It is a well-known fact that businesses today generate significant amounts of data in their daily operations. A typical business uses hundreds of applications and several other on-premise systems for storing enterprise data. Some of it is produced by marketing, some by sales, financial transactions, production activities, research and development, and others by customer service. All in all, data is one of the most crucial components using which today’s businesses are thriving.
Doing business without data and analytics is like playing one of those blindfold games where you have to hit the target with a bat but with your eyes closed. Data from each source, when taken as a whole, can reveal strategically vital information. Now, while businesses are producing massive data every second, it isn’t worth it if it is not adequately analyzed and integrated. Loads of data can get piled and ultimately becomes unmanageable. This is where data integration comes into the picture.
Data integration is a highly discussed topic these days and often confused because the term can be interpreted differently based on the context. On the surface, the concept of data integration is quite simple. Because the majority of the enterprises store data on multiple databases, they have to access data from different sources and integrate it into a unified view.
With that said, data integration is a complicated approach. There isn’t a universal way of doing data integration, and the techniques used by the industry experts are still evolving.
In technical terms, data integration is the process of combining data from different sources into a single unified view. The integration process involves ingestion with approaches like ETL (extract, transform, load), mapping, cleansing, and transformation. Ultimately, the integration of data allows the analytics tools to produce actionable insights and business intelligence.
As said earlier, the data integration discipline is vast, and there is no universal approach to performing data integration. Nevertheless, the process usually includes a few common elements like the master server, a network of data sources, and clients accessing information from the given master server.
Simply put, the process allows businesses to systematically consolidate data from varying source systems and transform them into meaningful and useful information.
This helps with BI quite dramatically. For instance, to get a holistic view of the targeted customer base, an enterprise will combine information and data from their CRM system, customer-facing applications, automation software, emails, etc. Data analysis becomes difficult if relevant data is not pulled from their respective data sources.
The data integration process has emerged as one of the primary components in the overall data governance process. With big data integration and the need to share existing data promptly continues to grow, enterprises are employing data integration with increasing frequency.
As a result, there have been some significant developments in modern data integration that businesses need to understand. They are:
Suppose you run a company with your main product being a mobile application. With your mobile application, the users can click pictures of different objects and conduct an online image search to determine what the item is.
For this, you will need to use a lot of data integration tools, such as:
* Please note that each of the data integration tool mentioned above is just for reference purposes. A business can use any quality tool.
Ease of these application-based stores a silo of information about your app’s operations. So, for your business to get a 360° view of the operation, you will have to combine all the data from these platforms into a single, dedicated platform. This process is known as data integration.
You will be surprised to know that data integration has become prominent in the majority of the industries, including retail, healthcare, finance, and more.
Businesses have come to an understanding that integrating data is the only step to unleashing its complete potential. When enterprises have all the required information in one place, they can find and use the most relevant and accurate insights within it. They strategically implement these insights into their business operations, and this is what gives them a competitive edge.
Here are some real-world examples of how transformative data integration is and how exactly it can help businesses from different sectors and industries.
Treating patients require utmost care as well as information. Previously, patient data used to be spread across systems, which compromised care. However, with the concept of data integration, hospitals have started integrating as much data as possible into a single comprehensive record. This has resulted in elevating health and wellness, improving patient outcomes, and controlling costs. Ultimately, the healthcare industry is in the transformation phase.
Brick and mortar stores and online retailers deal with tons of data. So, their performance tracking entirely depends on having all the relevant data in one location. Data integration empowers retailers to manage sales, labor hours, inventory, and other vital metrics within their different outlets and channels.
The finance industry is hit by fraudulent activities and is a growing problem across the globe. However, banks have started integrating data, which is allowing them to determine, eradicate, and prevent instances of fraud. It is still in initial development phases, but with the inclusion of AI, things can get smooth for the people in the finance industry, as they can easily mine data for anomalies and outliers.
Quality customer service is a massive effort but vital in telecommunications. While it is hard to sustain, integration of data from as many disparate sources available can provide a 360° view of company-client relations. Issues leading to low customer services or more customer service requests can be determined and corrected.
Marketing is all about sending a message to the relevant customer. Since it is a detail-oriented approach, if things are not finely-tuned, marketing campaigns can go south in no time. Managing information on potentially millions of customers is impossible without proper integration channels and tools for data integration. This can lead to disappointing campaigns and wasted budgets. Data integration can help in keeping things organized.
Here are a few ways enterprises use data integration solutions:
Big data analytics allow businesses to accumulate valuable information from unstructured, structured, and semi-structured data. As a result, companies are integrating and combining data at once and using it to gain necessary insights for making valuable business decisions.
Businesses are leveraging the CRM (customer relationship management) software for gathering and capturing information about their customers. Data integration has made it possible for the enterprise to identify and target the right audience and reap value-generating benefits.
Data integration helps in collecting and transforming data to meet the required structures of business intelligence. In doing so, it is allowing enterprises to leverage their critical business processes like reporting, dashboards, business performance management, advanced analytics, and enabling tactical approaches and valuable strategies.
Data integration has allowed businesses to recognize the real value of data, enabling them to track and monitor data throughout an entire business process. You could use an open source monitoring tools guide to get started here.
Companies are using data integration technology in multiple ways and approaches that benefit them the most. Each process represents functionalities that others don’t. The specific method you choose to integrate data will entirely depend on your requirements, including why you want to use the data. The following are the different data integration approaches used by enterprises:
ESB is considered to be a critical aspect of data integration. It is designed to combine numerous applications over a ‘bus-like’ architecture. ESB helps in distributing tasks among the connected components and acts as a middleware located between an application based suite and framework.
An iPaaS provides enterprises with a centralized console for managing, governing, and integrating cloud-based applications with the help of tools that connect cloud services and applications. iPaaS solutions are beneficial in scaling performance needs, structuring application based integration, and adding product functionalities for increasing the value of their business relationships.
As the name suggests, the required data is extracted, transformed, and loaded from disparate sources and converted into a standard format so that analyzing common data becomes much more comfortable. Through the process of Reverse ETL, the analyzed data can be activated and put into the hands of business users.
A data warehouse is another way of consolidating and synchronization data. Business intelligence users prefer this approach because a data warehouse provides them with sufficient data for further analysis.
The most preferred approach to data integration is consolidation. Because tons of data need to be retrieved, moved, and transformed, the process can get more complicated. Data consolidation is an excellent way for companies to simplify data access.
We hope that you have got the hang of data integration with the examples and approaches we have mentioned. Now, we will move on to its purpose. While it is pretty clear why every business needs data integration, here is a detailed explanation of the importance of data integration to technical and business practices.
As said early, a typical business run using hundreds of applications and internal systems. Among those hundreds of applications, there could be thousands of interfaces. This web of networks and connections can quickly get you tangled. With a data integration plan, this web of networks and links can be easily conquered. You will be able to manage complexities, streamline the connections, and make standard data delivery to any system easy.
Integrating and accumulating data into a single platform makes it easy for the employees or associates to retrieve, scrutinize, and analyze data. When data is easily accessible, it becomes easy for the project managers to integrate any information they want into their projects, keep the data up-to-date, and share results. This availability of data all the time is the key for knowledge-sharing, innovation, and collaboration.
With accessibility comes a greater collaboration. A team will now be able to collaborate more efficiently thanks to access to relevant data. Collaboration requires sharing information, and with the ease of sharing among the internal teams, projects can be carried out in a more streamlined manner.
Data integration means transparent business processes within the enterprise. Integrated data gives people the flexibility to use data in different internal systems in whatever way they want. This allows them to understand data clearly, so they don’t have to hit-and-try. Therefore, decisions regarding projects and business processes can be made quickly and smartly.
Data integration helps in cleansing and validating the information that you are using. Businesses want their data to be robust, free of errors, duplication, and inconsistencies. A proper integration strategy can help in making the data more relevant.
This is everything you need to know about data integration. In conclusion, data integration benefits everything from real-time information delivery to data enrichment, customer data analytics, and business intelligence. So, it can be said that the original use case of data integration solution is the management of customer and business data.
Published on Aug 26, 2020
Former data analyst and the head of Whatagraph blog team. A loving owner of two huskies, too.
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