Six Data Integration Pain Points & How to Solve Them

Recent statistics from OKTA inc show that a significant proportion of large companies deploy more than 200 applications to support their operations. Data is created, captured, transformed, or transmitted by each of these applications in some form or the other.

Even a simple job like reconciling time sheets to compute overtime is spread over multiple systems – one system captures the time in and time out stamps, another one calculates the duration against the employee’s required daily hours to mark attendance, and this information is further refined till it is admissible by the payroll systems.

These tasks are made even more complex when you factor in variable scenarios, such as the current move towards remote workplaces in light of the coronavirus epidemic. It’s no wonder that the demand for data integration software is expanding exponentially worldwide. This market was estimated at USD 7.9 billion in 2018 and is expected to reach over USD 20 billion by 2026.

What Does Data Integration Software Do?

Long story short, data integration solutions unify the data from various isolated systems to one location, usually a data warehouse.

The data is extracted, cleansed, validated, transformed, and then loaded into a centralized database so that there is no latency in processing and the C-Suite has full visibility of business performance across all functions/departments. This data can be used to generate actionable business insights and holistic performance reviews, enabling the management to make well-informed decisions.

Top Data Integration Pain Points & How to Solve Them

Data integration comes with its own set of challenges. Here are the six biggest data integration problems faced by businesses everywhere. And not just that, you’ll find the solution to each of these as well. 

So here it goes.

# 1: Volume of Data

The longer a company has been in business, the higher the volume of data they will have.

When a business is starting out and they have limited financial resources, they’re more likely to prefer spreadsheets over systems. And as time goes by, maintaining those spreadsheets becomes a hassle. Typically, they’ll resort to a quick solution and possibly get one application to support a specific function.

Over time, they might get a couple more applications with separate databases to perform specific operations. Soon, they find themselves in a situation where they have data stored in 20 or 30 different places – with plenty of duplicates to boot – and they have no idea how to bring it together.

SOLUTION: Integrating such large stores of data is definitely a data management challenge. But it can be solved using scalable ETL solutions and workflow automation. 

Instead of hiring a team of tech professionals to manually extract, transform, and load data to a warehouse, businesses can identify the sequence of processes/actions/tasks needed to achieve the same. Automate each of these processes to eliminate the ‘human’ factor in data processing – error, delay, and inconsistency. Businesses can benefit from high-quality and consistent data transformation that offers clear visibility across the organization.

# 2: Organizational Silos

In the example above, the business ended up creating isolated pockets of data in each of the applications. The most logical way of bringing this disparate data back together is through data integration.

SOLUTION: There are two ways to solve this data integration challenge.

In the first scenario, the business can utilize data virtualization – establishing connectivity between all data sources to create one real-time unified entity. The data is not moved or stored anywhere except the databases it was originally created in.

Alternatively, businesses can deploy the concept of data warehousing – replicating data from all disconnected data sources into a centralized warehouse. The original source of data (and the system it was created in) remains untouched. However, this can’t be done in real-time.

Nevertheless, these data integration solutions can help businesses overcome organizational silos with ease.

# 3: Variety of Data

Generally, businesses churn out data in several different formats. Some of it may be structured data whereas some may be unstructured. Each application database is likely to have its own unique format which may or may not resemble others. Even the way metadata is generated may be different for all systems.

SOLUTION: Integrating complex and inconsistent sources of data is clearly a data management challenge. Apples can never be matched to oranges. This challenge, however, can be addressed via process orchestration. This involves creating a set of protocols to channel different types of data across individual pipelines in the required manner, with consolidation as the desired objective.

# 4: Government Regulations

It’s not only businesses that have realized the importance of data. Governments everywhere have identified it too, in addition to the potential problems it may cause if it falls in the wrong hands. From identity thefts to financial fraud, there’s a lot of harm that can be done if data is leaked (or breached).

SOLUTION: This is why data privacy regulations like the GDPR and CCPA came into being. Choose a data integration solution which is compliant to the applicable data protection laws in the region. Such policies govern the type of data (like personally identifiable information) that can and cannot be stored.

# 5: Performance Issues

Data extraction, transformation, and loading (ETL) consume computing capacity. Every infrastructure is prone to unexpected damage and downtime. This might translate into performance issues or delays in reaching the desired goal, especially if the business generates a high volume of data on a daily basis. If such incidents happen frequently, it becomes a major data integration problem.

SOLUTION: A simple solution to this data integration challenge is to always look for data integration solutions that boast about high availability so you can have an agreed level of operational performance and reliable data processing. Remember, data doesn’t wait for anyone.

# 6: Ensuring Data Quality

Last but not least, the reliability of data analytics depends heavily on the quality of raw data input into the system. Data cleansing is therefore imperative. This implies removing duplicates, establishing data integrity via appropriate metadata, and standardizing it as per the destination database requirements.

SOLUTION: This data integration challenge can be resolved by setting up appropriate validation rules and automating workflows. This ensures data inconsistencies and errors can be identified and eliminated as and when they occur. Use job monitoring to keep a check and balance on data elimination. The key is to make sure high-quality data is fed into the system so that the analytics generated are reliable.

How Data Integration Tools to Solve Pain Points  

Now that you’re aware of the data integration pain points you’re likely to face while performing data integration, the next step is to choose the right option. There are many data integration software available in the market. Each of these is built to suit specific industries and possess different unique selling propositions.

Ideally, however, in addition to the features mentioned above, you should be looking for a code-free environment. The IT team at your workplace is already preoccupied with ensuring connectivity across all fronts. A code-intensive data integration software will not only add to their plate of responsibilities but also result in unnecessary data processing delays when the IT team is already overwhelmed.

Data integration solutions like Astera Centerprise boast drag-and-drop, code-free mapping functionality so even non-technical business resources can perform data integration with ease. Avail the limited-time 14-day free trial to get a hands-on experience today.

Sharjeel Ashraf

Sharjeel loves to write about all things data integration, data management and ETL processes. In his free time, he is on the road or working on some cool project.

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