5 Biggest Data Integration Challenges (and How to Solve Them)

Enterprises leveraging analytical insights from data reap huge efficiency gains and competitive advantages. But while most enterprises are good at collecting data, they falter in driving relevant insights that matter. A 2022 Deloitte survey reveals only 28% of CMOs could integrate all customer data collected over multiple touchpoints.

The main reason for such a state of affairs is data integration challenges.

Data integration combines data from various databases and applications. The aim is to offer a consistent, up-to-date view of the data. Big data analytics require such up-to-date and consistent data.

Here are the top data integration challenges that stand in the way and how to solve them.

1. Data overload

The world is drowning in data. Internet users worldwide generate ~ 2.5 quintillion bytes of data every day. Enterprises that accumulate data without a reliable management mechanism soon become overwhelmed. The larger the data size, the costlier the storage and the more difficult the processing. Also, the presence of data that does not add value prevents informed decision-making. All it does is add to executive stress and inflate operational costs.

Too much data buries valuable data underneath a ton of useless data. To avoid indiscriminate data collection.

Have clarity on business objectives. Evaluate the data sources that need integration to further such purposes and data sources that do not further business objectives. Ensure the data sources that further business objectives are compatible with the data integration tools.
Review the data collection channels. For instance, update a landing page full of unnecessary information fields to collect only the needed data.
Speed up data processing. Most data have a shelf life. Delays between collection and analysis lead to data decay and the data becoming unusable. But the enterprise has to balance speed with security and governance.

2. Disparate data formats

Enterprises accumulate data through various applications and touchpoints in different file formats. For instance, an enterprise may have a mix of CSV, Excel, and JSON files in different database systems, such as MySQL, Oracle, and MongoDB. Data from diverse sources may also have different schemas or structures.

Enterprise systems may duplicate the information already available in a different format. Such duplication soon gets out of hand, and storage costs go through the roof. The data becomes inconsistent and untrustworthy.

The first step to integrating such disparate data is converting it to a standard format. To do so:

Enforce company-wide standards for data entry and maintenance.
Eliminate manual data entry to the extent possible. Manual entry increases the likelihood of errors and omissions.
Use data manipulation and de-duplicator tools to clean up data.

3. Data fragmentation

Enterprise data grows organically over time. It accumulates in desktops, pen drives, unused servers, cloud accounts, and many other places. Many departments set up silos for political reasons or to sidestep the IT bureaucracy and get things done faster. Accessing or even identifying such silos is difficult.

With no central control or validations, different versions of the same data may reside in multiple places. Data analysts end up wasting time seeking information that makes a difference.

As solutions,

Have a proper data management strategy in place.
Establish policies and procedures to ensure data integrity and ease of use. Prioritise technical and organisational control on data handling and storage. Company-wide data entry and management protocols reduce outdated or duplicate data in the system.

4. Poor data governance and compliance

Poor data governance and compliance compound data integration challenges. When the enterprise does not have robust governance in place, it results in inconsistent data standards and silos across different departments. The data may have inadequate metadata, making classification difficult.

Compliance requirements may also impede data integration. Compliance requirements may add extra steps to accessing and processing data. For instance, the EU GDPR regulations place restrictions on accessing customers’ personal data without consent.

Good governance ensures data is trustworthy and flows seamlessly. To improve data governance for integration:

Assign responsibilities and ownership of data. Data ownership renders clarity on who, how, when and where to enter and update data.
Define standards for data collection, cleansing, and validation to ensure consistency.
Establish access control and data use policies to improve security and compliance. Also, establish data lineage, or a system to track data as it moves through various channels. Better traceability improves data security and integrity during the data integration process.
Monitor and audit data practices to ensure ongoing compliance and continuous improvement.

5. Lack of enterprise-level support

Seamless data integration depends on simplifying the complex ecosystem surrounding the data.

Data collection depends on work processes. Enabling integration may need changes to work processes. Often, the scope and magnitude of such changes necessitate support from the top management. Data integration projects often become change management interventions, complete with resistance to change.

To streamline data integration and strengthen enterprise-level support:

Train employees in data management so that they understand the importance and implications of data integration. Ensure the team understands how to input and update data and how the tools connect in the back end.
Put in place robust communication channels to ensure free flow of data and strengthen enterprise trust.
Adopt a holistic approach to data management. Focus on data quality, privacy, security, and scalability as data management goals. Instead of deploying piecemeal tools, adopt an integrated approach. Co-opt data integration platforms, data analytics tools, and security software in the stack.
Invest in AI tools such as natural language processing (NLP) and machine learning (ML) models to enrich data ecosystems. NLP and process automation convert raw, unstructured data to structured data. The human intervention needed remains minimal.

6. Using suboptimal tools

The traditional data integration approaches involved doing it manually. Many enterprise users understand the impossibility of manual ways in today’s digital world. But they try to improvise hand-coding ad-hoc pipelines to connect sources to meet immediate needs. Such users underestimate the under-the-hood demands of data integration.

Data aggregation and sorting tasks are resource-intensive. Ad-hoc processes require temporary storage and streamlining techniques, which are logistically challenging. Most of these solutions soon become prohibitive cost-wise.

Integrating data from disparate sources requires extract, transform, and load (ETL) tools. These tools connect various data sources, standardise the format and make the data analytics-ready. Many enterprises do invest in ETL tools. But they fail to perform due diligence or try to cut corners and end up with sub-optimal tools.

These suboptimal tools fail to process complex data structures or handle huge volumes. The results include errors, duplications, and even data loss. Some tools do not have the flexibility to handle diverse data sources. For instance, they do not integrate data from non-standard databases or unstructured data from social media. The enterprise ends up investing a sizable amount of money without getting the desired results. It can even become counterproductive and can create data security loopholes. The inefficiencies cause a domino effect that erodes customer trust and brand credibility.

Smart enterprises overcome these challenges by partnering with reliable providers such as Informatica. Informatica’s data integration tools pass the key tests of being easy to deploy and use, affordable, and easy to scale.

The no-code, cloud-based, and free Informatica Data Loader allows enterprises a fast and easy way to create reliable pipelines between data sources and cloud destinations.

Informatica cloud data integration solution, an end-to-end ETL solution, delivers high-performance, high-speed and high-scale data transformation. The powerful tool connects and maps data across multiple systems and formats. It then cleanses and standardises vast volumes of data at very high velocity. One of the key USPs of the tool is a seamless integration of data from legacy systems that coexist with modern apps. Many ETL tools stumble at this point.

Informatica enables your enterprise to make smarter business decisions. The tool gives the complete picture of the process and helps businesses develop effective strategies.

Durjoy

Durjoy is a hands-on expert in Software Automation, Infrastructure and Fintech at DigiLeap Technologies, a specialist solutions provider for Chatbots, RPA, Process Automation and Financial Compliance. He has previously worked in the mobile payments sector and before that has had a long stint at Citibank across multiple roles in their operations and technology division. He loves picking on business processes in various sectors and transforming them through automation. Durjoy can be contacted at durjoyb@digileap.net.