Decisive information is the most prized possession in today’s times as organizations around the world are looking to get connected to their end customers. In a world full of data, one can stay a step ahead by organizing and processing the data to get meaningful information out of it. It helps in making informed decisions that are crucial for an organization’s growth and success. Data integration solutions play a major role in unlocking data from disparate sources. It not only helps in connecting all the data sources at one centralized location, but its role becomes more crucial as the concept of big data is garnering more and more attention. The integration of heterogeneous data to big data is what has led to the tremendous growth of data sciences. But integration is easier said than done. Why? Read on to know more!
The data sources range from structured formats (Relational Databases, XML, JSON, EDI, IDOC, Flat files (fixed length and delimited), CSV, etc.) to unstructured formats (PDF, IoT streams, Emails, Social media, Website, etc.) and are often disconnected and isolated from each other. The data stored is essentially scattered and diversified, having different models, formats, languages, query engines and is available at multiple locations being administered by different users. It eventually comes down to an integration solution to deal with the heterogeneity and provides a unified logical view to the end user to make informed business decisions. Also, with the volume of data and data sources increasing exponentially, expectations regarding time-to-delivery are increasing too.
For long organizations worldwide are using legacy ETL (Extract, Transfer, Load) methods. The extract step is complicated as it has to deal with multiple data sources. The transfer step modifies the extracted value in the previous step with the help of some sorting or data manipulation functions. The load step finally loads the data to the warehouse. The problem arises when the data needs to be loaded regularly instead of certain time periods. The modern integration solutions provide a good alternative to the traditional ETL approach as it can provide real-time data to the data warehouse. This builds a strong proposition for the application of integration solution in current times of data analytics and big data where real-time feeding is everything
Having said that, let's go back to our initial discussion where we mentioned how complicated and challenging is the process of carrying out the integration when it deals with disparate systems. Firstly, we need to understand why there is data heterogeneity. There are multiple reasons for that such as -
• Discrete sources of data ranging from manual logs to digital logs, all of them having a different source and semantics.
• Different units/functions of organizations prefer to keep the information in different formats.
• Data is hosted on multiple environments, ranging to cloud to on-premise on multiple servers and operating systems each of them having their own format guidelines.
• Mismatch of current and historical data stored in different locations.
Therefore, an integration solution has to deal with data heterogeneity along with the other strategic issues such as pushing the data from source to destination without altering its meaning, dealing with data inconsistencies and data value conflicts, providing secured data, and auditing the integration data. The ultimate objective is to provide cohesive data that can be drilled into meaningful information to support the underlying business interest.
A typical integration solution requires expertise, enough knowledge of multiple data sources and robust planning to carry out the complex integration process smoothly. It helps in centralizing the data which makes it accessible across the organization to retrieve and analyze. This leads to improved collaboration as any user can now use the data in the format they require, and they can share it easily with each other.
The main benefit of integrating multiple data sources lies in making the data more accessible to all the business users without any need for duplication and thus eradicating silos.
In fact, in a data-driven business landscape, an organization can get access to real-time integrated data by deploying a cloud as part of an integration strategy. This application is particularly useful for business that needs to have faster and immediate access to data for business intelligence.
For a sustainable business model, an organization needs to have a clear picture and strong control over this data to effectively understand and analyze their customer base. The power to take decisions relies completely on how well you know your data and how quickly you can interpret it. Whether the objective is to expand, to launch a new product or to do market research, integration of current and historical data will be of immense importance to provide the direction of the future path, to promote a culture of innovation and most importantly to help the organization reach its potential.