In the tech business, there’s this big myth that integrating data is as easy as pie. People think that all they need to do is plug in a few tools, press a couple of buttons, and boom – their data from various sources comes together, skyrocketing their performance overnight. This cliche story makes it all look so easy, doesn’t it?
But the reality looks different. Data integration is not always a smooth sailing process. It involves bringing together data from many sources into one place, and it comes with numerous challenges. Plus, it would help to have an effective big data strategy to make data integration work for your business.
Data integration, after all, can be a tough nut to crack due to data silos, inconsistent formats, and outdated information. Handling large volumes of data, ensuring security and privacy, and managing real-time data add to the complexity. So, tackling these challenges is crucial for smooth integration.
It’s no surprise that 89% of companies report data integration issues, regardless of whether they handle integrations in-house or with third-party help. Plus, almost 40% of projects fail due to difficulties in integrating different data sets.
But despite roadblocks, data integration shouldn’t be taboo. We’re here to help you. In this article, we’ll cover five common data integration challenges and show you how to solve them. Let’s dive in.
What Does Data Integration Mean?
Before moving to integration challenges, let’s clarify the meaning of data integration. Data integration is about bringing together data from different sources into a unified and coherent format. It involves copying, collecting, and changing data. So it can be stored in a place like a data warehouse or data lake.
Companies collect data from many places – like databases, apps, APIs, cloud services, and spreadsheets – but it often ends up with data silos and inconsistencies. The goal of data integration is to fix these issues by combining data and making it consistent and easy to use for analysis and decision-making.
Companies that are good at data integration have significant advantages, like faster operations, better data quality, and more valuable insights.
Today, businesses lose an average of $9,7 million annually due to poor data quality. That’s why it’s vital to understand all the data integration problems to use data more effectively and benefit your business.
Top 5 Data Integration Challenges
An American entrepreneur Viktor Kiam said, “Entrepreneurs understand that there is little difference between obstacles and opportunity.” It means that you should see challenges not as roadblocks but as chances to grow. That’s why the problems we’ll present here are not to scare you but to prepare you to deal with them and innovate.
Challenge 1: Data quality: From fragmented to consistent
The first big challenge in data integration is ensuring data quality. Big data only becomes valuable when it’s accurate and error-free. Quality data leads to great decisions, but poor and inconsistent data can cause serious consequences for your company.
Imagine you try to analyze customer buying patterns by jumping from one system to another. You feel lost in customer feedback, in-store purchases, and online sales. As a result, what you have is fragmented and inconsistent data in various formats. So, you can’t draw a unified picture of clients’ buying patterns and understand what products are the most in demand.
How to tackle data quality issues
The goal of your data quality management should be to understand and enhance data accuracy. For example, data validation and data cleansing can help to transmit data from the data lake to a data warehouse while preserving its precision. The use of automated data integration tools can also maintain data accuracy with minimal chances of mistakes.
It’s also recommended to implement data quality checks at each stage of the data integration process. This is an effective way to guarantee consistent quality. Batch processing is also a good option to verify the correctness of data before and after integration.
Struggling with data quality issues? Let us help! Our team of experts will enhance your data quality and drive better business decisions. Contact us today to transform your data into valuable insights.
Challenge 2: Data security and privacy: How to safeguard sensitive information
In one of the largest cyberattacks in US history, over 30,000 businesses were affected by a hack on Microsoft Exchange email servers. Hackers exploited four zero-day vulnerabilities, gaining unauthorized access to emails from small businesses to local governments. Once in, they could access data, deploy malware, and take over servers.
This situation proves that data security and privacy remain core dilemmas for companies dealing with large volumes of data. One of the main responsibilities in data handling is to keep sensitive data secure and comply with privacy regulations during data integration.
How to combat data security issues
During data integration, sensitive information moves through various channels and platforms, increasing the risk of security threats. This risk is higher, especially when you transfer data from legacy systems to modern platforms or cloud data warehouses.
That’s why security measures such as data encryption, secure data mapping, and the use of a secure integration platform are essential to protect sensitive data. When automating data integration, it’s also crucial to use tools that ensure compliance with legal regulations.
Challenge 3: Diverse data sources: How to unify data formats
Data integration comes with ups and downs because of the heterogeneity of data sources. Businesses need to integrate data from many sources. But the diversity of information channels is not the only dilemma. The main data integration challenge is that companies must deal with various data formats and structures. As a result, the integration process becomes more complex and time-consuming.
The data integration team must spend significant time and resources to unify the diverse data formats and structures. Plus, they should constantly monitor to ensure data quality and consistency.
How to address diversity in data formats
User data may come in XML files, JSON documents, spreadsheets, text files, or even hardcopy paper. Each format has its unique structure: Excel uses rows and columns, while JSON and XML are hierarchical. Integrating these formats requires specialized data integration tools that can convert and unify different data types seamlessly.
To manage this diversity, you may use comprehensive data mapping. Automated integration tools help map, validate, and cleanse data, ensuring quality and consistency.
Contact the Forbytes team to simplify your data integration process and ensure data quality and consistency.
Challenge 4: Technological limitations: How to integrate data from legacy systems into modern software
Another data integration challenge is related to technological limitations. Not every software and hardware can manage, transform, and integrate tons of data. The main dilemma here is how to migrate data from legacy systems to modern integration technologies.
Software constraints can restrain the efficacy of data integration processes. Manual data integration and batch processing can’t keep up with real-time data, leading to delays in insights. Legacy software may struggle with large data volumes, causing crashes and slowdowns. Physical limitations of servers and systems can result in slow data retrieval and hinder business processes.
How to overcome technological constraints
To overcome tech limitations, companies may need to invest in modern data management software. For example, implementing cloud warehousing can be a great solution to process and store larger amounts of data compared to traditional counterparts.
It’s also recommended to migrate from legacy systems to modern data integration technologies. Legacy systems often pose significant integration challenges, making it difficult to interact with current software. Plus, they can lead to problems with data quality management since many legacy systems don’t support data validation or other data operations.
Challenge 5: Human resources: How to find skilled experts
The final challenge in data integration revolves around human factors. First, it’s a dilemma to find skilled experts to navigate the data integration process. Second, it’s necessary to foster a data-driven culture that will lead to actionable insights and decisions.
Hiring skilled professionals and implementing data governance initiatives are crucial for the success of any data integration project. These factors ensure quality data and smooth integration processes.
How to address the human factors
To address the human factors in data integration, it’s recommended to invest in training internal staff or hiring external professionals. Balancing these two approaches can facilitate data integration without disrupting business processes.
Another essential strategy is to foster a culture that prioritizes the value of data in decision-making. It’s crucial to communicate the importance of data for operational effectiveness and underline the parallel between data-driven decisions and business excellence. When your team members understand the value of data, they will be more motivated to use and integrate it effectively.
Ensure quality data and smooth integration processes by partnering with our team. Contact Forbytes today to get started.
How Forbytes Helps Businesses Consolidate Data
At Forbytes, we help businesses consolidate data to produce valuable insights and data-driven decisions. Here’s one on-point story to tell.
Our client, Bygghemma Group, is a top Nordic online store for home and garden products. They sell many items through different e-commerce sites and have grown to 25 countries, becoming a major player in the European home improvement market.
Before working with Forbytes, our client used many separate data tools like databases, PIM systems, ERP systems, Google Analytics, Google AdWords, and Excel. This made it difficult to get a clear view of their 140+ stores and systems.
So, our client’s data was fragmented and hard to store and manage. It was a problem for them to extract practical value from big data. Another issue was that each dataset was managed by different stakeholders.
Therefore, Bygghemma Group turned to Forbytes to consolidate their data. See how we approached our client’s request.
We moved the data to BigQuery, where it could be accessed in real-time and historically. Then, we linked it to advanced BI and analytics tools so the client could view, manipulate, and analyze the data for useful insights.
Solving Data Integration Challenges with Forbytes
All in all, no one said data integration would be easy. But it’s worth it. Effective data integration is more than just a technical task; it’s crucial for staying competitive. It helps businesses make smarter decisions and operate more flexibly. That’s why data integration challenges shouldn’t scare you but empower you to take the most of your data.
Partnering with Forbytes, experts in data engineering services and solutions, can help you tackle these challenges efficiently. We assist businesses in structuring, managing, and optimizing data to streamline operations and propel them forward. From data engineering consulting to advanced data solutions, we offer everything you need.
Want to consolidate your data and overcome data integration challenges? Contact us for a free consultation to discover how to extract more benefits and valuable insights for data-driven decisions.
