Read on to discover what data integration is and why it’s important.
Few will disagree that organizations function better and achieve more of their business priorities when using their data strategically. But with a plethora of sources and formats – think ERPs, mobile, CRMs, flat files, and dozens more, consolidating and making use of that information is a hard row to hoe.
For ages, companies used data warehouses with specific schemas for a particular application or used in the business. IT integrators, for example, would develop and maintain scripts to extract, transform, and load data into warehouses, create source-to-target mappings or extract quality insights.
However, any time a team attempted to add a new data set, it required a time-intensive manual process. They’d have to define requirements, source new data separately, and manually build new processes to make necessary upgrades in the data warehouse – all while making sure that the updated schema did not break existing code.
Fortunately, through AI/ML automation, companies can collect structured, unstructured, or semi-structured data from virtually any disparate source and integrate it into one place. Integrating data into a central repository enables teams to enhance performance measurement, gain actionable insights, and make informed decisions to deliver delightful CXs and accelerate revenue.
Let’s gain a deeper understanding of data integration, its challenges, and how AI-enabled technologies are resolving them.
What Should You Know About Data Integration?
Data integration brings together data gathered from disparate sources into a valuable, meaningful data set for business analytics and business intelligence. By consolidating data, say, transactional, warehouse status, social media, etc., in various formats and structures, into one single place, business users get its 360-degree view. The unified view empowers users to comprehend the intricacies of business by deriving analytics and, therefore, helps them make decisions accordingly.
Without data integration, companies cannot access bi-directional data streams gathered in one system in another. For instance, a business can collect data in a CRM, which nobody can access outside its sales and marketing. No doubt, other teams in the company will want to gain access to that data, perhaps when completing an order or managing credit accounts. This leads to data being shared manually, via emails, phone calls, spreadsheets, etc. And when that happens, mistakes are inevitable. With data integration, data is shared between systems in a seamless manner. Teams can access ERP data in their CRM systems and vice versa. Errors are reduced, and users can easily extract insights to make informed decisions in business.
For better understanding, let’s dive into the five types of data integration.
- Manual Data Integration: It requires a dedicated data engineer who individually creates data connections manually in real-time. So, the engineer makes the right connection to each data source, cleans and reorganizes the data as needed, and manually transfers it to a desirable destination.
2. Middleware Data Integration: As the name suggests, middleware is software that sits between applications and connects applications and transfers data between them as well as databases. By performing transformations, it can make data compatible with new systems. It’s convenient when a business aims to integrate legacy systems with newer ones.
3. Uniform Access Integration: This technique accesses data from disparate data sets and presents it uniformly. While it displays the data in a consistent format for ease of use, it keeps the source data at its original destination. That way data replication occurs, but the data always remains at the original source.
4. Common Storage Data Integration: As data becomes more abundant and complex, this technique fits the bill. Like uniform access, common storage also undergoes data transformation before it’s copied warehouse. This enables systems to have access to one data source, not thousands. Since data is accessible at any time, users can run analytics and BI solutions when necessary.
5. Application-Based Data Integration: It collects, retrieves, and transforms data, and integrates data from disparate sources. Often, it involves automation, pre-built connections to a wide range of data sources, and the ability to connect to more sources when required. This allows departments and systems to transfer information seamlessly.
What Are the Challenges of Data Integration?
Although these techniques are tremendously useful in simplifying data-driven processes, there are times when they fall short. Here are some aspects that can grow the complexity of data integration projects:
Data is Dissimilar: A business collects data using a variety of applications including, email marketing app, CRM, lead generation solutions, accounting, and billing software, and others. Now each of these applications is used and maintained by different teams, and all of these teams have their own processes to update data. The point of concern is that these departments might not be handling the data in the same format. They could be different from one another. For example, one team could be updating phone numbers as (00) 222-5555 into one application, and another group could be entering them in another application as +00 222 5555.
Data is Voluminous: Apart from heterogeneity, volume is another big problem. Integrating large volumes of data involves immense effort and time. The problem doubles if data is collected from multiple channels without a robust data management system in place. With sheer amounts of data being created daily, it becomes an enormous challenge to manage, analyze, and derive value from data on time.
The Quality of Data is Not Up to Par: Low-quality data is a big deterrent in business. Analytics obtained from invalid or incompatible data streams would mislead businesses. Replication of data is another part of invalid data analytics. If one duplicate data is mixed with the valid ones, it will disturb the cycle of operations, ultimately hurting the decision-making process.
How Is AI Resolving Them?
AI can help companies resolve the challenges associated with data integration. It greatly diminishes the heavy lifting and improves data integration outcomes. Here are the AI capabilities that help change the way businesses extract insights and make decisions:
Pre-Built Mapping: AI can automate the data transformation creation through pre-built mapping templates. It enables users with less technical knowledge to map voluminous, disparate data using machine learning algorithms and drag-and-drop features, thus, reducing the time required for data integration and ultimately data analysis.
Autonomous Learning Capability: This capability enables organizations to learn more about hidden patterns and trends from large datasets. That helps users garner accurate insights and business intelligence by applying statistical models.
Fast Computational Speed: Users leverage machine learning (ML) with adequate input parameters to deliver insights at the speed of business. With less coding and fast computation power, users can achieve the desired speed objective.
AI has brought a colossal shift in the world of business. As per Harvard business review, “AI will help businesses bring $13 trillion into the global economy and companies that implement AI will find themselves at a great advantage.” It enables users to resolve the most common data integration problems and improve integration flow and accelerate insights delivery and, ultimately, decision-making. These cutting-edge data integration solutions help organizations gain insights from complex data streams, speeding decision-making, and value generation.