gettyData may be “the new oil,” but as human experience has shown time and again, there can be too much of a good thing. The essential insights and information quality data provides can be diluted or even distorted when a company is drowning in disorganized, irrelevant or duplicated data.
From better decisions to lower operational costs, there are compelling reasons for every organization to keep a close watch on the volume and quality of data it collects and stores. Below, members of Forbes Technology Council share their expert tips to help company leaders identify—and, more importantly, address—data overload.
1. Implement A ‘Data Hygiene’ Strategy
It’s clear a company is overwhelmed by data when decision making slows down because teams spend more time sifting through information than deriving insights. To correct this, leaders should implement a “data hygiene” strategy: Prioritize key metrics aligned with business goals, regularly audit data for relevance and eliminate redundant or low-value data. This keeps your focus sharp and your processes efficient. – Andres Zunino, ZirconTech
2. Consider A Centralized Data Team
All data is helpful and valuable, but you know you’re having a hard time managing data when you can’t make sense of it. This could be due to a combination of disparate tools and teams that own different parts of it. We talk a lot about a centralized data lake, but little about a centralized data team that owns all the required pieces. – Rajaram Srinivasan, Unbound Security
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3. Avoid Superfluous Metrics
To paraphrase Goodhart’s law, “When a metric becomes a target, it stops being a metric.” A company that collects too much data falls into the trap of defining superfluous metrics and measures, with teams spending time reviewing them rather than focusing on transformative impact. This is when you know you’re trying to manage too much data. – Harini Gopalakrishnan, Snowflake
4. Enforce Data Minimization
A sign of excessive data collection is when a company gathers data without having a rationale for its collection or use. To address this, a company can enforce data minimization, which ensures data is only collected for a specific purpose. Conduct regular brainstorming sessions to assess whether the data elements being collected are still relevant or if unneeded data can be deleted. – Aparna Achanta, IBM
5. Make Sure The Value Of Collected Data Is Understood
When the value derived from data is significantly smaller than the resources, both human and technological, spent on its collection and management, it raises questions. If the value of the collected data is not understood, it puts the entire data program at risk of budget cuts. – Michael Nirschl, Morgan Stanley
6. Connect Data Elements To Meaningful Analytics
A quick test of whether you are managing too much data (which is different from collecting too much) is to look at the managed dataset and see if you can legitimately connect those data elements to meaningful analytics that are in use by the organization’s operations, management and governance teams. If there isn’t a clear connection, you are likely managing way too much data—potentially 10 times too much data. – Terrance Berland, Unicorn & Lion LLC
7. Prioritize Metrics Aligned With Business Goals
A clear sign a company is overwhelmed by data is when decision making slows due to analysis paralysis, with teams spending excessive time sorting through data instead of acting. Leaders can correct this by prioritizing key metrics aligned with business goals, simplifying data streams and fostering a culture focused on actionable insights over sheer data volume. – Marius Ivanauskas, Zurich Insurance
8. Watch For Signs Of ‘Data Fatigue Syndrome’
You know there’s a problem when “data fatigue syndrome” sets in—when staff become numb and unresponsive to the constant barrage of metrics, reports and analytics. Some of the signs? Meetings are eerily quiet when data is presented, with few questions or engagement. Data-driven initiatives consistently underperform despite robust analytics. Staff shows a near-allergic reaction to numbers, glazing over at the mention of KPIs. – Nick Newsom, Ytel Communications
9. Take A Value-Over-Volume Approach
A company that struggles to extract actionable insights from data may be focused on data volume rather than its quality or relevance. Instead, prioritize high-value data that directly impacts business objectives and customer outcomes. A value-over-volume approach enables faster, more informed decisions; reduces the burden of managing excessive and redundant data; and drives meaningful outcomes. – Michael Meucci, Arcadia
10. Set Clear Retention Rules, Tag Information And Automate Cleanup
If your company is overwhelmed by data, you’ll notice “storage inefficiency”: Too much irrelevant data piles up, making it hard to find what’s important. To fix this, focus on collecting only valuable data, set clear retention rules, categorize and tag information for easy access, and automate data cleanup to keep things efficient and manageable. – Phil Portman, Textdrip
11. Integrate Advanced Identity And Access Management
When data access becomes unwieldy and error-prone, indicating overcollection, consider integrating advanced identity and access management solutions. These tools employ AI and digital twins to analyze data usage patterns and streamline access controls so that only relevant data is actively managed and accessible to authorized users. This enhances security by minimizing unnecessary data exposure. – Craig Davies, Gathid
12. Identify Priority Data
The question is not how much is too much data, but what data is important to facilitate a business’s existence and growth, and how does that data align with corporate and customer KPIs or compliance requirements? Once companies identify their priority data, they can make data-driven decisions for data storage requirements. – David Bennett, Object First
13. Conduct An Audit And Revisit Your Retention Policy
One sign a company is collecting and trying to manage too much data is when its storage costs go high while its value remains low. To address the issue, conduct a data audit to make sure you’re going for quality, not quantity. Plus, revisit your data retention policy and potentially revise it to further optimize data management and storage costs. – Yuriy Gnatyuk, Kindgeek
14. Optimize AI Deployments
Customizing and fine-tuning AI models for specialized tasks is a data-intensive exercise. Gartner notes that, in 2023, companies deploying AI spent between $300,000 and $2.9 million just on inference, grounding and data integration. If companies are not optimizing their AI deployments for a careful balance between accuracy, speed and cost, their data center budgets will quickly spiral out of control. – Vivek Jetley, EXL
15. Launch A ‘90-Day Data Challenge’
If your database backup windows are creeping from hours into days while query response times keep slowing down, you’re hoarding too much data. Leaders should launch a “90-day data challenge”—temporarily archive any dataset not accessed in the past 90 days, then only restore what teams actively request. After 30 days, permanently delete unrequested archives. – Balaji Dhamodharan, NXP Semiconductors
16. Categorize Collected Data
If a company has one-size-fits-all data retention policies, that’s a clear sign it’s collecting and trying to manage too much data. I suggest clearly categorizing each type of data you’re collecting and defining a retention period for each type. Some data can be deleted in two weeks, while some needs to be kept around for years, and your policy should reflect that. – Deepak Bhaskaran, Cisco Systems Inc.
17. Automate Data Collection, Analysis And Management
Eliminate unnecessary manual processes by automating data collection, analysis and management. For instance, banks and financial services firms by nature analyze massive amounts of data. Onboarding and transactions can be slow and cumbersome, which can lead to a poor customer experience. Automation improves the quality and speed of digital client journeys and enhances overall security and compliance. – Alex Ford, Encompass Corporation
18. Leverage Data Management Technologies
One sign a company is collecting too much data is a frequent need to increase storage capacity. It is very easy for IT staff to just keep buying more storage, but there are more sophisticated methods available—for example, technologies like thin provisioning, deduplication and data thinning (automatically deleting data that is not needed). There is so much data being stored that will never be used again. – Bruce Kornfeld, StorMagic
19. Conduct A Full Data Analysis
Keep an eye out for data sprawl across different systems—databases, cloud file services and so on—without data classification. Data becomes hard to secure, leading to duplications or, even worse, people (and AI training models) processing outdated documents. The answer is a full data analysis. Build data governance policies and utilize advanced data management tools to streamline storage and maintain data integrity. – Carl D’Halluin, Datadobi
20. Simulate A Data Breach
A simulated data breach often shows that a company is collecting too much data. If the results come in and everyone wonders where certain data came from and how it is stored, it’s time to dig deeper into your collection and storage practices. The first step in the process should be listing all the data silos within the company to gain an overview of what’s there. – Kevin Korte, Univention
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