Advisory Article: Strategies to Discern and Address Info Glut: Professional Insights
Data might be referred to as the "new gold", but history has shown us that too much of a beneficial thing can lead to its degradation or misrepresentation. Essential insights and the quality of information that data offers can become diluted or skewed when a company is swamped by disorganized, irrelevant, or duplicate data.
There are several compelling reasons that justify every organization's need to closely monitor the volume and quality of data they gather and store. Here, members of Our Website Technology Council share their expert advice to help company leaders identify – and most importantly, address – data overload.
1. Enact a 'Data Cleanliness' Strategy
When a company is swamped by data, decisive actions slow down due to teams spending more time sorting through information than deriving insights. To address this, leaders should implement a “data cleanliness” strategy: Prioritize essential metrics that align with business goals, frequently review data for relevance, and eliminate redundant or low-value data. This maintains a sharp focus and efficient processes. - Andres Zunino, ZirconTech
2. Contemplate a Centralized Data Department
All data may be valuable, but excessive difficulty in managing data can emerge from using disparate tools and teams responsible for different parts of the data. We often talk about centralized data lakes, but we rarely discuss the importance of a centralized data department that manages all the necessary components. - Rajaram Srinivasan, Unbound Security
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3. Dodge Superfluous Metrics
To rephrase Goodhart's law, “Once a metric becomes a target, it ceases to be a metric.” A company that collects an excessive amount of data falls into the trap of creating superfluous metrics and measurements, with teams squandering time on them instead of focusing on transformative effects. This signals that a company has too much data to manage. - Harini Gopalakrishnan, Snowflake
4. Implement Data Minimization Policy
Collecting excess data is a symptom of a company amassing data for unjustified reasons or purposes. To counter this, a company can institute data minimization, ensuring data is only accumulated for specific purposes. Regularly evaluate whether the data elements being collected are still relevant or if unnecessary data can be deleted. - Aparna Achanta, IBM
5. Grasp the Value of Collected Data
When the value derived from data is significantly less than the resources, both human and technological, expended on its collection and management, it raises concerns. If a company does not comprehend the value of collected data, its entire data program becomes vulnerable to budget cuts. - Michael Nirschl, Morgan Stanley
6. Establish a Connection between Data Elements and Meaningful Analytics
A quick test to determine if a company cumbersomes its data (not to be confused with collecting excess data) is to analyze its managed dataset and determine if the data elements can be correctly linked to meaningful analytics used by the organization's operational, management, and governance teams. If such a connection cannot be recognized, the company likely manages an excessive amount of data—potentially ten times the necessary amount. - Terrance Berland, Unicorn & Lion LLC
7. Prioritize Metrics Aligned with Business Goals
A clear sign that a company is overwhelmed by data is decision-making becoming obstructed due to analysis paralysis, with teams taking too much time to sort through data instead of acting. Leaders can rectify this by prioritizing essential metrics aligned with business goals, simplifying data flows, and fostering a culture that emphasizes actionable insights over sheer data volume. - Marius Ivanauskas, Zurich Insurance
8. Watch for Symptoms of ‘Data Exhaustion Syndrome’
You'll recognize a problem when symptoms of “data exhaustion syndrome” appear—when employees grow desensitized and disengaged due to the onslaught of metrics, reports, and analytics. Some telltale signs include: quiet meetings when data is presented, few queries or participation, underperforming data-driven initiatives despit robust analytics, and employee aversion to KPIs. - Nick Newsom, Ytel Communications
9. Adopt a 'Value over Volume' Approach
A company that struggles to extract actionable insights from data may be fixated on its volume rather than quality or relevance. Instead, emphasize high-value data that directly impacts business objectives and customer outcomes. Adopting a 'value over volume' approach enables faster, more informed decisions; decreases the burden of managing redundant data; and leads to meaningful outcomes. - Michael Meucci, Arcadia
10. Set Defined Retention Rules, Classify Data, and Automate Cleanup
If a company is inundated by data, it will show "storage inefficiency": excessive irrelevant data accumulates, making it challenging to distinguish vital information. To resolve this, focus on gathering only essential data, set clear retention rules, organize, and tag data for easy retrieval, and automate data cleanup to enhance efficiency and manageability. - Phil Portman, Textdrip
11. Integrate Advanced Identity and Access Management
When data access becomes impractical and prone to errors, implying overcollection, consider integrating advanced identity and access management tools. These tools employ AI and digital twins to scrutinize data usage patterns and streamline access controls so that only relevant data is actively managed and accessible to authorized users. This boosts security by minimizing unnecessary data exposure. - Craig Davies, Gathid
12. Determine Crucial Data for Business Operations
The quantification of excessive data is not the main issue, but rather identifying the data that significantly impacts a business's existence and expansion, and aligning it with corporate and customer key performance indicators or regulatory requirements. Once companies pinpoint their critical data, they can base data storage decisions on factual insights. - David Bennett, Object First
13. Perform a Data Review and Revise Retention Policy
A company's notification that it is accumulating and managing an excessive amount of data is evident when storage costs escalate while the data's value remains minimal. To tackle this issue, execute a data review to ensure that you prioritize quality over quantity. Additionally, reevaluate your data retention policy and consider amending it for optimal data management and cost reduction. - Yuriy Gnatyuk, Kindgeek
14. Enhance AI Deployment Efficiency
The optimization of AI deployments for specialized tasks is a data-heavy process. Gartner highlights that companies deploying AI in 2023 spent between $300,000 and $2.9 million specifically on inference, normalization, and data integration. If organizations fail to optimize their AI deployments for a harmonious balance between precision, speed, and cost, their datacenter budgets will swiftly escalate. - Vivek Jetley, EXL
15. Launch a '90-Day Data Assessment'
If your database backup durations expand from hours to days while query response times decelerate, you're storing an excessive amount of data. Leaders should instigate a '90-day data assessment'—temporarily archive any unutilized dataset from the past 90 days, just restore the datasets that active teams request. After 30 days, erase the unrequested archives permanently. - Balaji Dhamodharan, NXP Semiconductors
16. Organize Collected Data
If a company adopts an all-encompassing data retention policy, it could possibly be hoarding and managing an excessive amount of data. I suggest separating each type of collected data and defining a retention period for each category. Some data can be deleted within two weeks, while others need to be preserved for years, and your policy should reflect that. - Deepak Bhaskaran, Cisco Systems Inc.
17. Automate Data Collection, Analysis, and Administration
Eliminate redundant manual processes by automating data collection, analysis, and administration. For instance, financial institutions are required to analyze vast amounts of data. Manual procedures in onboarding and transactions can be time-consuming, deteriorating the customer experience. Automation enhances the quality and swiftness of digital client journeys, increasing overall security and regulatory compliance. - Alex Ford, Encompass Corporation
18. Utilize Data Management Technologies
Indications of a company accumulating excessive data include the frequent necessity to expand storage capacity. IT staff may easily opt for additional storage, but more cutting-edge solutions—like thin provisioning, data deduplication, and data thinning (automatically discarding unnecessary data)—are available. There is an abundance of data that will never be reused. - Bruce Kornfeld, StorMagic
19. Carry Out a Comprehensive Data Assessment
Monitor for the proliferation of data across diverse systems—databases, cloud file services, and so on—without data classification. Data becomes challenging to safeguard, resulting in duplicates or, even more detrimental, outdated documents being processed by individuals and AI training models. The solution is a comprehensive data assessment. Develop data governance policies and leverage advanced data management tools to optimize storage and preserve data integrity. - Carl D’Halluin, Datadobi
20. Simulate a Data Breach Scenario
Performing a simulated data breach demonstrates that a company may be accumulating excessive data. If the results expose doubt over the origin and storage of certain data, revise your collection and storage protocols. The preliminary phase in the process should involve cataloging all the data silos within the organization to grasp the overall landscape. - Kevin Korte, Univention
- To effectively manage data and prevent data overload, companies should consider seeking advice from the esteemed members of the Forbes Technology Council.
- In order to implement a comprehensive data management strategy, consider establishing a centralized data department, as suggested by Rajaram Srinivasan from Unbound Security, a member of the Forbes Technology Council.