Build in Trust to Unlock Business Insights

April 19, 2021

by Sean Butler, Director of Product Marketing at Privitar

Data and DataOps are fast becoming the lifeblood of any organization looking to grow and scale. Insights gleaned from datasets like customer demographics and buying habits provide businesses with the information they need to create innovative products and reach consumers where they are.

However, enterprises face a number of roadblocks on the path to effective data analysis – namely, getting access to sensitive data as the risk of compliance failures that could lead to fines, data loss and reputational damage continue to be prevalent. The hurdle that must be cleared to limit risk is access to that data must be provided without compromising privacy and ultimately trust. To overcome these challenges, the first thing businesses must do is understand the data they’re working with (it’s usability, quality and suitability) as well as the risks that may come with it.

Discern the Data’s Discoverability, Quality & Suitability

The first step to ensuring that data remains private so that businesses can use it for business intelligence and analytics purposes is understanding its overall DNA. This includes it’s discoverability (how easy it is to find), quality (if it’s reliable) and suitability (whether or not it’s being used for the right purposes).

To help identify these characteristics, businesses need to ask themselves a set of questions that answer the 5 W’s. These can include queries like:

  • Where is it?
  • What is it?
  • How sensitive is it?
  • Who owns it?
  • When was it created?

Answering “I don’t know” to any of those questions can open the door to unusable or unsafe data being ushered in and a loss of consumer trust.

It sounds like a simple concept, but the data that has been collected must also be relevant to fit the business purpose that it has been collected for. Too often, businesses collect large buckets of all sorts of data, and wade through it, trying to find the information they need. The risk here is that using large buckets of data means more data is exposed in the event of a breach or attack. By spending more time focusing on reliable data sources, you can reduce the amount of data that needs privacy controls attached to it, and cut down on the likelihood of collecting irrelevant data that can’t be used for effective analysis.

Calculate the Data’s Risk

Perhaps the biggest challenge is managing the risk associated with data analytics. Today, risk is ever growing as the current threat landscape widens and the number of laws and regulations that govern acceptable data use rapidly expands.

To start, the level of cyberthreats has been at an all-time high during the global COVID-19 pandemic.  Recognizing an opportunity to prey on already-strained businesses by utilizing fear-mongering tactics, hackers are increasingly trying to disrupt operations by stealing or holding mission-critical data ransom. If that data is stolen and not anonymized or de-identified, it can have massive repercussions. While looking at the makeup of their data, businesses must understand how “attractive” or likely that data is to be targeted by a cyberattack and apply the proper protections to maintain its privacy in the event that it is affected.

Laws like GDPR and CCPA are becoming the norm around the globe, and with that comes the risk of being exposed to fines for noncompliance. Effective data privacy measures must be deployed to keep all sensitive data across an enterprise in lockstep with these laws. Failing to comply with any of these regulations can cause extreme financial harm and come with serious consequences to a brand’s reputation.

Implement Data Privacy

Despite the associated risk, data remains a business’ number one asset for creating meaningful experiences that keep customers happy and coming back for more. A wide field of risk needs to be met with a wide field of practices and tools to handle that risk. This is why DataOps, built on a foundation of trust, is so critical for organizations today. Using practices like de-identification, masking, and other privacy techniques in your DataOps pipeline can allow datasets to be accessed more broadly with less risk. This allows organizations to get the analyses their business really needs – like narrowing target markets all the way down a granular level to reach customers with the most appropriate messaging and products possible – without compromising their privacy.

The ever-changing threat landscape and accelerated digital transformation efforts forced by the pandemic have put effective data analysis in the spotlight. In a time where communication is already challenged by things like social distancing and stay-at-home mandates, reaching audiences and consumers has never mattered more. Consumers simply are not going to give an organization their business if it can’t reach them with relevant messaging. The only way to successfully do that is by ensuring that the data being used remains private so their organization remains trusted.

Want to learn how we keep data private? Check out our Guide to Data De-Identification here.

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