A recent study outlined that data silos result in employees losing 12 hours every week. Such silos prevent departments from working together and sharing information. They restrict stakeholders' access to the necessary relevant data.
Data integration is one method for destroying data silos. It enhances data quality and provides a structured view of the data. In this article, we outline the challenges and solutions of data integration. So, without further ado, let's dive right in.
Varied Data Sources
Data can be stored in a variety of formats, including unstructured (documents, emails), semi-structured (XML, JSON), and structured (relational databases, spreadsheets). Different handling methods and processing tools are needed for processing and analyzing each format.
The challenge is made even more difficult by the diversity of data structures. Data silos are largely a result of diverse data sources. It is difficult to get a cohesive and accurate representation of the data when it is dispersed across various sources.
For businesses to curb data silos, they must thoroughly understand their data sources' quality, schemas, formats, etc. The integration process can be streamlined by implementing data integration platforms, like enterprise service buses (ESBs). At the end of the day, to deal with the complexity and variation in the data sources and platforms, businesses must employ the appropriate tools and technologies that suit their data integration requirements and goals.
Inconsistent and Poor-Quality Data
Duplicate records, inconsistent data values, and mistakes in data entry or processing can all cause data quality problems. As a matter of fact, data quality frequently decreases due to data inconsistencies that may traverse silos.
When you're working with a modest volume of data, it is easy to monitor and handle any concerns that transpire. However, the management task becomes overwhelming and error-prone when handling substantial data volumes. Inaccurate data causes lost sales, missed opportunities for actionable insights, and even reputational harm.
To promote innovation, maintain compliance, and make better business decisions, data quality management is crucial. In order to reduce the amount of incorrect data entering your systems, the approach is to proactively evaluate your data instantly as it is ingested. Data quality management helps in getting rid of duplicates, inconsistencies, and errors. To that end, data cleansing, data validation, and data profiling are all necessary.
Scalability
There may be difficulties with data segmentation, load balancing, fault tolerance, and assuring consistency across dispersed data sources when scaling data integration pipelines in distributed systems. That said, real-time data integration scaling can be challenging to accommodate.
Scalability issues in real-time integration necessitate the use of stream processing frameworks and technologies that can manage high data throughput while retaining low latency. To that end, enterprises must design a data integration pipeline to alleviate the scalability challenges by understanding the data and its sources. Accordingly, the right tools must be chosen.
Data Security
Most businesses are now aware of the harm that data security breaches can bring to a company's reputation. According to IBM, the average cost of a data breach was $4.35 million in 2022. These breaches may compromise employee information (such as social security numbers), customer data (such as billing information), a corporation's finances (such as private company earnings), etc.
To safeguard the security and privacy of sensitive data, enterprises must implement relevant data protection methods, such as data encryption, data masking, data access controls, and anonymization.
Besides, logging and auditing technologies can be used to record and analyze data activities and occurrences — with notifications and alerts to identify any anomalies or breaches. Also, it is crucial to involve and inform the data team and stakeholders of the concepts and practices of data security.
Delays in Delivering Data
Real-time or nearly real-time data collection is necessary for several analytics procedures. However, enterprises can't satisfy these requirements if relevant data isn't gathered within the anticipated time frame. Unfortunately, relying on your team to gather data manually in real-time isn't a viable option.
Of course, data silos have a negative impact on data delivery because they necessitate manual labor to gather data from many systems or departments. Processes for manually extracting data consume time and are prone to mistakes.
Therefore, organizations should use event-driven architectures and streaming technologies as a remedy. These allow data to be ingested and processed in real-time, guaranteeing that the information used for analysis and decision-making is current. Also, businesses should optimize the data processing and transformation phases to speed up data delivery into analytics systems. To increase the speed and effectiveness of data transformations, they can use techniques like parallel processing, data caching, or in-memory computing.
Wrapping Up
Organizations can maximize the value of their data assets and enable data-driven decision-making across the enterprise via data integration. However, breaking down data silos using data integration is not a cakewalk. Technical know-how, stakeholder cooperation, and a well-defined integration strategy are all necessary.
This is where experts at Wissen can help businesses to accommodate and accelerate data integration with customized support and superior-quality engineering services. For more information, connect with us here.
