APIs are used by developers to exchange data and execute a set of routines in an organization. API technology increases the level of efficiency by providing a platform where data can be shared with external parties.
Data Integration in Data Mining
When large amounts of data are combined on a single platform, it requires precise organizing. Data mining is a process in which a large amount of data is organized and recognized with the help of computer science, artificial intelligence, databases, and statistics.
Data Integration Approach
Big data integration has different approaches that can be utilized for populating a data warehouse. Generally, there are two approaches that are widely used by the organizations which are as follows:
- Data Integration ETL (Extract, Transform, and Load): In this approach, the data is extracted for several source systems, transformed into a systematic format, and then loaded into a data warehouse so that the users can work and report on the consolidated data.
- Data Integration ELT (Extract, Load, and Transform): This approach begins with extracting data from various source systems, loading it into the data warehouse, and then transforming it into a systematic format. This approach helps the users in meeting auditing and security requirements.
Data Integration Process
Data consolidation can sound like a complex and timely process, but the reality is a little different. With a data integration platform, business owners can bring effectiveness in their operations and eliminate the chances of data duplication and errors. Here, we have broken down the data integration process into 5 simple steps that are as follows:
- Determine how your data will sync: Before setting up an integration, you should have an idea about what you want to gain from it and for what data you want continuous access. To avoid confusion, create a spreadsheet with three columns that will include: system to integrate, object that data lives in, and fields to sync. Once you are clear about your integration plan, then you can move on to the next step.
- Choosing the right integration system: Once you have decided on how you want to integrate your business data, then you can select the platform for enabling the integration. Choose a platform that is cost-effective and offers bi-directional syncing. But in case your business only wants one-way integration, there are other systems in the market that can work well for you.
- Map fields and objects across each platform: After deciding about what data to sync and integration system to use, its time that you start mapping the fields across each platform for ensuring a seamless connection between them. Go for bi-directional syncing that will update the changes in your data in real-time.
- Set up filters to refine your integration: Once the integration process will begin, you will find a huge flow of information into your system in which you may find some data useless or inaccurate. You can refine the data by setting up filters that prevent bad or unwanted data from syncing.
- All set to integrate: Now, everything required for data integration is completed. You are all set to sync your past and present data between the systems you are integrating. Many integrations platforms store your data in their database to sync a cleansed copy of data into a specified system.
Benefits of Data Integration
Data integration helps organizations to bring relevant data from different sources to a single platform that provides users with a real-time view of business performance. Transforming data into useful information will help businesses to make decisions that will accelerate their business growth.
Some of the main benefits of data integration are mentioned below:
- Boost the efficiency of operations: When an organization automates its data integration process, it can invest more time to analyze the data. The integration also saves the time of the employees as now they don’t have to build connections from scratch at the time when they need to develop an application or run a report. Running the right cloud data integration tools can save more of your employees time so that they can focus more on critical matters.
- Reduces the chances of errors: Manual gathering of data involves a lot of effort, and the employees must know every account and location that they might need to explore to ensure that the data sets are accurate as well as complete. But most of the time, the manual gathering of data brings a lot of errors with it. Data integration solution synchronizes data effectively and reduces the chances of errors to a great extent.
- Availability of data: In the competitive business world, it is essential to connect all data sources as quickly as possible to obtain all the relevant information on a single platform. Data integration services will provide you the benefit of accessing all your data on a single place so that you can make effective decisions.
- Better collaboration: By just integrating data, you can improve collaboration between your employees as well as trading partners. The automatic flow of information can affect your business positively in the long run.
- Better insights bring improvement: If you are getting all your organizational data in one place, then you can easily analyze it to create insights that will have a positive impact on your operations and customers. With all the information in a single place, you can make decisions that will straight away improve your business processes.
- Data integrity: Data integrity refers to the accuracy, consistency, and completeness of data. In the business sector, where data plays a significant role, many customers face the problem when they receive incomplete or false data from their trading partners. A useful data integration model can help them to combat this problem by checking the data against the validation rules and then automatically reverting the data to the sender for rectification.
- Provide competitive advantage: There are many data integration companies that provide you the strategy that will help you to improve your actions to enhance the accessibility of data both internally as well as externally. With effective data integration, you can focus on your primary goals and provide better services to customers that will help you to stay ahead of your competition.
Challenges in Big Data Integration:
Bringing data from several data sources and converting them into relevant insights is not an easy task. There are numerous challenges faced by users during the data integration process that does not let them have a perfect integration. Some of the challenges faced by users are mentioned below:
- Understanding the overall data integration process: Every company understands their increasing requirement of data integration, but sometimes they are not clear about the types of data that need to be collected, the sources of data, the integration systems, what sort of analysis will be conducted, and how frequently the data reports will be updated. Therefore, before starting the data integration process, the companies should sort out all the mentioned things.
- Data Mapping: To map data from one source to another, you should have the knowledge of the source systems and business analysis process. You must consider the business rules that are embedded in the source systems to create an integrated data set.
- Syncing data sources: When you integrate data from different sources, you may sometimes find that data coming from one source is not up-to-date in comparison to the data from another source. Therefore, accurate syncing of all the data sources is essential to get correct information about your business processes.
- External data: When the data is taken from the external sources, it is not of the same level as compared to the internal sources that make it difficult to examine. Sometimes, the external vendors add a term in the contract that restricts the sharing of data across the organization.
- Infrastructure problems: Systems backed with advanced technology generates various types of data from different sources like sensors, cloud, IoT devices, and videos. To meet the requirements of integrating all these data, you should quickly adapt to your data integration infrastructure.
Analytics, business intelligence, and competitive edges are all at risk when it comes to data integration. Therefore, every organization must have complete access to all forms of data from each source. With cloud data integration platforms, the users can update data virtually from any location and at any time that helps organizations to make decisions at the right time to gain success in the market.