We're now well into the era of “ big data”. In the subsequent years, the amount of data businesses generate has continued to increase exponentially.
This presents a tremendous opportunity. Companies once had to settle for sales figures and customer surveys. Now, they can draw on detailed analytics covering all aspects of their business.
But as the quantity of data increases, so do the challenges of managing it. Without a coordinated approach to data management, your dreams of data-driven growth may become a nightmare of siloed teams and misguided insights.
When we talk about data management risks, we often focus on security issues — and understandably so. With data protection fines hitting billion-dollar highs, security is at the top of everyone’s mind. But data management isn’t just about avoiding costly data breaches.
In this article, we’ll break down seven key data management risks and show you how to solve them.
Summary of data management risks
Risk 1: Loss of oversight
Risk 2: Lack of access to fresh business data
Risk 3: Friction between teams
Risk 4: Using AI tools that weaken data privacy
Risk 5: Lack of data literacy
Risk 6: Failure to understand customers’ needs and expectations
Risk 7: Manual processes impacting productivity
Risk 1: Loss of oversight
Having access to a wide array of data sources has obvious benefits. Each source represents a different perspective on your business. Combined, they offer a comprehensive picture of your performance.
But the more data sources you have, the harder it is to manage them effectively. Data owners can adopt divergent strategies using incompatible systems. For example, your sales and marketing teams might use different platforms that store data in different formats. They may develop different interpretations of key terms, or define important metrics in conflicting ways.
As a result, your datasets become inconsistent, and gaps emerge in your validation processes. Data quality issues start to multiply, while security falls by the wayside.
Without effective oversight, all of this goes unnoticed — until it leads to a costly error.
Solution: Build a data-driven culture
To ensure your organization can manage data effectively, you’ll need more than just tools and processes — you’ll need an organizational culture that embraces the challenges and benefits of working with data.
By cultivating a data-driven culture, you can ensure that teams across your organization adopt data best practices. This means there’s less chance of data quality issues arising — and makes it easier to spot them when they do.
Why it matters?
Without centralized data governance, organizations lose control over data quality, security, and consistency across the enterprise.
Risk 2: Lack of access to fresh business data
In a fast-paced business environment, responsiveness is key. The quicker you can identify emerging trends, the better your chances of capitalizing on them. By the same token, spotting the first signs of a downturn helps you react before it’s too late.
But this is only possible if you have access to the latest data. If you’re waiting on access requests or wasting time tracking down the data you need, you can’t react to what’s happening as it happens. Your decision-making will be delayed, and your competitors will get a head start.
Solution: Maintain a live data catalog
A live data catalog provides an up-to-date overview of your business data, centrally managed by your IT team. This means you won’t need to spend time searching for the data you need. And because a live catalog syncs data directly from the source every time you request it, you’ll have access to the latest data in a single click.
Why it matters?
In competitive markets, access to real-time data can be the difference between capitalizing on trends and being left behind.
Give business users access to curated, reliable data with CloverDX Data Catalog
Risk 3: Friction between teams
Data silos can emerge for many reasons. In some cases, teams are protective of data they’ve gone to great lengths to gather. In others, incompatible systems make sharing data an arduous process.
Whatever the cause, data silos turn working with data into a daily headache.
Imagine this scenario: every data access request needs chasing twice before you hear anything back. When you do get the data, it’s in a format you can’t interpret. You reach out to IT to help, but they’re trying to manage their own priority workloads. Your deadline is approaching, and there’s no solution in sight.
Instead of helping your teams make better decisions, data has become a trigger for internal tensions. You lose the chance to develop important insights, and you run a bigger risk of data quality issues.
Solution: Enable data self-service
Data self-service allows business users to access the data they need directly. This means you’ll cut out the endless back-and-forth — and avoid those sources of friction.
Why it matters?
Data silos don't just slow down operations — they actively prevent organizations from developing a unified view of their business.
With streamlined processes and fewer bottlenecks, your teams will find it easier to tackle their urgent tasks. A live data catalog is one of the most effective ways to achieve this, as it enables your business users to identify and access the data they need without seeking support.
Risk 4: Using AI tools that weaken data privacy
As organizations adopt AI-powered tools for data management and analytics, a new risk emerges: many AI services require sending your sensitive data to external providers.
This creates serious data privacy and compliance challenges that weren't considerations in the pre-AI era. When you use external AI tools for data transformation, analysis, or processing, your customer information, financial records, and proprietary business data often leave your controlled environment, effectively sharing your sensitive information with other users through model training. This creates multiple problems: potential violations of GDPR's data principles, HIPAA safeguards, and CCPA consumer rights.
Solution: Choose privacy-first AI solutions
- On-premise AI processing: that keeps data secure within your environment
- No third-party model training: using your proprietary or customer data
- Built-in privacy controls: including data masking and anonymization that work seamlessly with AI features
- Complete audit trails: for demonstrating compliance with regulations
- Automated privacy enforcement: that applies your policies to all AI-powered workflows
Why it matters?
This means you can leverage AI to accelerate data management tasks—from transformation to quality checks — while maintaining full compliance with GDPR, HIPAA, CCPA, and other regulations.
Learn how CloverDX maintains data privacy when using AI to transform customer data
Risk 5: Lack of data literacy
How many people in your organization:
- Work with data regularly?
- Understand data best practices?
- Can spot data quality issues?
The lower the number, the bigger your risks. A sharp divide between data specialists and business users won’t just limit your ability to draw insights from your data — it’ll also increase the likelihood of errors making their way into your datasets.
At worst, a lack of data literacy can lead to security issues. An inexperienced employee uploading the wrong file to an vulnerable cloud application can cost your organization dearly. In fact, an astonishing 85% of data breaches are caused by human error.
Solution: Support business users to engage with data
To cultivate data literacy across your organization, non-technical users need to be able to work with data consistently. To support this, explore data transformation tools that use intuitive visual interfaces for interacting with data and don’t require coding knowledge.
This will empower business users to engage with data at greater depth without the need for IT support. With regular exposure to data, they’ll be able to draw valuable insights and spot data issues with confidence.
Why it matters?
With 95% of data breaches caused by human error in 2024, improving data literacy is critical for both security and data quality.
Risk 6: Failure to understand customers’ needs and expectations
Customer data comes in many forms, from product usage data to marketing analytics and survey feedback. Taken together, this data provides a granular view of your customers’ needs. It can identify where you’re delivering a valuable service and point to where you’re falling short.
But this only applies if you can integrate this data into a unified view. With data distributed across different systems or managed by multiple teams, assembling it into a coherent picture of your customers can become a time-consuming task. You may be able to gather a few pieces of the puzzle, but you won’t understand how they all fit together.
Solution: Prioritize data collaboration
Whatever their day-to-day focus, your teams have a shared goal — delivering for your customers. A collaborative approach to data can keep them pulling in the same direction.
This could be as simple as reaching out to colleagues to discuss an emerging trend. Or, it could stretch to a top-down strategic approach involving regular workshops and knowledge sharing. But at its core, it requires the tools to support data self-service, so everyone can get access to the data they need, and so non-technical users can still contribute domain expertise to data pipelines.
If you succeed, you’ll benefit from using your organization’s full range of experience to develop shared insights and tackle complex problems.
Why it matters?
Customer expectations are higher than ever. Without unified customer data, you're making decisions with incomplete information.
Risk 7: Manual processes impacting productivity
The unfortunate truth is that humans struggle with repetitive data tasks. They get tired and lose focus. They rely on reminders to keep them on schedule. They even need the occasional day off.
As a result, manual data processes are slow, difficult to manage and prone to errors. They lead to bloated workflows, data quality issues, and slower time-to-value. And even the most trivial mistakes can have a major cost. Something as simple as a few transposed figures can result in over-ambitious forecasts and misallocated resources.
Solution: Automate key processes
By replacing your repetitive manual tasks with automated alternatives, you can significantly reduce your overall data workload. Your IT team can focus on their key objectives while business users skip the tedious data prep and get on with their analysis. And with automated validation and error handling, you’ll be able to trust your data is accurate.
Why it matters?
Manual data processes waste valuable time and resources while introducing errors that automated systems eliminate.
Moving on from scripts and Excel: Why should you be automating your data pipelines?
Finding the ideal solution for your data management issues
Data is a powerful asset for your organization — but only if you use it effectively.
If your data is growing faster than your processes can accommodate, it can start to feel like more of a burden than a benefit. Data management issues will leave you wrestling with inefficient workflows and siloed teams, while unused data continues to pile up.
CloverDX is a powerful, comprehensive tool for tackling your data management woes. With features designed to keep IT in the driving seat while supporting data self-service for business users, we can help you get the most out of your data.
Book a tailored demo with one of our experts to learn more.
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