The 21st century is often touted as the age of information, in which data is gold. There’s truth to that statement, considering that data has become an important asset to nearly any business. However, data also poses a significant challenge for IT teams and departments, especially when it comes to data overloads.
The explosion of data sources, coupled with the growing number of departments dependent on said data, results in a flood of information that’s often fragmented, at best. What might’ve been manageable has now turned into a complex web of fragmented systems, uncoordinated data requests, and reactive management rather than a proactive approach.
But the data overload is often just the most visible symptom of a broader issue, and it’s far from being the only problem IT teams face. The truth is that businesses have been drowning in data since the big data boom of the early 2010s, and IT teams and departments are being dragged to the bottom of the chasm trying to untangle the mess caused by unstructured and fragmented data systems.
So, in this guide, we’ll go over why IT teams and departments are drowning in data and, more importantly, how they can reclaim control through strategic data management. We’ll examine the common pain points, go over the “drowning effect” and its causes, and outline practical steps that can help IT teams shift from a reactive approach to proactive data governance.
What Is Data Overload and Why Does It Matter
Data overload happens when the sheer volume of data collected or generated by a business or an organization becomes too much to process effectively, making it difficult to extract useful insight and make data-based, informed decisions.
Sorting through excessive data consumes valuable time and resources, leading to reduced productivity across an entire organization. This can further hinder effective decision-making and problem-solving, especially since data overload often results in analysis paralysis—a situation which occurs when your analysts spend hours analyzing the data, without reaching any meaningful conclusions.
Without these meaningful conclusions and insights, analysts cannot predict trends, and decision-makers cannot take decisive action, which results in missed opportunities and reduced operational efficiencies. Lastly, data overload in IT can negatively impact the mental health of your IT department members, which significantly reduces their efficiency and leads to burnout.
Common Pain Points
As mentioned at the beginning of our discussion, data overload is often the most visible symptom of a broader issue. The lack of a solid and sound data strategy often leads to poor data management practices, resulting in data overload in IT, the formation of data silos, and a complete lack of valuable insight that would otherwise lead to optimization and growth.
This is a major issue for IT because multiple departments independently bring their own analysts who end up building their own reporting environments. This often happens without IT’s knowledge and leads to the formation of what’s referred to as “shadow IT.”
Shadow IT refers to the tools and databases used for business purposes that aren’t managed by a particular company’s IT security protocols and policies. The most basic example of this would be an employee using his own personal cloud space to store work files. While this isn’t necessarily bad, it carries significant security risks, especially in terms of data protection.
Shadow IT also spawns isolated databases, duplicate reports, and inconsistent data definitions. As many organizations lack system-wide data governance frameworks, shadow IT will keep your company’s IT department struggling to keep track of all the requests coming in, leading to redundancy and waste.
Think of it like this: stacking more bricks on a weak foundation will inevitably cause the house to fall down. Thus, without a solid data foundation (strategy-defined data management), your analysts will struggle to produce accurate, actionable insights simply because the underlying data is either fragmented, corrupted, or both.
As a result, your IT teams will constantly react to urgent requests rather than plan strategically, and their bandwidth will be consumed by fixing issues caused by overlapping systems.
Here are some additional pain points for poor data management
- Broken or Immature Data Infrastructure
- Ad-hoc reports with inconsistent definitions
- Reactive firefighting instead of proactive reporting
- Compliance risks due to poor governance
These pain points perfectly summarize why IT teams are overwhelmed by data and struggling under data overload. Team members are often scrambling to generate reports because an increasing number of people want access to data for insight. The upper level wants their insights, but due to poor-quality data at large volumes, analyst teams are not able to provide these reports.
A Working Example
Data overload affects companies of all sizes, from small businesses to large corporations. It even affects public transportation. One of the reasons why data overload might happen is because the company’s internal team is generating more data requests than the company’s IT department could handle.
And with those same internal teams independently creating reporting databases, the IT staff faces a flood of access requests, data extractions, and troubleshooting demands. More often than not, IT departments aren’t even fully aware of all the data sources in use or whether that data was duplicated across different departments.
This lack of a unified data architecture makes the whole situation more challenging, as the lack of visibility and clear structure led to inefficiencies, delayed fulfillment of data requests, and frustration across the entire company.
These issues compound, and the snowball effect quickly reveals systemic issues and uncoordinated data growth that simply overwhelms IT teams and creates a cycle of reactive work that hinders operational efficiencies and innovation.
What Causes the Drowning Effect
Most businesses nowadays collect and generate vast amounts of data, but only a fraction of that data is actually useful for meaningful decision-making. The “drowning effect” in IT, however, isn’t caused by the sheer volume of collected and generated data alone. There are several structural factors that also contribute to data overload in IT.
We already mentioned that the lack of a solid and sound data strategy leads to poor data handling. Data strategies outline different standardizations within an organization, which govern how that organization handles data. Without that standardization in pace, the data architecture becomes chaotic and evolves in a piecemeal fashion. Organizations, particularly big ones, often juggle new solutions (like cloud computing) with legacy systems, leading to a fragmented architecture that complicates further integrations and compromises data quality and access.
Organizations, particularly big ones, often juggle new solutions (like cloud computing) with legacy systems, leading to a fragmented architecture that complicates further integrations and compromises data quality and access.
Fragmented architectures, like balancing cloud migrations and on-prem legacy systems, further exacerbate data fragmentation. In an attempt to have their data needs quickly fulfilled, many business units (different departments) will try to bypass IT, leading to the formation of independent databases and shadow practices, and the subsequent data overload in IT.
These independent efforts often lead to duplicated and inconsistent data, inconsistent security practices, and uncontrolled data growth. These factors often force IT teams into a reactive mode and overwhelm them with the volume of growing data, its complexity, and the growing number of requests.
The repercussions of the “drowning effect,” in which your teams are drowning in data while thirsty for insight, are lost revenue, missed opportunities, and overall inefficiency. Teams drowning in data tend to spend hours manually pulling reports, cross-checking data, and fixing errors instead of focusing on high-value tasks.
The Way Forward: Strategic Data Management
We previously mentioned that stacking bricks on a loose foundation would make the house topple, and the same applies to data and data architectures. Building a strong foundation lies in developing a good data strategy and data management in IT, which aligns data use, people, processes, and the tech surrounding data with the business goals of your organization. Here’s how to do it:
1. Create a Good Data Strategy
A data strategy is a long-term plan that defines the technology, processes, people, and rules required to manage a business or organization’s data assets. It basically outlines the “what” and “why” your organization collects, processes, and uses data while simultaneously aligning with your business strategy.
2. Create a Data Inventory
The first step is to conduct a full data inventory and understand why particular data exists, where it lives, and who owns it. This is a crucial step towards eliminating data duplication and filling in the knowledge gaps.
n fact, you can use automated data validation to remove duplicates, address inconsistencies, and add missing values before that data is used in reporting. Your data inventory forms the foundation upon which you can build further and make informed decisions about data and data governance.
3. Central Catalog and Ownership Matrix
Once you’ve established a data inventory, it’s important to create a central data catalog and ownership matrix. This establishes transparency and accountability within an organization, as it makes the data’s location, status, and stewardship known to all members of said organization.
Creating a central data catalog and ownership matrix also makes the data easier to manage and access, ensures data quality, and reduces redundant work.
4. Align IT and Business
Besides fragmented data, one of the biggest agility killers across all industries is fragmented teams, which inevitably leads to operational inefficiencies. That’s why you should establish good data governance that involves both the IT department and business stakeholders, as it ensures that data policies, quality standards, and security requirements align with business plans and growth.
It’s also a good idea to provide training associated with data tool use and develop a data-driven culture where teams actively work on ensuring better compliance and reducing errors, with the aim of promoting data-driven, strategic decision-making.
5. Build for Scale
You should also strive to build for scale. Building scalable on-prem solutions isn’t very cost-effective, as the need for data changes over time. Thus, the best option is to build a cloud-native data warehouse with full observability. Cloud platforms often support real-time monitoring of data flow, use patterns, and performance metrics.
Most importantly, unlike on-prem hardware solutions, cloud-based solutions are incredibly scalable, almost instantly, because you’re only a few clicks away from expanding your bandwidth.
How Data-Sleek Helps
Most IT teams that are already drowning in data prefer working with outside help to alleviate the issues, with strategic partners handling the data while the IT team troubleshoots various daily issues. Data-Sleek specializes in helping organizations and businesses transform their decentralized data environments and turn them into streamlined, managed data systems.
Data-Sleek will collaborate closely with both IT teams and business leaders to design data governance frameworks tailored to your organization and its needs. So, if you’re struggling with uncoordinated data requests and decentralized data environments, Data-Sleek offers tools to help your IT team regain control of your organization’s data and unlock its full potential.
If you want to learn more, don’t hesitate to contact us via our website and book a free consultation with our data expert.
