The global IT services market is on track to nearly double over the next decade, growing from USD 1.40 trillion in 2023 to a projected USD 2.98 trillion by 2034, at a CAGR of 7.1%. IT teams are facing a staggering 39% increase in project demands, but 98% of IT leaders face significant obstacles with digital transformation that makes it challenging to maintain the data quality needed to keep up. Issues like data silos, inconsistent records, duplicate entries, and incomplete information can lead to costly inefficiencies, decision-making errors, and even compliance risks.
To overcome these challenges effectively, IT leaders and service providers must prioritize data quality as a fundamental component of their operations. DataMatch Enterprise can be a great asset in that journey.
Designed by Data Ladder specifically for the complex data quality challenges modern organizations face, DataMatch Enterprise or DME (as we would like to call it) empowers IT services companies to achieve accurate, complete, and reliable data.
Whether you’re undertaking digital transformation to improve internal processes or seeking to enhance the services you offer to your customers, our advanced and comprehensive data quality management software ensures your data is primed for success.
13 Common Data Quality Challenges IT Services Companies Face and How Data Ladder Can Help Resolve Them
With the exponential growth of the industry and data volumes, many IT services companies often find themselves grappling with messy, unreliable data that risks disrupting their internal processes, operations, projects, and ultimately, the services they offer to their customers.
Let’s explore some of the most common data quality challenges faced by IT services providers:
1. Inconsistent Data Across Systems/ Data Inconsistencies Across Platforms
Organizations now use an estimated average of 991 applications across their digital estate. Quite often, data is stored in multiple formats and/or definitions across many of these applications. For example, dates may appear as MM/DD/YYYY in one system and DD/MM/YYYY in another, or customer information might be recorded differently across platforms.
This lack of standardization creates inconsistencies in data, which not only causes confusion, but also creates significant barrier to data integration, analysis, and decision-making. For example, when attempting to generate reports or extract insights, data professionals may find themselves spending excessive time reconciling different formats rather than focusing on high-impact, strategic tasks. Worse, these inconsistencies can impact customer-facing services; if a client’s data is mismatched between platforms, services teams may provide incorrect information or inaccurate responses, which can lead to client frustration and reputational damage for the company.
2. Duplicate Records
Duplicate records are a common byproduct of unstructured data processes, and are a widespread issue across multiple sectors, including the IT services industry.
These duplicates do not just take up unnecessary storage space, they also clutter your databases, making it hard to analyze and extract meaningful insights. According to the State of CRM Data Management 2022 report, 44% of CRM users and stakeholders, duplicate data seriously impairs their ability to fully leverage their CRM systems.
Having multiple records for the same client can cause miscommunication, impede service delivery, and also make it difficult for leaders to gauge business performance accurately.
For example, if a client’s contact information is recorded multiple times with slight variations across different systems, service representatives may struggle to determine the correct point of contact. This will prolong response times and can lead to inconsistent communication. They might contact the wrong individual or send incorrect proposals, all of which will affect the customer experience as well as skew performance metrics, making it difficult for leadership to evaluate business outcomes accurately.
3. Inaccurate Data
Inaccurate customer data is a major hurdle for IT services companies that rely on up-to-date information to deliver tailored solutions and support. In the State of CRM Data Management 2022 survey, 91% of the participants revealed that the data used to make key decisions in their companies is often (51%) or sometimes (40%) inaccurate.
These inaccuracies – whether caused by typos, outdated information, or misentered details – can severely impact a company’s ability to engage clients effectively.
For example, if an IT service provider has incorrect contact information for a client’s decision-makers, it can lead to missed opportunities, communication breakdowns, or even lost business. Inaccurate data can also misguide analytics, which makes it difficult for business leaders to assess customer needs or forecast demand accurately. Furthermore, it limits a company’s ability to provide personalized services and meet customer expectations.
4. Incomplete Data Sets
Missing data is the second biggest challenge in achieving high-quality data, as revealed in a 2023 survey of data and analytics professionals.
In the IT services industry, missing or incomplete information can severely disrupt processes and degrade the quality of services provided by a company.
When critical data fields – such as customer contact details, service requirements, or system configuration data – are absent, it can misinform technical teams, delay responses, and even cause service-level agreement (SLA) breaches.
Incomplete data sets hinder operational efficiency, as well as compromise reporting, analytics, and forecasting, all of which are essential functions that IT services companies rely on for smooth provision of services and informed decision-making.
Picture an IT services provider attempting to troubleshoot a client’s system issue but lacks crucial configuration data. This can delay response times and increase the risk of outages and failed SLA commitments. The knock-on effect is a decline in customer satisfaction and potentially irreversible damage to long-term contracts.
5. Lack of Real-Time Data Monitoring
According to a 2024 State of Data Quality survey, only 19% of organizations have implemented operational tools for monitoring data quality metrics.
In the IT service sector, this lack of monitoring can cause organizations to miss critical alerts and emerging issues that demand immediate attention. As a result, service quality is compromised and organizations take longer to identify data incidents. Research indicates that the average time to detect a data incident is over four hours. This delay increases the likelihood that issues will be discovered by someone outside the data team. Alarmingly, 74% of decision-makers and stakeholders reported being impacted by data quality issues that go unnoticed by their teams.
Without constant monitoring, IT service providers may struggle to identify network outages, performance degradations, or security incidents until clients report them. This reactive approach not only jeopardizes client trust but can also escalate operational costs due to unplanned downtime and remediation efforts.
6. Poor Data Governance
Data governance is the backbone of effective data management within IT services companies. When data governance practices are weak or poorly defined, they can lead to data inconsistencies, compliance risks, and an inability to leverage data effectively. Poor governance frameworks also often result in unclear roles and responsibilities regarding data ownership, which creates confusion and leads to inefficiencies in data handling. Without proper governance, data inconsistencies proliferate, and accountability becomes blurred.
In a recent survey, 62% of organizations highlighted a lack of data governance as a major inhibitor for their AI initiatives. This highlights the critical role that governance plays in the digital world and is quite concerning given the rising importance of AI in driving innovation and efficiency.
Without a comprehensive and well-designed data governance policy, different departments may manage data in isolation, which can lead to discrepancies in data interpretation and usage. This fragmentation hinders the organization’s ability to achieve a single source of truth, impairs decision-making, and exposes the company to regulatory compliance issues.
Data governance is also increasing becoming an obstacle to data integrity. According to the 2025 Data Integrity Trends and Insights report from Drexel University LeBow College of Business, 51% of data and analytics professionals rated it among the top three data integrity challenges for their organizations. This is an alarming 89% year-over-year increase from 2023 and underscores the urgent need for effective data governance frameworks.
7. Integration Issues with Legacy Systems
Many IT services companies face significant hurdles when integrating legacy systems with modern applications. In a recent study conducted to identify the challenges IT leaders face in AI adoption, integration issues emerged as the topmost barrier. 95% of the participants reported experiencing integration issues impeding their AI adoption efforts.
Legacy systems, often built on outdated technologies, struggle to communicate with newer platforms. This lack of integration can create data silos and inconsistencies across the organization. For instance, if an IT services company struggle to synchronize data between an outdated CRM system and a new ERP platform, it can delay project timelines, causes inaccurate data transfers, and limits on operational agility.
Moreover, legacy systems slow down digital transformation efforts and limit agility and data management capabilities. Companies that depend on these outdated environments face increased operational costs, slower response times, and more frequent data quality issues.
8. Data Security and Compliance Risks
As the volume of data continues to grow, so do the risks associated with its security. In fact, security concerns are the second biggest issue for IT leaders during advanced digital transformations and AI adoption.
IT services handle sensitive information daily, including personal client data, financial records, and proprietary business information. Failure to adequately protect this data can lead to significant security breaches, financial losses, and legal repercussions.
With cyber threats constantly evolving and compliance requirements becoming increasingly stringent, security measures that were once sufficient may no longer be adequate. Organizations must continuously assess and update their data security protocols to mitigate these risks effectively.
9. Scalability Issues
As IT services companies grow, they often face challenges in scaling their data management processes effectively. Scalability issues can manifest in several ways, including performance degradation, increased latency, and the inability to efficiently process larger volumes of data.
When IT services fail to scale their data infrastructure, they risk bottlenecks that not only affect their day-to-day operations but can also hinder their ability to meet the evolving demands of clients.
Let’s say, a rapidly expanding IT services firm takes on a new set of clients but find their current data infrastructure can’t handle the increased load. This can slow response time, create data bottlenecks, cause them to miss deadlines, and, in extreme cases, result in data loss or system failures.
Scalability issues also increase the risk of security vulnerabilities and inaccuracies in reporting. As data volume grows, legacy systems may fail to keep up, creating gaps in data quality and threatening the integrity of critical business insights. Without proper planning, IT services companies could face both financial losses and reputational damage due to their inability to scale effectively.
10. Outdated Data
The timeliness of data is just as important as its accuracy, especially now when the IT industry is evolving at a rapid pace causing the client needs to change as well. Relying on stale data in this scenario can lead to misguided strategies, missed opportunities, and failed initiatives, as it hinders the ability to accurately analyze trends and forecast future demands. All of this ultimately affects the company’s performance and competitiveness in the market.
Outdated data can result from several factors, including insufficient data governance, lack of real-time data monitoring, and failure to establish a culture of regular data updates. For IT service providers, this can manifest in various ways, such as ineffective resource allocation, misinformed customer interactions, and an inability to meet service-level-agreements.
11. Data Silos
Data silos remain a significant barrier to digital transformation. In a recent survey, 81% of IT leaders acknowledged that silos obstruct seamless data flow across their organizations. This fragmentation isolates critical data within different departments or systems, when then can create a host of challenges.
For example, if sales and operations teams use different data sets, they may develop conflicting project scopes or performance metrics. This can lead to inefficient use of resources and misaligned strategies.
When teams cannot access complete and consistent data, it undermines their ability to deliver accurate reporting, strategic insights, and informed decisions. Overcoming these silos is vital for maintaining high data quality and fostering smoother operations.
12. System Fragility
72% of IT leaders report that their current IT infrastructure is overly dependent on interconnected systems, which creates a state of fragility. This dependency increases the likelihood of breakdowns when one part of the system fails, causing ripple effects across the enterprise. For IT services companies, fragile systems make it more difficult to maintain data quality, as system failures often result in inconsistent or incomplete data across platforms. This can slow down business operations, hamper decision-making process, and delay response times.
13. Skill Gaps
With 38% of respondents citing a lack of skill sets within their IT teams, the skills gap emerges as a major roadblock to optimizing data quality. Effective data management requires specialized skills to cleanse, integrate, and govern data. The shortage of these capabilities leads to errors, slow system implementations, and an inability to fully optimize data for strategic initiatives – particularly in IT services companies that are constantly balancing multiple complex systems.
How DataMatch Enterprise Can Resolve Data Quality Challenges for IT Services Companies
DataMatch Enterprise (DME) by Data Ladder is an advanced data management solution specifically designed to address complex data quality issues across various industries. For IT services companies, where data quality directly impacts service deliver, system performance, and client satisfaction, DME offers an excellent suite of features that tackle these challenges head-on. Here are some of its standout capabilities that IT services companies can greatly benefit from:
1. Advanced Data Matching Algorithms
DataMatch Enterprise excels at identifying and merging duplicate records. It uses sophisticated matching algorithms to ensure that organizations maintain a single, accurate version of data. This ensures a single source of truth for business-critical operations. By eliminating duplicate and inconsistent records, IT service providers can deliver accurate reporting and streamline communication across client projects. The ability to rapidly and effectively clean and merge records is especially valuable when handling large-scale, multi-client data environments typical in IT services.
2. Comprehensive Data Profiling
The software provides an advanced data profiling tool that help organizations assess the quality of their data. By identifying discrepancies, missing values, and inconsistencies, DME enables IT service providers to take proactive measures to enhance data integrity. With DME’s data profiling tool, IT services companies can create a unified view of information across departments to enable consistent access to accurate and complete data for reporting, analytics, and decision-making.
3. Standardization of Data Formats
DataMatch Enterprise enables the standardization of data formats and definitions across multiple systems. This ensures that all applications reflect consistent and accurate information, which is crucial for reliable reporting and analytics. Whether you’re integrating new data from clients or working with older systems, DME’s data standardization feature ensures your data remains accurate and in a standardized format. This improves integration and reduces manual intervention. As a result, IT services companies benefit from smoother operations and fewer data entry errors, particularly when managing large-scale migrations or multi-system integrations.
4. Real-Time Data Monitoring
With DME’s real-time monitoring capabilities, organizations can keep a continuous check on their data quality. It provides instant alerts and notifications about anomalies that empower IT leaders to address issues proactively, before they lead to larger disruptions. This proactive approach to data management helps prevent bottlenecks, reduce system downtime, and enhance system resilience by addressing issues as they arise. This safeguards against the risk of business interruptions.
5. Data Enrichment for Better Insights and Reducing Skill Gaps Challenges
DME’s data enrichment capabilities aren’t just limited to identifying missing or incomplete information; they also help append additional relevant data from external sources. This allows IT services companies to develop a better understanding of their data assets and build more comprehensive data sets, which enables better insights into client needs, project requirements, and operational processes, and facilitate decision-making. Additionally, by automating enrichment and data cleansing processes, DME reduces the dependency on highly specialized skill sets, which can mitigate the impact of skill gaps within IT teams.
6. Robust Data Governance Support:
DataMatch Enterprise aids organizations in establishing and maintaining strong data governance frameworks. It helps automate workflows for data validation, auditing, and lineage tracking, provide transparency into data lineage to ensure compliance with industry regulations. This mitigates legal risks as well as strengthens the overall data security framework thereby, reducing vulnerabilities and protecting both the organization and its clients from costly breaches or non-compliance penalties.
7. Seamless Integration with Existing Systems:
Designed to work harmoniously with legacy systems and fragmented IT infrastructures, DataMatch Enterprise simplifies the integration process. This reduces the burden on IT teams by allowing to connect disparate systems, databases, and applications with minimal friction. Whether integrating new client data or consolidating data from various internal sources, DME ensures that data moves smoothly between systems. This reduces manual data handling and enhances operational efficiency.
8. Scalability and Flexibility to Handle Growing Data Demands:
As businesses grow and evolve, so do their data needs, which often leads to performance degradation and bottlenecks. DataMatch Enterprise’s scalable architecture allows IT services companies to manage increasing volumes of data without compromising performance. Whether you’re handling large datasets, accommodating new clients, or expanding into new markets, DME’s flexible solution ensures that IT services companies can adapt their data management processes to changing business environments and ensure sustained success.
In conclusion, DataMatch Enterprise is a powerful solution for IT services companies seeking to overcome data quality challenges. Its comprehensive features address the critical pain points that IT services face in managing complex, high-volume data environments. It not only helps streamline data management but also enable organizations to deliver superior services, enhance operational efficiency, and maintain strong client relationships.
Download a free trial today or book a demo with one of our experts to learn how DataMatch Enterprise can help you resolve the specific data quality challenges that your organization faces.