Our ability to freely share and use data is too often plagued with interoperability challenges. Data integration can be used to alleviate this problem. Many businesses and organizations use a variety of data management systems which inevitably means that there are a variety of data formats that exist within a single working entity. Data integration, to put it simply, combines various data types and formats into a single location that is commonly referred to as a data warehouse. The ultimate goal of data integration is to generate valuable and usable information to help solve problems and gain new insights.
Data integration can be used in any and all industries. The rewards gained from unifying data into a single data source can and will help you access untapped information from within the datasets. Whether a government department looking to eliminate data silos between departments or an organization looking to merge databases between partners, data integration can play an essential role in mitigating tedious data manipulation methods.
Data is worth nothing if it just sits there. Across all industries, data must be made accessible to those who need it before its full potential can be achieved. Integrating data can help an organization leverage information that would otherwise still be hidden. Doing so can help increase communication between departments, provide better customer service, streamline operations, improve decision making, and overall increase productivity.
What Can Data Integration Solve?
Below are a few of the problems that data integration can help solve. While the solutions that data integration can solve are not limited to this list, they are some of the major topics that many technology and IT departments are dealing with today.
Big data is a huge topic in the tech world. While the idea of managing big data may seem strenuous due to high data volume, high data variety is often a bigger issue. Data integration can help to make sense of all the data that is encapsulated within your organization, whether the data is generated internally or collected externally.
Data silos refer to heterogeneous data sources that store data in specific locations. They have long been an issue due to legacy systems and disjointed departments. In the past, it would make sense for departments to select software and methods for data storage with only their needs in mind. Now it is essential to consider cross-functionality. Consolidating data can help bring proprietary, legacy data into new systems that can easily be accessed by any team member.
A common problem with using various systems to collect data or having many people collect data is ontology issues. This means having multiple types of data that describe the same thing but are organized differently. An example of this may be the way that dates are stored (“DD/MM/YYYY”, “MM/DD/YYYY”, “Month Day, Year”, etc.). By removing variations and creating a structured data warehouse, you will be able to find data more easily, analyze patterns, and make sense of it much more efficiently.
“Create once, deliver to many”. By creating a central data source, data users within your company will all be able to access the same information which can reduce the number of questions asked, increase the speed of data access, and limit the possibility of having erroneous replicated data. s can continue to use systems of their choice while end users can access what they need from a central location.
How Does Data Integration Add Value?
While the above data integration solutions listed above inevitably add value by saving time and money, data integration is also useful for much larger concepts and processes. The data management methods listed below are key examples where data integration is an essential part of their processes, however, there are numerous applications that data integration can aid in than just those listed here.
Business Intelligence (BI)
Business intelligence is an umbrella term describing the process of using technology to analyze business data to help make better business decisions. Prior to using these tools, it is essential that data is structured, cleaned, and prepared for analysis. The data can also be used to generate informative visual reports.
It is vital that decision-makers have an in-depth understanding of all necessary information to help their organization thrive. Identifying what strategies to use and what steps to take cannot be done effectively when data is left unstructured, is siloed, or is difficult to access.
Master Data Management (MDM)
MDM by definition sounds very similar to data integration itself, however, data integration occurs a step before the actual master data management is done. MDM requires the input of specific policies and guidelines that the data administrators enforce to create a “single version of the truth” for the end user.
By consolidating and managing customer information in a structured manner, you will inevitably be able to provide better customer service. Customer data integration (CDI) can help create a more efficient data management system that allows your representatives to easily access and query customer data as needed.
Data virtualization allows a user to access, manipulate, and query data without needing access to the actual data storage location. To virtualize data effectively, having a well-constructed back-end structure is key for data to be properly maintained. This will allow for front-end applications and self-service solutions to function optimally.
FME for Data Integration
Data integration is done by using a data integration tool or program. FME (Feature Manipulation Engine) is a program which takes an ETL (extract, transform, load) approach to data integration. FME supports 400+ formats which makes it a flexible data integration tool for those dealing with a large variety of data formats. FME is recognized as the integration platform with the best support for spatial data worldwide, however, it can handle much more than just spatial data.
FME has been optimized to perform a wide range of data integration functions, as opposed to being tailored to perform specific tasks. One of the main reasons this is possible is because each format that FME supports has the requirements of that format built into the tool. Many data formats are based on specific data models that must be adhered to in order to be used. Having this understanding built into FME reduces the amount of work the user needs to do to transform their data and ensures semantic translations. Additionally, FME has over 450 transformers which are tools that perform specific functions like clipping, aggregating, or attribute managing. There are even format specific transformers like the KMLStyler, XMLValidator and, JSONExtractor.
ETL data integration is not typically thought of as a process used for integrating data that is constantly updated. To make ETL and dynamic data compatible, FME Server and FME Cloud were created to automate tasks and keep data up-to-date in real time. FME Server works with workspaces that are created in FME Desktop. A user can create a workspace by selecting a reader file they’d like to convert, dragging and dropping transformers they need to manipulate their data, and finally selecting the format they’d like to write to. This workspace is then linked with FME Server and can be run when an event is triggered to ensure that the output data is always updated. This entire process can be done without any coding required along the way.
Safe Software, the makers of FME, are leaders in the technology world that strive to stay one step ahead of the data integration trends. FME is continuously upgraded to ensure that is has been adapted to support new data formats, updated versions of data formats, and large amounts of data. Gone is the idea that individual departments must work in their own data silos, with IT structures limiting the company’s potential to truly work as one. Data should be able to flow freely no matter where, when, or how it’s needed.