Big data is transformative because of the insights they give entrepreneurs about their businesses. Coupled with artificial intelligence and machine learning software, big data has the potential to deliver better returns on marketing, inform product development, increase efficiency, reduce overstocking and prevent fraud.
But with that opportunity comes risk. Big data attracts hackers so, in response, many businesses’ cybersecurity and risk management policies now include data minimization as a way to defend themselves against an attack.
Below, we explain what data minimization is, how to put it into practice and its benefits.
Tip: Many businesses store sensitive information on the cloud. Before signing up with a provider, make sure they use robust cloud data encryption technology to protect your data.
What is data minimization?
Data minimization means:
- Keeping only the data your business needs to operate and no more
- Using data only for its intended purpose
- Restricting access to that data depending on who’s viewing it
An example of data minimization might be your employee’s bank account numbers. While they work for your company, it’s necessary to keep it stored so you can pay their wages. But when they quit, there is no legitimate reason for retaining the information.
Why is data minimization important? All responsible businesses have procedures in place to protect against data breaches. But cybercriminals are always finetuning their approach to increase their chance of success.
If you are breached, then data minimization reduces the amount of information that they obtain.
Using data management software may make it easier to gather all your data in one place so that you can see just how much of it your business processes you need.
How do you apply data minimization?
If you are considering how to apply data minimization to your business, you need a comprehensive implementation plan that includes four key principles.
1. Narrow data collection
First, you need to determine what data is necessary for your business to operate.
You need to narrow down your data-gathering techniques so that only the most valuable information (however you define that) is collected for analysis. For the data you do keep, it’s critical to control who has access to what. For example, a secretary does not need to see as much data on a customer as a sales rep or someone in customer service. Make sure that each user only has access to the data they need to do their job and no more.
For example, with the best call center software, you can restrict what customer data is shown to different types of users. Our GoTo Contact Center review found that you can customize what data system administrators, supervisors and agents can see by role.
2. User verification and screening
Many bulk data collection workflows function on the assumption that the vast majority of users submit usable, relevant information. In reality, this is not the case. A large proportion of businesses, from startups to multinationals, unintentionally collect large amounts of dangerous data. The data you hold could be fraudulent or unconditioned and thus it generates risk for everyone involved simply by sitting in company servers.
Strong data minimization plans create user verification and screening processes to weed out such data. For instance, a rideshare company with such tools in place could catch an applicant with a violent criminal conviction attempting to submit someone else’s personal details. [Related article: What Is a Criminal Background Check?]
With these initial assessment procedures in place, organizations will gather only usable information from verified sources.
3. Progressive data management
User data eventually goes stale yet many organizations do not take this into account. This results in databases full of unusable or incorrect information. This places a burden on your information technology infrastructure and, if you use the data for business analysis, it will skew the results.
Data minimization plans that include progressive evaluation protocols avoid this as users keep data accurate and up to date. This approach also makes it easier to cultivate databases for reports as they are optimized for actionability. This saves the business time and money in the long run and it better mitigates the risk that comes as the volume of user data grows.
4. Strategic deletion
Strategic data erasure is a core component of data minimization methodology. All user information has a lifespan, especially so in today’s fast-moving digital marketplace. Businesses must consistently purge stale data from their servers to ensure their information retains value and doesn’t pose a security threat. As a result, all data minimization plans should include deletion protocols.
Going forward, decisions on the future direction of a business should always include identifying the new types of data a company needs and the deletion of outdated types of information that no longer serve the organization.
Any time you store data, you are vulnerable to breaches, unverified data and more. There’s no way to eliminate those risks altogether. However, companies that pursue sound data minimization strategies can streamline information collection workflows, gather more valuable data and reduce the risk.
Eighty-two percent of businesses in a survey by data integration firm Fivetran make decisions based on “stale information.” Eighty-five percent of respondents stated that this caused them to lose money and make the wrong decisions.
What are the benefits of data minimization?
Data minimization is a requirement for any business or organization that must comply with the European Union’s General Data Protection Regulation (GDPR). Even if your business doesn’t trade with Europe, the benefits of minimization go well beyond compliance.
Specifically, companies are realizing the following:
- Reduced risk of data loss: In the United States healthcare sector, the average data breach size was 9,871 records, according to the “HIPAA Journal.” The average cost per breached record is $161, reported IBM. Keeping fewer records reduces the chances of a loss and the potential severity of any occurrence.
- More efficient data retrieval and storage: Management of data is simpler when there is less of it. Knowledge workers can spend less time hunting through archives and feel more confident that they are retrieving the most current data when minimization is practiced in a well-disciplined manner.
- Faster responses to requests: It is easier to respond to requests when there is less stored data.
- Enhanced customer approval: Customers prefer to be asked for less personal data and trust companies that provide assurances about what data is stored.
- Preparedness for future regulations: Companies enacting data minimization efforts now will be ahead of the game if regulations similar to GDPR are passed in the future.
Data minimization shouldn’t stop small and medium-sized businesses from embracing big data but you should approach it in such a way that your data, especially sensitive data, is kept as safe as possible.