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

Christophe Toum
Talend

What's Involved in Risk Assessment?

When it comes to your data, there is no single magic bullet that can protect you from every scenario. But you can improve your overall data health by taking a closer look at the three aspects of data risk: sources, security and compliance.

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

Data Sources

Understanding both the quality of individual sources and the quality of your data mapping is key to assessing your risk. When we talk about data sources, we have to consider not only where data comes from, but how it enters our systems. For example, it's probably safe to assume that the lead list you purchased from a vendor isn't as accurate or up-to-date as the list of leads you captured from a recent, targeted, double-opt-in campaign.

But even if you could 100% trust the accuracy of every record from every source — including manual entry by salespeople, submissions from any range of online forms, engagements within products or mobile apps and shared data from partners or parent companies — you would still be looking at a multiplicity of fields, standards and definitions across sources. One source may require a country code in the phone number field, while another does not. One source may have a single name field, while all the others break out first and last names.

Getting these sources to all speak the same language can be a challenge in and of itself, but it is well worth the time and consideration. Fortunately, there are technologies available that will automate data quality as part of the data integration process, so you can avoid risk with the steep time investment of manual data correction. Also, the industry is beginning to recognize the importance of swiftly identifying data's integrity — 95% of executives agree there should be cross industry standard metrics to assess the quality of enterprise data.

Data Security

If all your data were collected in a single Excel spreadsheet, it would be pretty easy to assign a person or two to watch over that data, to keep it secure and to validate it, line by line. But that's not the world we live in. In fact, less than half of enterprise executives report delivering data accuracy, consistency, accessibility or completeness as "very good."

This data disorder is created by a landscape of data infrastructures composed of a complex network of interconnected programs and platforms. There are obviously tools that specialize in connecting systems and ingesting data into a repository. And some businesses have success just doing that — but are they really getting a true sense of data health? Would they even know if they had data quality issues?

The first step of data security is securely connecting to our data sources, ingesting the data and performing that first pass of data quality checks to ensure that we're getting the right data in the right fields. Next, data profiling technology can help us make sure that phone numbers look like phone numbers, and emails look like emails, and so on, so we can feel safe that we haven't mis-categorized sensitive information. Some profiling technologies may even be able to automate resolution for common data errors.

After that, it's time for people to get involved, so the data experts can manually correct, reconcile and validate any records that cannot be confidently evaluated by the automated data quality tools. Proper processes and workflows need to be in place so that the right people can look at it in a formal way. This will require technology for data inventory, data stewardship and data preparation.

Compliance

Good intentions — even good intentions backed by good technology — can only take you so far. A recent study by the UK Information Commissioner's Office (ICO) discovered that up to 90% of data breaches can be traced back to human error. Believe it or not, this is good news — back in 2015, IBM reported that a full 95% of data breaches were caused by human error.

Technology can help here by providing a centralized infrastructure for managing and ensuring compliance across the organization. These products allow you to establish clear access protocols and permissions that will protect your data, without creating false barriers to access that might make people less effective at their jobs. They also make it possible to automate the classification of data through semantic types and build a well-defined business glossary, so that everyone is speaking the same business language when it comes to their data.

Protecting Yourself from Risk

Your data is too important to leave anything to chance. It will take a balance of people and processes, supported by the right technology and automation, for you to keep up with the never-ending flow of data through your company. In a perfect world, we would all have top-of-the-line security solutions and 100% compliance with every piece of advice from the IT team. But, even in this imperfect world, we can make significant progress.

If you're getting ready to make a change, start small: make sure that your data is standardized, cleansed and adheres to whatever standards you have. Solving the problem of compromised data sources will have a ripple effect throughout the organization, making everyone more effective and efficient, and freeing up resources to devote to larger data issues.

Christophe Toum is Senior Director of Product Management at Talend

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Kubernetes was not initially designed with AI's vast resource variability in mind, and the rapid rise of AI has exposed Kubernetes limitations, particularly when it comes to cost and resource efficiency. Indeed, AI workloads differ from traditional applications in that they require a staggering amount and variety of compute resources, and their consumption is far less consistent than traditional workloads ... Considering the speed of AI innovation, teams cannot afford to be bogged down by these constant infrastructure concerns. A solution is needed ...

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

Christophe Toum
Talend

What's Involved in Risk Assessment?

When it comes to your data, there is no single magic bullet that can protect you from every scenario. But you can improve your overall data health by taking a closer look at the three aspects of data risk: sources, security and compliance.

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

Data Sources

Understanding both the quality of individual sources and the quality of your data mapping is key to assessing your risk. When we talk about data sources, we have to consider not only where data comes from, but how it enters our systems. For example, it's probably safe to assume that the lead list you purchased from a vendor isn't as accurate or up-to-date as the list of leads you captured from a recent, targeted, double-opt-in campaign.

But even if you could 100% trust the accuracy of every record from every source — including manual entry by salespeople, submissions from any range of online forms, engagements within products or mobile apps and shared data from partners or parent companies — you would still be looking at a multiplicity of fields, standards and definitions across sources. One source may require a country code in the phone number field, while another does not. One source may have a single name field, while all the others break out first and last names.

Getting these sources to all speak the same language can be a challenge in and of itself, but it is well worth the time and consideration. Fortunately, there are technologies available that will automate data quality as part of the data integration process, so you can avoid risk with the steep time investment of manual data correction. Also, the industry is beginning to recognize the importance of swiftly identifying data's integrity — 95% of executives agree there should be cross industry standard metrics to assess the quality of enterprise data.

Data Security

If all your data were collected in a single Excel spreadsheet, it would be pretty easy to assign a person or two to watch over that data, to keep it secure and to validate it, line by line. But that's not the world we live in. In fact, less than half of enterprise executives report delivering data accuracy, consistency, accessibility or completeness as "very good."

This data disorder is created by a landscape of data infrastructures composed of a complex network of interconnected programs and platforms. There are obviously tools that specialize in connecting systems and ingesting data into a repository. And some businesses have success just doing that — but are they really getting a true sense of data health? Would they even know if they had data quality issues?

The first step of data security is securely connecting to our data sources, ingesting the data and performing that first pass of data quality checks to ensure that we're getting the right data in the right fields. Next, data profiling technology can help us make sure that phone numbers look like phone numbers, and emails look like emails, and so on, so we can feel safe that we haven't mis-categorized sensitive information. Some profiling technologies may even be able to automate resolution for common data errors.

After that, it's time for people to get involved, so the data experts can manually correct, reconcile and validate any records that cannot be confidently evaluated by the automated data quality tools. Proper processes and workflows need to be in place so that the right people can look at it in a formal way. This will require technology for data inventory, data stewardship and data preparation.

Compliance

Good intentions — even good intentions backed by good technology — can only take you so far. A recent study by the UK Information Commissioner's Office (ICO) discovered that up to 90% of data breaches can be traced back to human error. Believe it or not, this is good news — back in 2015, IBM reported that a full 95% of data breaches were caused by human error.

Technology can help here by providing a centralized infrastructure for managing and ensuring compliance across the organization. These products allow you to establish clear access protocols and permissions that will protect your data, without creating false barriers to access that might make people less effective at their jobs. They also make it possible to automate the classification of data through semantic types and build a well-defined business glossary, so that everyone is speaking the same business language when it comes to their data.

Protecting Yourself from Risk

Your data is too important to leave anything to chance. It will take a balance of people and processes, supported by the right technology and automation, for you to keep up with the never-ending flow of data through your company. In a perfect world, we would all have top-of-the-line security solutions and 100% compliance with every piece of advice from the IT team. But, even in this imperfect world, we can make significant progress.

If you're getting ready to make a change, start small: make sure that your data is standardized, cleansed and adheres to whatever standards you have. Solving the problem of compromised data sources will have a ripple effect throughout the organization, making everyone more effective and efficient, and freeing up resources to devote to larger data issues.

Christophe Toum is Senior Director of Product Management at Talend

Hot Topics

The Latest

As businesses increasingly rely on high-performance applications to deliver seamless user experiences, the demand for fast, reliable, and scalable data storage systems has never been greater. Redis — an open-source, in-memory data structure store — has emerged as a popular choice for use cases ranging from caching to real-time analytics. But with great performance comes the need for vigilant monitoring ...

Kubernetes was not initially designed with AI's vast resource variability in mind, and the rapid rise of AI has exposed Kubernetes limitations, particularly when it comes to cost and resource efficiency. Indeed, AI workloads differ from traditional applications in that they require a staggering amount and variety of compute resources, and their consumption is far less consistent than traditional workloads ... Considering the speed of AI innovation, teams cannot afford to be bogged down by these constant infrastructure concerns. A solution is needed ...

AI is the catalyst for significant investment in data teams as enterprises require higher-quality data to power their AI applications, according to the State of Analytics Engineering Report from dbt Labs ...

Misaligned architecture can lead to business consequences, with 93% of respondents reporting negative outcomes such as service disruptions, high operational costs and security challenges ...

A Gartner analyst recently suggested that GenAI tools could create 25% time savings for network operational teams. Where might these time savings come from? How are GenAI tools helping NetOps teams today, and what other tasks might they take on in the future as models continue improving? In general, these savings come from automating or streamlining manual NetOps tasks ...

IT and line-of-business teams are increasingly aligned in their efforts to close the data gap and drive greater collaboration to alleviate IT bottlenecks and offload growing demands on IT teams, according to The 2025 Automation Benchmark Report: Insights from IT Leaders on Enterprise Automation & the Future of AI-Driven Businesses from Jitterbit ...

A large majority (86%) of data management and AI decision makers cite protecting data privacy as a top concern, with 76% of respondents citing ROI on data privacy and AI initiatives across their organization, according to a new Harris Poll from Collibra ...

According to Gartner, Inc. the following six trends will shape the future of cloud over the next four years, ultimately resulting in new ways of working that are digital in nature and transformative in impact ...

2020 was the equivalent of a wedding with a top-shelf open bar. As businesses scrambled to adjust to remote work, digital transformation accelerated at breakneck speed. New software categories emerged overnight. Tech stacks ballooned with all sorts of SaaS apps solving ALL the problems — often with little oversight or long-term integration planning, and yes frequently a lot of duplicated functionality ... But now the music's faded. The lights are on. Everyone from the CIO to the CFO is checking the bill. Welcome to the Great SaaS Hangover ...

Regardless of OpenShift being a scalable and flexible software, it can be a pain to monitor since complete visibility into the underlying operations is not guaranteed ... To effectively monitor an OpenShift environment, IT administrators should focus on these five key elements and their associated metrics ...