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Is Your Data Safe? How to Assess Your Data Risk - Part 1

Christophe Toum
Talend

Data is one of a company's most valuable assets — but Talend's recent Data Health Survey found only 40% of executives always trust the data they work with.

Today, we have codes and inspections for physical infrastructure, satisfaction surveys for employees, and up-time monitors and stability tests for websites. But are we doing everything we can to understand the degree to which our data is exposed to risk?

There's more to security than protecting yourself from hackers. On one end of the spectrum, you have those big exposures to governmental regulations and security breaches that can shake an entire organization. But even small things — like a little bit of bad data entering the system — can cause a trickle down effect that impacts every department.

We could all be doing a better job of assessing (and mitigating) risk to our data. The key is to start small: just make sure that you have the right data in the right place.

Then you want to make sure that the right people have access to the data and the wrong people don't have access to the data.

Once you have that covered, and you've defined processes for keeping your data clean and standardized, then you can start focusing on making that a daily practice. All it takes is the right combination of people, processes and technology.

What Do We Mean by "Risk?"

When most people think about the risks associated with data, they immediately recall the headline-grabbing data breaches that seem to flood our news feeds with alarming regularity.

But it doesn't take an epic leak affecting millions of users to have serious consequences for most companies. Even a handful of exposed records could have serious legal, financial and reputational repercussions. Fines for GDPR violations alone can run in the millions of dollars, to say nothing of the incalculable cost of losing consumer trust in an increasingly connected and competitive marketplace.

How do these breaches happen?

It can be something as simple as the right data in the wrong place. So much of our conversation about security centers around personally identifiable information (PII). If PII data isn't identified or isn't in the right field — for example, payment information erroneously mapped to an unprotected field and viewed by unauthorized individuals — you could be at risk of exposing some very sensitive information.

But external risks aren't the only dangers we should be worried about. A few years ago, IBM famously calculated that bad data costs US businesses over$3 trillion per year. This is death by a thousand cuts, parceled out in seconds, minutes and hours lost to manual data correction, re-running suspect reports and pursuing strategies and programs that were originally scoped based on data that was later revealed to be faulty.

Of course, the volumes of data we must deal with has grown by over 400% since IBM released that study — and it's only growing.

So how much could we be losing today?

And how much do we stand to lose over the coming years?

Taking all these dangers into account, one thing is clear: no company can afford to expose its data to risk.

Go to: Is Your Data Safe? How to Assess Your Data Risk - Part 2

Christophe Toum is Senior Director of Product Management at Talend

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For decades, trust in the digital workplace rested on familiar signals. We trusted faces on video calls, voices on the phone, and emails that appeared to come from people we knew. These cues felt human and intuitive. They anchored how decisions were made, approvals were granted, and access was authorized. AI-powered deepfakes have quietly broken that model ...

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Is Your Data Safe? How to Assess Your Data Risk - Part 1

Christophe Toum
Talend

Data is one of a company's most valuable assets — but Talend's recent Data Health Survey found only 40% of executives always trust the data they work with.

Today, we have codes and inspections for physical infrastructure, satisfaction surveys for employees, and up-time monitors and stability tests for websites. But are we doing everything we can to understand the degree to which our data is exposed to risk?

There's more to security than protecting yourself from hackers. On one end of the spectrum, you have those big exposures to governmental regulations and security breaches that can shake an entire organization. But even small things — like a little bit of bad data entering the system — can cause a trickle down effect that impacts every department.

We could all be doing a better job of assessing (and mitigating) risk to our data. The key is to start small: just make sure that you have the right data in the right place.

Then you want to make sure that the right people have access to the data and the wrong people don't have access to the data.

Once you have that covered, and you've defined processes for keeping your data clean and standardized, then you can start focusing on making that a daily practice. All it takes is the right combination of people, processes and technology.

What Do We Mean by "Risk?"

When most people think about the risks associated with data, they immediately recall the headline-grabbing data breaches that seem to flood our news feeds with alarming regularity.

But it doesn't take an epic leak affecting millions of users to have serious consequences for most companies. Even a handful of exposed records could have serious legal, financial and reputational repercussions. Fines for GDPR violations alone can run in the millions of dollars, to say nothing of the incalculable cost of losing consumer trust in an increasingly connected and competitive marketplace.

How do these breaches happen?

It can be something as simple as the right data in the wrong place. So much of our conversation about security centers around personally identifiable information (PII). If PII data isn't identified or isn't in the right field — for example, payment information erroneously mapped to an unprotected field and viewed by unauthorized individuals — you could be at risk of exposing some very sensitive information.

But external risks aren't the only dangers we should be worried about. A few years ago, IBM famously calculated that bad data costs US businesses over$3 trillion per year. This is death by a thousand cuts, parceled out in seconds, minutes and hours lost to manual data correction, re-running suspect reports and pursuing strategies and programs that were originally scoped based on data that was later revealed to be faulty.

Of course, the volumes of data we must deal with has grown by over 400% since IBM released that study — and it's only growing.

So how much could we be losing today?

And how much do we stand to lose over the coming years?

Taking all these dangers into account, one thing is clear: no company can afford to expose its data to risk.

Go to: Is Your Data Safe? How to Assess Your Data Risk - Part 2

Christophe Toum is Senior Director of Product Management at Talend

Hot Topics

The Latest

Enterprises today operate in a real-time environment where uninterrupted access to trusted data has become a baseline expectation for users, applications and automated systems. Traditional DataOps models, built on manual effort and human triage, cannot keep pace with this always active demand. AI agents are emerging as the operational backbone, ensuring consistent data availability, reinforcing trustworthiness and enabling a level of scale that manual processes cannot achieve ...

For decades, trust in the digital workplace rested on familiar signals. We trusted faces on video calls, voices on the phone, and emails that appeared to come from people we knew. These cues felt human and intuitive. They anchored how decisions were made, approvals were granted, and access was authorized. AI-powered deepfakes have quietly broken that model ...

Cloud migration was supposed to be a one-way door. For most enterprises, it turns out it isn't. Cloud data repatriation is a real and growing trend. A new survey ... finds that 89% of organizations plan to expand their on-premises infrastructure footprint over the next two years — and 75% have already moved at least some workloads back from public cloud in the past 24 months. The findings point to a broad rethinking of where data belongs ...

Over the past few years, large language models (LLMs) have revolutionized the software industry. Given their ability to excel at multi-step reasoning, LLMs have helped enterprises streamline workflows and adapt to the unknown. However, employing such models comes with sky-high costs, latency issues, and limited flexibility. In the realm of IT operations, it is generally wiser to employ smaller, domain-specific models instead ...

For years, DevOps teams operated under a simple assumption: collect enough telemetry, and you can find and fix any problem. That assumption is breaking down. Modern enterprises now operate across microservices, hybrid cloud environments, APIs, Kubernetes, and highly automated delivery pipelines. Releases happen continuously, dependencies shift constantly, and failures spread faster than teams can diagnose them ...

New Relic surveyed IT and engineering leaders from the media and entertainment (M&E) sector to understand what's working — and where challenges persist with their observability practices. The findings reveal how M&E organizations are navigating rising platform complexity, audience expectations, and AI-driven change. Below are five takeaways that stand out ...

Let me start with something I've seen play out more times than I can count. A team hits a wall with the cloud. Costs creep up, then spike. Performance starts to feel inconsistent. Someone in finance asks a simple question like "why did this double?" and nobody has a clean answer ... Maybe this isn't the right place for everything. That realization feels like a breakthrough, like you've identified the problem. In reality, you've just identified the starting line ...

In MEAN TIME TO INSIGHT Episode 24, Shamus McGillicuddy, VP of Research, Network Infrastructure and Operations, at EMA discusses network observability tool sprawl ... 

In cloud-native systems, scaling is often as simple as moving a slider. For on-premise databases, the stakes are different. Over-provisioning hardware is expensive. Under-provisioning leads to performance bottlenecks that are difficult to fix once the equipment is in the rack ...

When most people think about cybersecurity, they picture firewalls, encryption, and access controls — technical tools designed to protect systems and data. But beneath the technology lies a deeper set of principles about trust, decision-making, and resilience ... The best leaders don't eliminate risk. They manage it intelligently. And in many ways, cybersecurity offers a surprisingly useful playbook for doing exactly that ...