Data is the lifeblood that provides enterprises and businesses across the globe the information they need to grow and make actionable decisions. In order for those judgment calls to be made with heightened accuracy, good data practices have to be adopted at every touchpoint of a company.
Data integration is one of these all-important areas. But what is it? In this useful guide, we’ll assess exactly what makes data integration vital for a variety of industries, as well as how it works, different types of integration techniques, the challenges and benefits, modern approaches, and much more.
What is data integration?
At its core, data integration is the process of combining multiple streams of data into one digestible and unified view. By pooling data in a way that allows for actionable business decisions to be made, companies are able to learn, grow, and adapt to ever-changing regulations and market trends.
At its core, data integration exists to make the analysis and understanding of disparate sets of data easy and fluid. It serves as a way for varying data sets to work symbiotically, even if their primary focus was initially placed on totally different areas.
How does data integration work?
Much as with most data management processes, there’s more than one way to effectively carry out a successful integration. Here are some of the most common ways that the data integration method might be undertaken.
ETL vs ELT
ETL stands for Extract, Transform, and Load. This process sees data taken and transferred to a standalone processing server, before being loaded to a core data warehouse. This more traditional method is used when data must conform to the regime of a target database. The use of a secondary server, aside from the core data warehouse, makes ETL a little slower than other integration methods. It can also mean costs add up, and makes the management of data tricker and more complex.
This method stands for Extract, Load, and Transform. And while that name might appear similar to ETL, the process is entirely different. Unlike ETL, data transforming does not need to take place before data is loaded to a warehouse. Once this raw data is uploaded to the warehouse, data cleansing, enrichment, and data transformation all take place. This makes the process much faster and more cost-effective. In some instances, the load and transform processes can happen simultaneously.
Batch vs real-time data integration
Batch data integration
This method sees a company collecting and then storing a large quantity of data, and then processing it as a group (or “batch”). This is the more traditional approach to data processing, and it’s preferred by those who want to save network bandwidth by compressing data. Batch integration is best utilized when data doesn’t need to be constantly processed or accessed —such as for historical analysis, rather than immediate instantaneous decisions.
Real-time data integration
This approach processes data as it’s collected, meaning your results are offered up in real time. This is a vital approach for enterprises looking to get instant results and actionable advice when utilizing data sets. These faster results are seen as preferable by a lot of industries.
Other ways data integration works
Streaming
This fluid and constantly “in motion” form of integration sees data consistently streaming into a data warehouse, having been ingested, filtered, transformed, and then enriched before storage. Owing to the nature of this kind of data, it can be seen as the primary form of real-time integration, and it allows organizations to minimize the risk of fraud and make real-time decisions based on data analytics.
Canonical data models (CDMs)
CDMs work to allow different data subsets to communicate effectively with one another. It does this by translating data into a syntax that can be read by a secondary system, which in turn processes this information and understands it in its own canonical format. In doing this, an organization can have several systems connect and talk with each other, even if the initial language, syntax, or base protocols differ.
Gain control of your data
Four key types of data integration use cases
Data integration can be utilized by enterprises in a variety of ways. While the ultimate aim of the integration process is to optimize data on a company-wide scale, how this is achieved will differ depending on the use case. Here are four methods of some of the most common integration examples.
As the name suggests, a data warehouse is a centralized hub of structured and semi-structured data, which has been drawn in from multiple external sources. Integrating data warehousing makes it easier for a company to carry out ad hoc analysis and custom reporting. The long-term storage options that a warehouse provides also allow for the tracking of data over time, which helps to support forecasting efforts and promote growth through business intelligence.
A core component of data integration, this method is normally drawn upon when a significant amount of data from varying and disparate sources needs to be pooled together into one readable data store. This consolidation of data is vital for enterprises who want to be able to quickly read, process, and make informed critical decisions using their existing data.
This form of integration allows a user to access the source system of the data they want to analyze, getting real-time access without the need to extract or transform it to comply with internal single data models. This natural accessing of the data in its truest form ensures a reduced risk of errors, and also makes it possible to pull out single entity views of any relevant information.
Also known as “database replication,” this method sees data copied to guarantee that information stays identical between data sources. This process allows for different end users to access and see the identical results when analyzing data, with the replicated copy serving as a safety net for the original data set.
How to strategically integrate data?
The way you approach data integration will depend on your desired outcome. It’s vital for a company to understand which data strategy works best for them in order to optimize the wider process. Consider each of the following when applying a strategy:
Application-based integration
When a company has several standalone applications that need to be able to work in tandem with each other, merging data and workflow processes are often a necessity. Interconnected data exchanges make it possible to heighten efficiency, with an enterprise working as one cohesive unit, rather than a series of individual parts. A good example might be in healthcare, where integrated data across multiple applications allows a physician to quickly pull up multiple health records for a patient.
Middleware data integration
This software serves as the centralized hub through which connectivity can be achieved across multiple applications or application components in a distributed network. The name comes from the fact that most middleware acts as a mediator between the front end of an application and the database through which a client is requesting information. Middleware also works to safely secure data transfers while also helping to dynamically manage traffic moving between distributed networks.
Manual data integration
This somewhat outdated version of integration sees a company hiring a dedicated data engineer who’ll manually manage and code data connections in real time. It’s their responsibility to clean and organize data movement, ensuring it moves smoothly between applications. This naturally lends itself to human error, as well as being a very time-consuming and slow process.
Uniform data access integration
This form of integration ensures that all data points stay in their original location, but acts as a lens through which different syntaxes, codes, and languages are translated to provide a company with a clear and immediate breakdown of data. This allows for a unified view of data, while also negating the need to store any information yourself. This approach is best utilized alongside data replication techniques.
Common storage integration
This process sees different sets of data transformed before being copied to a single data warehouse. This method is useful for large enterprises who want to store and access data from one individual point, where the need to run quick business intelligence analytics is a necessity. While this method does make data more uniform and of a higher integrity, it might also mean data storage and maintenance costs are higher.
Cloud-based data integration
This innovative form of data integration is becoming increasingly popular as cloud technologies continue to improve. These systems are more agile, as they’ll allow you to deploy integration features faster between on-premises data points and cloud applications. This also allows a company to scale at a manageable speed, while providing the flexibility to manage big data sets without the need for servers.
The importance of data integration
Those unconvinced by how intrinsic a part data integration can play in operational success should not underestimate the power that this method can have. Here are some of the areas where data integration plays a pivotal role in the success of any enterprise.
Accessing a data warehouse
We’ve already discussed how the collating of key information in one dedicated data warehouse is pivotal to the success of any enterprise. This solitary store of data makes it easier to pull from, manage, and plot core company strategies around. Data integration is what makes this all-important hub of readily available information possible in the first place. Without it, this centralized hub wouldn’t be possible.
Business intelligence
In order for any business (whether an SME or industry leader) to grow and evolve, a measured approach needs to be taken in regards to decision-making. In order for that process to be optimized, data integration and management need to be made a priority.
Master data management (MDM)
This technology-enabled system is the umbrella network under which data integration strategies operate. The integration of data makes it possible for MDM to ensure the uniformity, accuracy, stewardship, semantic consistency, and accountability of an enterprise’s official shared master data assets.
Data fabric
Data fabric refers to an architecture and set of data management practices that enable organizations to create a unified and integrated view of their data across various locations and formats. This works in tandem with data integration to guarantee enterprises are making holistic, data-centric decisions. It closes the gaps between different data platforms (for example, an HR system, supply chain, or customer database), and places it in one centralized environment. This helps to provides better clarity in the decision-making process.
Benefits of using a data integration solution
There are a number of immediate positives which companies of all sizes might experience when a data integration solution is introduced. Some of the most relevant to day-to-day and long-term operational success include:
Wider insights and analytics
The more actionable data you have on hand, the more insights and analysis it’s possible to do to see an enterprise grow or adapt to new challenges in the workplace. This heightened intelligence can only be a good thing, offering companies a wider picture and unlocking information that might have been hard to see without these insights.
Data quality and integrity
Data integrity is key for guaranteeing that these insights provide useful and relevant information. This is the term given to the assurance that data is consistent and adding value throughout its entire lifecycle. By adopting an autonomous approach to data management and storage, an enterprise will be able to ensure data retains this quality at all points.
Increased competitiveness
By placing yourself in a position to improve the accessibility of both internal and external data, a company can optimize processes, heighten service offerings to consumers, and strategize for the future. This ultimately serves to maximize competitiveness in any given sector, while increasing the chances of future growth.
Improved collaboration across a company
Having a dedicated warehouse of accessible data (as well as consistent processes for using and managing it) across a company is also a major benefit. This provides a centralized hub, which in turn ensures consistent data usage and understanding across all departments of a business or large company.
Challenges of data integration
Just as with any wholesale change to your data approach, integrating data might bring with it a handful of challenges. While these may feel like large obstacles at first, they can be traversed. Understanding what hurdles your company might face during data integration is the best way to combat any potential issues.
Scalability as a company is often not an issue, but if a data integration plan isn’t designed with this growth in mind, it won’t be able to keep up with the enterprise's demands. This can lead to data being handled incorrectly and mismanaged. The solution here is to ensure that growth is at the forefront of any data integration plan, and that any proposed future mergers or upscaling attempts are factored into the rollout of any integration.
As we’ve already discovered, a manual integration plan is not always the best approach for managing data. While handy for small businesses, larger industry leaders will be prone to a number of issues utilizing this method, such as a higher risk of human error, lost time, heightened costs, and a lack of cohesion across the company. Instead, enterprises need to ensure their data integration methods are automated.
Inaccurate, misleading, or duplicated data won’t automatically be eradicated with the use of data integration. Rather, it will actually make it harder for valuable, accurate insights to be garnered. The solution here is to rely on a data quality management tool to analyze your data before usage.
At its core, a good data integration strategy exists to help a company achieve a desired aim and provide evidence to support this direction. The end game needs to be predetermined in order for a company to achieve these aims. Simply hoping that data will be useful for growth is far less likely to result in actionable results.
Data integration vs data unification
While similar in nature, data unification differs slightly from traditional data integration approaches. At its core, data unification exists to combine your datasets into one comprehensive source of information, rather than integrating them into a storage system like a data warehouse.
Think of it this way:
Data unification combines all your existing data into one clean, error-free data subset, which can be accessed by one universal method.
Data integration draws on data from different locations, working to transform data and store it in one centralized warehouse or storage facility.
Both have their merits, and we've already discussed the benefits of data integration. Here are some of the core takeaways that using a data unification plan will offer a company:
Improved insights
Having unified and accurate data makes it simpler to segment your target consumer base, which in turn provides more valuable insights when it comes to understanding their habits.
Personalized campaigns
Data unification allows for any campaigns to be personalized and hyper-focused on specific customers. You’ll be able to infer what sort of words and images will appeal to them, and even consider things like the kinds of device they regularly use to access your products or service.
Increased revenue
A more finitely targeted marketing approach has the natural byproduct of increasing consumer spending or engagement. That directly translates to an increase in revenue. What’s more, you’ll also be able to understand where your company loses most people during the sales cycle. By having this information, you’ll be able to work to discover what might be triggering them to exit the consumer journey, and pivot to target that area.
Customer satisfaction
Fragmented or inaccurate data heightens the chance of misinformation being spread to a customer, as well isolating them with marketing campaigns that don’t appeal to their needs. Data unification gives you a much more well-rounded view of whom you’re dealing with — which in turn means consumers and clients are left feeling seen and listened to, and therefore happier.
What is big data integration?
Big data integration involves sophisticated processes designed to handle the vast volume, variety, and velocity of big data. It consolidates data from diverse sources, such as web data, social media, machine-generated data, and the Internet of Things (IoT), into a unified framework.
To support big data analytics platforms, which require scalability and high performance, a common data integration platform is essential. This platform should support data profiling and quality, offering users a comprehensive and up-to-date view of their enterprise to drive insights.
Real-time integration techniques are crucial in big data integration services. These techniques complement traditional ETL (Extract, Transform, Load) technologies and provide dynamic context to continuously streaming data. Best practices for real-time data integration consider the challenges posed by its dirty, moving, and temporal nature. These practices include:
Conducting extensive stimulation and testing upfront
Adopting real-time systems and applications
Implementing parallel and coordinated ingestion engines
Establishing resiliency at each phase of the pipeline to anticipate component failures
Standardizing data sources with APIs for improved insights
Who needs data integration?
Regardless of industry, data integration can play a vital role in the continued growth and success of any business, large or small. Here are some of the different sectors that might rely on this transformative technology, as well as exactly how they’ll prosper as a result of implementing a solid integration strategy.
Healthcare
The care of patients can be compromised if medical records and other personal information is spread across a series of databases. One comprehensive record will optimize the treatment of patients by medical professions, while also reducing the risk of sensitive information being mismanaged.
Telecommunications
Good customer service practices are the crux of any successful telecommunications enterprise. A strong customer and company relationship can be achieved by providing a detailed, all-encompassing view of data. This will be heightened further if this information is accessible in real time.
Finance
Financial institutions rely heavily on safe data integration processes for things like fraud prevention and detection, the measuring of credit risk, maximizing cross-sell and up sell opportunities, and retaining customers. In an industry where the safe management of data needs to be a priority, data integration is a must.
Marketing
Those in the marketing industry rely heavily on understanding a core consumer base in order to optimize strategies and target the right people at the right time. In order to launch campaigns that are timely, influential, relevant, and effective, accurate data insights are needed to increase the chance of success and avoid wasting a marketing budget.
Retail
Several data sets need to be managed when working in retail. Whether it’s inventory management, consumer data storage, sales and revenue numbers, or predictive seasonal trends, it’s natural for a company to rely on a variety of systems. Data integrations allows these multiple channels to succinctly merge together, offering a unified, 360-degree view of the entire business.
Useful links
We’ve discussed a lot in this guide, but there might still be more you want to discover about data integration. Browse these handy sources to learn more.