Collecting data can provide corporations with worthwhile information to enhance processes, reduce costs, improve customer support, and more. But it also comes with risks: any organization that collects data, no matter size, is a goal for hackers. In addition, latest and evolving government regulations, including the EU’s General Data Protection Regulation and the California Consumer Privacy Act, impose latest responsibilities and restrictions on corporations’ collection and use of information.
Organizations across all industries are exploring data minimization initiatives that focus not only on reducing the quantity of information they have already got, but additionally on collecting less latest data in the longer term. Below, 20 members of Forbes Technology Council share practical, effective data minimization strategies that could be utilized by corporations across a wide range of industries and share success stories they’ve experienced themselves.
1. Create a knowledge mapping
To minimize the quantity of information you store, discover where critical and sensitive information is stored. An information map showing storage locations and security patterns will help resolve what to maintain and manage in a retention plan. Observing a corporation creating overlapping data management plans for multiple regulators highlighted the importance of cross-planning for regulatory compliance and future training. – Kathleen Hurley, Sage, Inc.
2. Introduction of Data Mesh and Data Fabric architectures
We can address this problem with two latest modern data architecture paradigms: Data Mesh and Data Fabric. Data Mesh provides decentralized data management in order that only the obligatory data is collected, processed and stored from each domain. Data Fabric provides unified data access and management to scale back the necessity for multiple data copies, thus minimizing data duplication and storage overhead. – Suri Nuthalapati, Cloudera
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3. Reduce non-essential information
We helped a retail store clean up its customer data by keeping only the essential information resembling names and speak to details while ensuring every part was securely protected. This not only saved the shop money on data storage but additionally increased the safety of shoppers’ personal information. By adhering to strict data usage rules, we improved customer support without compromising privacy. – Balasubramani Murugesan, Number7
4. Keep regulatory requirements in mind
With the introduction of GDPR, I even have seen the quantity of information in retail banking increase significantly. Working with data protection officers, we’ve launched several simplification initiatives to strike a balance between meeting legal requirements and retaining obligatory data. We have been very precise in our classifications and have been careful to differentiate which forms of data must be retained for a set time period (e.g. 10 years) somewhat than simply retaining all information collected. – Mr Luboslava, Solvd Group
5. Use causality evaluation
By using causality evaluation in our AI work, we’ve been in a position to significantly reduce the quantity of information required to develop intelligent recommendations for a variety of selections facing a Fortune 100 company. Even with less data, we will gain useful and actionable insights into manufacturing operations, potential market opportunities, optimizing customer support, and improving company culture. – Pravir Malik, QIQuantum
6. Choose a multi-stage approach
I worked with a financial services company to scale back data overload. The idea is to attenuate the gathering, storage and processing of sensitive data to scale back the chance of information breaches and improve data management efficiency. We classified data, eliminated unnecessary collection points, enforced strict collection and retention policies and applied data masking and anonymization, leading to a 40% reduction in stored data. – Dutt Kalluri, Advanced technologies
7. Reduce the quantity of non-public data collected
As a part of our data minimization strategy, we’ve significantly reduced the quantity of non-public data we collect. This has not only improved user privacy, but additionally improved data security, leading to incidents being virtually eliminated. The strategy has also increased customer trust, is linked to greater regulatory compliance, and has improved overall customer satisfaction. – Michael Beygelman, Claro Analytics
8. Implement schema-based data generation
By implementing schema-based (structured) data generation from end-user devices, we’ve significantly reduced the computational power required for processing and minimized the quantity of derived data that have to be stored before data engineers and scientists could be supplied with data gold. This approach has reduced our storage costs by 40%, improved our GDPR and CCPA compliance, and increased customer trust. – Ravi Bandlamudi, DUNGEON
9. Eliminate duplicate and outdated data
Data cleansing is critical for corporations looking to attenuate the quantity of information stored. One life sciences company we worked with had gathered various legacy systems. They needed to consolidate all their data right into a single SAP ECC instance. We helped them eliminate duplicate data and take away outdated and unnecessary data before migrating to the brand new, unified system. – Kevin Campbell, Syniti
10. Implement a time-limited retention and deletion policy
Internally, we recently implemented a seven-year data retention and purge policy. We had experienced staff with files dating back to the 2000s! Our client project folders were just as old. By retaining the old data, the chance of getting to report breaches was 300% higher than it’s today. Users were reluctant at first, but with some change management principles, we were in a position to delete all but some HR and financial data without complaints. – Chris Stegh, Enabling technologies
11. Replace sensitive elements with tokens
Data plays a vital role in digital promoting. The most vital thing is to gather only the information you would like. At the identical time, I might recommend corporations to interchange sensitive elements with tokens. Since tokens can’t be by accident decrypted, the system is safer, which is helpful for each an organization and its customers. – Roman Wrubliwskyj, SmartHub
12. Delete fields in registration forms
We decided to remove some fields from certain intake forms and lead lists to extend productivity. The result was lead lists that were easier and faster to investigate and improved production overall. Sometimes easy is best. – Michael Gargiulo, VPN.com
13. Check, anonymize and automate
At a financial firm, I led a knowledge minimization initiative that audited and anonymized data and automatic data cleansing. This increased data security, reduced storage costs, improved compliance and customer trust, and increased operational efficiency. The initiative protected the corporate from security threats and compliance issues and benefited each the corporate and its customers. – Sumit Bhatnagar, JPMorgan Chase
14. Remember that quality is more essential than quantity
AI requires huge amounts of information, but as all the time, quality is more essential than quantity. By taking a tough take a look at data quality, one computer vision company managed to scale back the variety of images needed to coach models by about 16%. This was achieved by removing similar and low-quality images and improving annotation quality. The result was not only higher performance, but additionally cheaper and faster AI training. – Erik Aasberg, eSmart systems
15. Use ML to filter out unnecessary data
We conducted regular data audits, revised our collection policies, and implemented machine learning algorithms to filter out unnecessary data. These efforts resulted in a 25% improvement in system performance, improved data security, increased customer satisfaction, and a 20% reduction in data management costs. – Ketan Anand, Suuchi
16. Break down departmental silos
I worked with a government that desired to consolidate spending across all departments right into a centrally managed procurement dashboard. The problem was data overload: each department managed its own data and there was no records management. By breaking down these silos so that every one data might be analyzed in a single place, the federal government saved over $1 million in the primary three months. – Lewis Wynne Jones, ThinkData works
17. Promote a culture of privacy and responsibility
An information minimization initiative should include promoting a culture of privacy and responsibility inside the organization. Employees have to change into more aware of the importance of information protection and their role in protecting confidential information, as it will lead to raised data handling across all departments. Automation tools can streamline compliance checks and data management processes. – Roman Reznikov, Intellias
18. Consider data as a “toxic asset”
Our policy is to treat data as a “toxic asset,” which is a helpful concept to bear in mind when handling data. Just like a toxic substance, we try to attenuate the handling of information, limit the number of people that come into contact with it, reduce the retention period, and reduce the burden. – M.Nash, integrity
19. Focus on collecting data just for specific, defined purposes
We have benefited most from streamlining the user analytics process. We have revised our data collection practices and implemented a method of collecting only the essential data points needed for performance evaluation and user experience improvements. This has reduced the quantity of information stored and improved security, regulatory compliance and customer trust. – Phil Portman, Text drip
20. Implement edge devices to attenuate data sent to the cloud
Manufacturing generates 18 PB of information annually. We have implemented edge devices to normalize, contextualize and detect anomalies and send only useful data to the cloud. This reduces costs and avoids overprocessing. For example, when anomalies are detected in images or videos, only chosen files are sent for further evaluation and model training. This has also significantly optimized data processing and value efficiency. – Ravi Soni, Amazon Web Services
