The UNDENIABLE Importance of Data Integration In Business Processes

When it comes to making important and informed decisions, all successful companies rely on data. That doesn’t mean executives and business owners must wade through endless amounts of data in spreadsheets. Instead, they need easy ways to gain powerful insights into this information in order to make educated and calculated decisions. Turning raw data into meaningful actionable information is what’s commonly known as “Data Integration”.

An Introduction to Data Integration

Data integration is also about bringing data from multiple sources into a single instance or single source of truth. It is a process of combining those data into a single, unified view — and later transforming it into the target model. Before the transformation, you will need to clean the data, also known as data scrubbing, and map it into the desired template or format. Ultimately, the goal is to use analytics tools to generate compelling and actionable business intelligence (BI).

Although there isn’t a universal approach or a hard and fast rule, it’s crucial to establish a reliable data integration method. You should be able to send a request to the master server, which then intakes the needed data from various sources. The extracted data will be consolidated into a single, cohesive set and sent back to you for use.

In this article, you will find an overview of the irrefutable importance of data integration and the technicalities or complexities associated with the process. Let’s get right into it, shall we?

The Need for Data Integration

Since businesses need to analyse the insights that come from integrated and transformed information, it’s essential to leverage data in order to come up with business intelligence that empowers business owners and stakeholders to take action.

Besides, a complete data integration delivers data from multiple on-premises and cloud sources to support a business-ready trusted data pipeline. With this, teams can work together and rely on the information they receive, while businesses can make data-driven decisions for their steady growth. It also reduces the burden on business analysts as data integration eliminates data silos and repetitive work.

It’s evident that data integration powers business analytics through the ETL or ELT process (extract, transform, load/extract, load, transform), removal of data replication, and the use of engaging data visualisation. However, your company will have in-depth insights into what your customers want and what their major pain points are. This helps you spot opportunities easily — and see what works readily and what doesn’t.

Some of the obvious benefits of data integration are listed below.

Enhanced data integrity and quality: Data integration validates the information you receive by making it error-free, without duplication, and without inconsistencies. The analytics then become more relevant to your business as the overall data integrity and quality increase.
Increased efficiency and ROI: With unified and integrated information across channels, you can easily leverage different data types to maximise the return on investment (ROI). This also increases business efficiency, fostering better collaboration within the company.
Better customer experience: From the data you have collected from various channels, you already understand what your customers want and what obstacles they face. With robust business analytics, you can work towards improving your customer experience and addressing their pain points.
Seamless inter-departmental communication: There will be better inter-departmental communication and collaboration, thanks to the teams’ access to relevant data. Your staff can share information among the internal teams and carry the projects in a more streamlined manner.
Business intelligence, insights, and analytics: Since data integration makes your business information available in a single place, you can analyse the data quickly. Also, you can access integrated data in real-time, get valuable insights, find untapped opportunities, and recognise potential bottlenecks — things you need for better business growth.
Better data-driven decisions: Data integration makes the business processes more transparent, as you have the flexibility to use data in different internal systems. This also allows you to understand data clearly and drive project and business decisions quickly and effectively.

Data Integration From Different Perspectives

Let’s understand what data integration means from two different perspectives.

From a Technical Perspective:

ETL is the most common model, which follows three steps: extract, transform and load data. It allows developers and data engineers to fetch data from one repository and place it into another after converting it as per the requirements.

First, data is extracted from multiple sources, such as on-premise and cloud databases. After that, data is scrubbed and transformed into a format that meets your business needs and the constraints of the repository. Data engineers use rules, logic, look-up tables, or concatenate one data set with another. Finally, the transformed data is loaded into the target repository.

Another standard data integration method is ELT (extract, load, transform) that moves the raw data from source to target — and then transforms the data in a data warehouse server. Compared to ETL, ELT decreases the load time, making it more efficient by reducing the time consumed in data transmission.

Data can be collected from external applications, data services, unstructured files, and cloud applications. These days, cloud computing is becoming increasingly popular when it comes to creating data integration through application programming interfaces (APIs). You can also use Integration Platform as a Service (iPaaS) to connect otherwise disparate systems and get a unified solution for your business.

From a Business Perspective:

Businesses depend on analytics which relies on accurate data being fed from disparate sources. Thus, data integration preserves the integrity of data even when it’s being combined and transformed so that your company can achieve its well-defined goals. For example, if your business goal is to get a 360-degree view of your customers, you should plan to integrate customer data in your enterprise, sales, and marketing channels.

While you can gain insights by looking at different data streams, the integration allows you to see the bigger picture as a business owner. Without a robust integration method, your business might be left with only partial information, such as your company’s position in the marketplace or strategies you should follow to stay ahead of the game. So proper planning is necessary before starting to integrate your diverse data sets.

Yet, you will need the necessary technical infrastructure, including an IT team, to use a reliable integration method. In addition, you should employ strong security measures and compliances to evade the common challenges faced by small and medium-sized businesses (SMBs) when integrating data and using analytic tools. For instance, if you want to build an in-house integration software, you will need a bigger team of developers, quality assurance engineers, and IT experts. Besides, you will also need more budget and robust data management and security systems.

Here Are Some Common Data Integration Challenges

Now that you know how data integration works from technical and business perspectives, let’s discuss some of the common challenges that companies of all kinds — from small to midsize to enterprise-level — have been facing for a long time.

The first challenge is, of course, choosing between a variety of integration methods. You should plan ahead of time and use a reliable integration method suitable for the planning phase. Whether you want to use third-party software or build an in-house tool depends on your budget and resource constraints. Not all businesses can afford to create, run, and maintain their own systems, yet it obviously offers better control and unique customisation over the integration system.

Another challenge that businesses face is a lack of a business strategy when it comes to data integration and business intelligence (BI). Many seem to overlook the planning and documentation phase, regarding them as mere afterthoughts. A clear roadmap and well-established strategy will help you avoid the common pitfalls — and getting things right will help you accelerate your business growth.

Furthermore, other technical challenges include but are not limited to a) data update, b) data mapping, c) data duplication, and d) data uniformity. The data you get isn’t always where it needs to be and is often not formatted correctly. Sometimes, you might get poor quality data with duplicates throughout the pipeline. When there’s no clear common understanding of your data use objectives, that’s the most complex challenge of all.

How Could You Integrate Data For Your Company?

It’s evident that complex and diverse databases, even when housed structurally, may not be able to communicate with each other. This disparate data is usually stored in a data warehouse and runs through a predefined schema. The governing schema permits users to submit queries on the data and explore the content. Let’s put this into perspective.

Say you are running a mid-sized company. Your company will typically use different systems to store, retrieve, and analyse data. Some of them can be:

HubSpot for marketing, sales, content management, and customer service data [Marketing & Sales teams]
Salesforce for customer information and sales data [Sales team]
Google Analytics for customer tracking, user and website analytics [Marketing or SEO team]
MySQL database for storing user information [Data team]
Quickbooks for expense management [Accounting team]

To combine all those disparate data sources would mean cleaning, transforming, and unifying the data into a single source. Thus, you need a complete integration solution. Some of the standard methods of data integration are as follows.

Manual Data Integration
- In this method, data engineers oversee the integration process by manually writing the code. They do all the tasks, from collecting data to scrubbing and transforming data. Obviously, the cost gets reduced, but there’s also greater room for error. This strategy works for one-time instances only but becomes tedious for repetitive or real-time integrations.
Middleware Data Integration
- Middleware is a system that connects applications and transfers data between them. The software is coded in such a way that there’s easier access between the systems. However, middleware systems also come with limited functionalities. This method is helpful for businesses integrating legacy systems with modern ones.
Application-Based Integration
- Here, software applications do all the work. They clean, transform, and load the data from different sources. This method is simple and straightforward, and there’s an easier exchange of information. However, it can come with limited access and inconsistent results at times.
Uniform Access Integration
- By accessing data from different sources, this method presents the final information uniformly. This method creates a uniform view of data for users and has lower storage requirements. Nonetheless, businesses requiring access to multiple data sources might be slightly disadvantaged due to data integrity challenges.
Common Storage Integration
- Similar to uniform access, this method creates and stores a copy of data in a data warehouse. This makes the process more versatile and increases the data version management capabilities. However, this brings more storage costs and higher maintenance costs. If businesses can afford the cost and resources, this integration technique is the best of all.

“Off-the-shelf” Options Avaliable To Use For Data Integration

Using data integration software, you can pull data from disparate sources and formats and bring them together into a unified place. Two standard data integration solutions are available: cloud and on-premise. Choosing the right data integration software depends upon various factors, such as the size of your company, the database types, and the resources available.

With cloud-based solutions, you can manage, cleanse, and integrate data from a web-based application with the help of an internet connection. They do not require additional hardware or software to access. Platforms such as Zapier, Appy Pie, Integrately, etc., are popular cloud-based integration solutions.

For companies with enormous databases, having an on-premise solution built by a qualified and experienced team (could even be in-house) will work the best. These companies often utilise a combination of on-premise and cloud-based solutions. Since this type of integration is highly customised and specialised, it helps companies choose enterprise applications and select new vendors whenever necessary.

However, you will need extensive technical support with high security and compliance with a custom-made integration solution. Companies such as MuleSoft, Dell Boomi, and Workato provide enterprise integration platforms.

Data Integration For Growth Minded Businesses

It’s apparent that you need data integration to make smarter and more informed business decisions. You will need the right amount of intuition and statistics to make better decisions and empower your organisation’s growth and profitability. Data integration is also about solving business problems by ensuring the consistency, reliability, and scalability of data from multiple sources.

For a successful data integration implementation, you need a strategic plan and to choose the right solution for your company and its own unique requirements. A reliable and effective data integration system benefits everyone, from business owners to developers to sales and marketing teams. In short, it contributes to the overall success of any business.

If you have any further questions regarding data integration or want to discuss how you could leverage it to make your business more efficient, feel free to reach out to us. We’d be happy to help!