Businesses generate up to 200 petabytes of data per day[1]. This data is instrumental in driving innovation and making informed data-driven decisions. But, with most businesses using multiple applications with data locked in different silos, it’s nearly impossible to make decisions based on the fragmented data. In fact, up to two-thirds of data generated by organizations goes unused [2]. This is why data integration is necessary for any business looking to derive meaningful insights from its data pool. Which begs the question, how, exactly, can businesses achieve this?

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Well, read on as we dive into the intricacies of data integration, from what it is, to the methods and techniques involved.

What is data integration?

Data integration typically entails consolidating data from different sources to create a single, unified view. The process begins with the ingestion process, which involves steps such as data cleansing, ETL mapping, and transformation. Ultimately, data integration enables analytics tools to produce actionable, effectual business intelligence services.

data integration

Although there is no universal approach to data integration, it typically involves a few common elements, such as a clearly defined network of data sources, a master server, and a user accessing the data from the master server.

A typical data integration process involves a user sending a request to the master server, which then siphons the required data from different sources. Once extracted, the data is consolidated into a single, cohesive data set, which is then served to the user.

Why does it matter?

We live in a data-driven world, with most businesses using collected data to make important decisions. But most organizational data is distributed and fragmented, rendering it almost useless.

By connecting different data silos and integrating them across different departments and applications, organizations can better achieve a unified data access platform, which promotes data availability and quality across the organization.

Any business that hopes to gain a competitive advantage in today’s economy needs to conciliate its data. The level of connectivity that comes with integrating consolidated data across various departments and applications can help an organization achieve data continuity and seamless information transfer.

Benefits of data integration

Seamless connectivity

Developing data connections across an organization is a tedious and time-consuming endeavor. Point-to-point hand-coded integration[3] is not only difficult but also leaves room for error, especially when dealing with more than three connections.

Fortunately, modern solutions are programmed with pre-built adapters that can seamlessly integrate with established systems, thus allowing smooth data sharing and connectivity. Advanced data integration technologies significantly reduce set-up time and allow developers to work on one end of the system without inadvertently affecting the other.

Easy access to data

Data integration breaks down data warehouses and silos, thus providing a company with real-time data on internal operations such as stock levels, sales, and promotions. Without it, you would have to wait for data to be copied manually, which is not only time-consuming but also leaves room for error.

benefits of data integration

Using traditional manual integration methods also increases the risk of data becoming irrelevant. And new data may be available by the time various departments consolidate and transfer information to management teams. But automated data integration solutions enable organizations to keep up with fast-paced changes by providing access to real-time data.

Removal of data barriers

Most organizations rely on different systems to cater to their specific needs. For example, a retailer may require marketing and Customer Relationship Management (CRM) software, whereas a restaurant owner may need a CRM and a food management solution. The increased number of unconsolidated data from applications often results in information silos that categorize collected data based on specific applications. Ultimately, these silos limit communication across an organization by denying access to operational insights outside a specific department.

Data integration breaks down these barriers, allowing seamless data exchange across various departments. Advanced data integration solutions can also enable an organization to connect with external parties like manufacturers, suppliers, and distributors, thus streamlining outside operations.

Increased availability of actionable insights

Once data is consolidated, data analysts can conduct further analysis to generate actionable insights from the raw data. The generated insights use key performance indicators (KPIs)[4] and other metrics to suggest improvements across different operations.

Increased availability of actionable insights

For example, an organization may learn how to improve its marketing strategies and loyalty programs to boost customer satisfaction and retention through collaborative data derived from marketing, point of sale (POS), and customer relationship management solutions.

Increased data accuracy

Inaccurate and outdated data cannot be used to generate insights or drive business decisions. Therefore, any company that seeks to derive useful insights from collected data needs access to real-time, quality information.

Data integration technologies ensure that data is always accurate and up-to-date by continuously collaborating information whenever new data is entered or an event occurs. By automating your data integration strategy, you can limit human intervention, thus limiting exposure to human error and security issues.

Enhanced communication

Data integration enhances communication between various departments in an organization and other third-party associates. Consolidating your data, for example, allows stakeholders to access the relevant data and insights they need to enhance their operations. This open flow of information ultimately creates an open and trustworthy relationship between an organization and its associates.

enhanced communication

Competitive advantage

Implementing web-based integration solutions can promote your business’s scalability by giving you access to big data, which enables you to adapt and grow the business through innovative insights.

For example, consolidated data from different customer management systems enable an organization to work on brand image, demand forecasting, and marketing promotions to improve customer satisfaction and retention.

Data integration techniques

Any organization with data coming in from various internal and external sources needs a data integration approach. The data integration technique used depends on the complexity, disparity, and the number of data sources involved. Here are the most common data integration techniques;

Manual data integration

Also called hand-coding, manual data integration is among the most basic data integration processes. Unfortunately, this method is only feasible where a small number of data sources are involved. It typically involves writing code to collect the data, transform it if necessary, then consolidate it.

manual data integration

Although manual data integration might not require you to invest in any software, it is very time-consuming, and scaling the process to include more data is quite difficult.

Data warehousing

Data warehousing is also called common storage integration. This data integration process typically involves using a common data storage location like a warehouse to cleanse, format, and store data. Essentially, all organizational data from different applications are copied to a data warehouse, where data analysts can query it.

The primary purpose of querying data on a warehouse rather than on the source applications themselves is to avoid impacting application performance during the process. Additionally, data analysts can view and analyze data from the entire organization in a single, consolidated location. This allows them to check the data for accuracy, completeness, and consistency.

data warehouse

On the downside, data warehousing presents additional storage costs, not to mention the costs required to create and maintain the data warehouse. For this reason, most organizations prefer cloud storage solutions since they are more cost-effective and efficient.

Read more about Data Warehouse Implementation

Data consolidation

Data consolidation typically involves combining data from sources to create a single, centralized data source. Once consolidated, the data can then be used for reporting and analytics. Developers often use ETL software to support data consolidation. ETL applications enable developers to pull data from multiple sources, transform it into an ideal format, and then transfer it to the final storage location.

The most notable benefit of data consolidation is data consistency. Since data is transformed before consolidation, its format is consistent with the central data source. This gives developers a chance to improve data quality and integrity.

Data consolidation

However, there may be some latency involved with the process since it takes time to transfer data from the source locations to the central data source.

That said, you can significantly reduce the latency period with more frequent data transfers.

Middleware data integration

Middleware data integration involves using a middleware application as a go-between, allowing data to move between source systems and a central repository. This way, data analysts can format and validate the data before sending it to a repository, such as a data warehouse, database, or cloud.

This approach is especially effective when integrating an older system with a newer one since the middleware helps transform the legacy data into a format that newer systems can use.

That said, middleware data integration has a few potential issues, including maintenance. Additionally, the middleware must be maintained and deployed by knowledgeable developers due to the intricacies involved. Moreover, since most middleware applications have limited compatibility with source applications, the method is barred with limited functionality issues.

mildware data integration

Data federation

Data federation typically involves creating a virtual database to consolidate data from different sources. The virtual database can then be used as a central source of all organizational data. Once a query is made, it is directed to the relevant underlying data source, which then serves the data.

Essentially, the data is served on an on-demand basis rather than being integrated before it is queried like with other methods. In data federation, the data is given a common data model, despite different data sources having different data models.

Data virtualization

Unlike other data integration techniques, all the data remains in different systems whist giving users a unified view. At its core, data virtualization is essentially a logical layer that integrates data from multiple sources and delivers it to users in real-time.

Data virtualization presents numerous benefits, including the fact that you don’t have to move your data around. This basically means that you don’t have to worry about the added storage costs associated with maintaining multiple copies of data.

Data propagation

Data propagation involves using applications to copy data across different locations on an event-driven basis. This data integration technique employs the use of enterprise data replication (EDR) and enterprise application integration (EAI) technologies [5].

data propagation

EAI typically provides a link between two systems for business purposes like transaction processing, while EDR is used to transfer data between two databases. Unlike ETL [6], which extracts, transforms, and loads data from multiple sources to a unified data repository, EDR does not involve data transformation. Instead, data is removed from one source and moved to another.

Data integration tools

All the data integration techniques described above require data integration technologies like data loaders and ETL applications to support the process.

Therefore, you need to choose a tool that integrates seamlessly with all your current applications or one that allows you to create a connector if your data infrastructure doesn’t have one. An ideal data integration tool should be flexible enough to support any applications you might adopt in the future.

You should also look for a data integration tool with an intuitive interface. An intuitive interface guarantees that the tool is easy to learn and use, so your team can quickly get it up and running. Tools without an intuitive interface are often clunky and hard to use.

Tips for successful data integration

Data integration is an intricate process that requires detailed planning and extensive efforts to implement successfully. That said, there are a few tricks to ensure a relatively seamless data integration process that yields impactful results. These include:

Tips for successful data integration

Find a suitable provider

There are tons of integration tool providers out there, each focusing on specific functions. That said, an ideal data integration software should provide fast response time, connection flexibility, and extensive storage. Choosing an advanced software solution with high functionality can help you eliminate the need for custom coding.

Understand current data integration trends

Emerging trends show that many businesses are transferring their systems and data to cloud-based solutions due to their increased sharing and accessibility. Conversion may not be feasible for firms that rely on obsolete systems or manual data integration methods.

As a result, management should develop a strategy for gradually moving data to cloud computing solutions rather than attempting to migrate all operations at once. Businesses should think about their current and future software security features to ensure data protection during transfer.

Employ hybrid integrations

Businesses with several data sources want a hybrid integration solution that can pull and translate data in various forms. This process quickly becomes inefficient without an advanced integration platform, requiring custom scripts and data encryption to deliver data.

Hybrid systems have a complicated design that allows them to link multiple software solutions safely, regardless of infrastructure or format. Therefore, organizations should consult with their IT departments to assess the integration capabilities of their current systems and determine which platform provides the most seamless interface.

Modernize all legacy systems

Businesses that rely on legacy or outdated systems must upgrade in order to improve performance and expand. Failure to upgrade might leave you at a significant disadvantage compared to other modern businesses.

Gradually replacing outdated systems with cloud-based solutions allows you to stay ahead of the competition. This rapid evolution allows you to prepare a budget and focus on security when migrating data from a legacy system to a modern system.

The bottom line

Data integration strategies don’t have a one-size-fits-all approach. That said, there are a few common similarities among the processes that may integrate seamlessly with most organizations’ applications. For the best results, you should evaluate your current data infrastructure, then choose a data integration strategy that aligns with your business needs and goals.

 

[1]Findstack.com. Big Data. URL: https://bit.ly/3PQTc6b. Accessed May 20,2022
[2] Frontier-enterprise.com. Two-Thirds of Data Available to Firms Goes Unused. URL: https://www.frontier-enterprise.com/two-thirds-of-data-available-to-firms-goes-unused/. Accessed May 20, 2022
[3]Informit.com. Web Services Part 6: Models of Integration. URL: https://www.informit.com/articles/article.aspx?p=28713&seqNum=2. Accessed May 20,2022
[4]KPI.org. KPI Basics. URL: https://kpi.org/KPI-Basics. Accessed May 20,2022
[5] Academia.edu. Data Integration: Using ETL, EAI, and EII Tools to Create an Integrated Enterprise. URL: https://bit.ly/3MGyAeE.
[6] Ibm.com. ETL (Extract, Transform, Load). URL: https://www.ibm.com/cloud/learn/etl

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