As the data privacy landscape continues to expand, businesses rely on data mapping, risk-based search, and robust, automated governance policies to solidify their data privacy capabilities. Unfortunately, data privacy isn’t a single event. We, as a whole, generate 2.5 quintillion bytes of data per day. And, each day, that business data is ETLed & shoved in DBMS, data lakes, and file stores with the hope that it can be leveraged for rich analytics.
Businesses continue to collect more data. Government bodies continue to create more data privacy standards. There’s a looming uncertainty around data privacy, and, unless businesses tackle data privacy holistically, they risk the full weight of these regulatory bodies. Most want to address that immediate need and go out to find a one-time/single-use data privacy solution. They want to quickly invest and quickly mitigate their privacy woes. But it’s not possible.
Here’s the problem: data privacy doesn’t really work that way. It doesn’t work as a single, one-time database query – it’s a complete retooling of your entire approach to data governance. In fact, Data classification and mapping is more like a virus scanner; for it to be successful you need to leverage it at a regular cadence. It’s a continuous discipline and organizations have to absorb the basic principles for privacy by design to be good stewards of sensitive data.
Data Privacy Solutions Aren’t One-and-Done
Mapping, ops, and infrastructure aren’t one-time solutions. You need consistency and regularity. In fact, the reason data mapping is such a powerful solution is that it can be automated for efficiency. Reporting in Excel from data teams or running a mapping solution weekly/monthly isn’t sufficient. Data is coming in too fast.
Every new bit of data is a liability. Even when we’re talking about use-case data (i.e., data that you’re actually using), you have fresh new batches streaming to you every second. Mapping run over time leaves gaps. There’s another side to this story: dark data. This is all of the data you aren’t actually utilizing. Mapping this type of data (especially in unstructured lakes) is incredibly difficult. Typically, it gets left out of the governance architecture when you use manual data mapping processes. Don’t leave it out. Data privacy regulations still apply to this type of data. And this “dark data” accounts for 55% of your data on average.
Data governance has to be ongoing. It doesn’t stop. Instead of focusing on ad-hoc governance and mapping, automate your mapping solution to apply governance and policies at scale. The result is a holistic, comprehensive, and solidified data privacy framework.
The frequency of your scan cadence can be based on:
- The potential risk of data sources
- How fast data is susceptible to touchpoints or changes
- The type of data in each source
- The presence of 3rd party data that might carry sensitive information
You can set up the right internal policies to regulate your scanning frequency. Of course, you can take an aggressive approach and regularly scan all databases to ensure broad compliance.
You Need to Figure Out Your Framework
Data privacy isn’t a feature. It’s a culture. When it comes to data privacy: the stakes are high. Customers are paying attention to how you handle data, regulators are hovering above every piece of data you collect, and people across the globe are ready to boycott, isolate, and ignore your brand based on how you mitigate and handle data breaches. 85% of people will never do business with a brand that’s suffered a data breach.
79% of Americans are concerned about the way businesses are utilizing their data, yet 66% believe that it’s impossible to go through an average day without sharing data. They’re not wrong. Companies collect a massive amount of data daily, and between chunking it in data lakes and analyzing it for top-level systems, the pure magnitude of customer data in our hands is staggering. It’s easy to lose track. But almost every data point represents something, and if data is leaked or exposed it could put people in real danger. Identity theft is on the rise, and it costs the average consumer over $1,000 to resolve — not to mention hours upon hours of labor.
To avoid consumer frictions, stay in the good graces of regulatory agencies, and mitigate your chances of damaging data breaches, your organization should focus on three core preventative practices:
- Data mapping: How does all of the data in your big data systems, structured databases, data lakes, and SaaS apps relate back to your governance policies? If you don’t know, you’re in trouble. Data mapping helps you categorize and catalog your data sources for governance and policy application. There are a few ways this can happen on the back-end. At Integris, we take a granular approach to data governance. Our solution recognizes data at the elemental level, allowing us to dig deeper than identity-based categorization. We highly recommend that you leverage data mapping that holistically tackles data structuring. You don’t want to be left with loose ends when it comes to compliance.
- Operations: Mapping data is the first step. But you still need ops. Organizations need robust data policies and governance frameworks that support privacy and minimized data collection. Ideally, data mapping helps enforce and regulate these policies broadly. Access policies, retention policies, deletion policies, and security policies all fall into the bucket of operations. In addition, GDPR and CCPA require you to fulfill data subject access requests (DSAR). Mapping helps you rally together the key pieces of data necessary for this transaction, and DSAR automation helps you fulfill requests within the specified time-range governed by data privacy standards.
- Infrastructure: You need technology to enable data governance. Secure data lakes, apply hygiene to structured and unstructured data, and leverage solutions that enable data governance automation and policy control. Digital transformation (at least on the data privacy and security layer) is a necessity for today’s hyper-regulated data privacy ecosystem.
Establishing a Regulatory-agnostic Data Privacy Architecture
With thousands of data privacy regulations, evolving compliance needs, and an ever-rising data tsunami on the horizon, businesses need to stop thinking about data privacy as a silo. It’s part of your IT mainframe. Running a few “mapping scans” or applying new policies to a certain pool of data doesn’t cut it. You need to build a regulatory-agnostic framework that supports consistent, holistic, and continuous data mapping, operations, and infrastructure.
We believe that building future-proof data privacy architectures shouldn’t be difficult. Our solution helps automate data mapping at the elemental level — which facilitates speedier governance, more accurate policy control, and smarter data analysis. Are you ready to experience the future of data privacy? Contact us. Let’s create tangible data architectures that go beyond satisfying compliance requirements. Let’s create better customer experiences.