GDPR Software 11

GDPR Software: 11 Options to Help You Comply in 2022

Businesses today have an ever-growing list of privacy restrictions to deal with when collecting and managing data. One of the most notorious pieces of privacy legislation is the EU’s General Data Protection Regulation (GDPR), which became the law of the land in 2018 and carries stiff penalties for violators.

Suffice it to say that GDPR compliance can be challenging. This is largely due to its size and scope as well as its evolving nature. In order to meet GDPR requirements, many organizations are turning to purpose-built software solutions that are designed to be GDPR-compliant out of the box. 

Without a doubt, this is the fastest and safest way to use data and avoid regulatory complications for businesses that sell to customers who reside in the EU.

What Is the GDPR?

The GDPR is one of the most comprehensive and far-reaching global privacy protocols implemented to date. It replaced the EU’s Data Protection Act and is now the main data privacy law in the EU.

While the GDPR is extensive, it boils down to some basic foundational principles. At a high level, companies that handle data from consumers in the EU need to operate with lawfulness, fairness, and transparency. They also have to limit the data they collect and focus on data minimization, accuracy, integrity and confidentiality, and accountability, among other things. 

GDPR also grants users eight basic rights to personal data and privacy, including the right to data access, data consent, data deletion, data portability, processing restriction, notification, and rectification.

The GDPR applies to all kinds of personal data, ranging from health and biometric data to basic identity information like names, mailing addresses, and email addresses. GDPR also impacts any company that collects or processes the personal data of residents of the EU regardless of the organization’s location. It applies to large companies as well as to small businesses with 250 or more employees.

Violators of the GDPR may face penalties of up to €20 million (about $23 million) or up to 4 percent of annual worldwide turnover from the previous financial year, whichever is larger.

What Is GDPR compliance?

When an organization is GDPR-compliant, it means the company meets the law’s various requirements for handling personal data.

The list of requirements is extensive. Some of the most important points involve designating an EU representative, embracing an opt-in mode of data collection, establishing time limits for breach notifications, and responding to customer requests for personal data.

Top GDPR Software Solutions To Consider 

In light of the extensive nature of GDPR, it comes as no surprise that organizations are struggling to comply. According to one study, 85 percent of U.S. companies believe that GDPR compliance regulations put them at a disadvantage against their European competitors.

Even though complying with GDPR is proving to be difficult for global businesses, recent technology advancements make it easier. In fact, there are a variety of GDPR compliant tools on the market that can help streamline workflows and keep you out of trouble. 

1. PrivIQ (Formerly GDPR365)

PrivIQ offers a one-stop shop for GDPR compliance. This platform provides everything you need to know to understand your company’s risks and to manage data privacy. 

Some of PrivIQ’s top features include data mapping, access to privacy notice and governance documents, breach logging support, and graphical dashboard reports. 

2. Onspring

Onspring provides cutting-edge risk management software that simplifies workflows, improves transparency, and helps maintain GDPR compliance. 

This software is excellent for capturing and remediating risks as they appear, including financial, reputational, and third-party threats. The software also makes it possible to control access by user, role, and group.

3. SolarWinds Access Rights Manager

SolarWinds Access Rights Manager (ARM) gives you everything you need to manage access rights across your entire IT environment to ensure GDPR compliance. 

Of note, the GDPR requires detailed user access monitoring. This is especially important for users with sensitive data. ARM can produce custom Active Directory and Azure AD reports, providing instant visibility into what different users can access. 

4. LogicGate Risk Cloud

LogicGate Risk Cloud is a cloud-based platform with prebuilt applications that perform a variety of critical GDPR-related functions. 

For example, the platform automates and centralizes customer requests, investigates breaches, and communicates with supervisory authorities. Additionally, LogicGate ensures that third parties are managing personal data effectively. 

5. Netwrix Auditor

Netwrix Auditor can minimize risk during a data breach. The platform quarantines sensitive data, secures overexposed documents, and manages privilege attestations, among other things.

By using a solution like Netwrix Auditor, your team can promptly discover security threats. If a breach occurs, you can spend less time combing through systems and databases and put more effort into dealing with customers and strategizing on a fix. 

6. OneTrust

OneTrust helps companies enhance their privacy programs. The platform offers prebuilt workflows, templates, automation, and regulatory intelligence to help operationalize data and remain in compliance with GDPR.

On top of this, this platform provides transparency about online tracking and captures consent for tracking technologies, cookies, and marketing communications. It also helps maintain and distribute policies and notices.

7. Vigilant Software Compliance Manager

Vigilant Software Compliance Manager identifies legal and regulatory information security requirements for GDPR. 

Using this software, your company can understand the specific actions that it needs to take to comply with various information security laws. Compliance Manager provides effective dates, direct links to legislation, and implementation requirements. 

8. Boxcryptor

Boxcryptor delivers advanced data protection using state-of-the-art encryption, which is a fundamental part of GDPR. 

With the help of Boxcryptor, your company can ensure that all data receives adequate protection in the cloud. The software encrypts files end to end locally on user devices before they go to cloud storage, enabling strong access control.

9. Didomi

Didomi is a leading privacy and consent management platform. The company offers Didomi for Developers, a comprehensive platform that runs on open APIs and helps integrate customer consent into operations.

Didomi makes it easy to build customer permission into your technology, enabling you to simplify privacy protection and preference management.

Further, the platform provides legal and business teams with real-time consent and preference data for easy compliance reporting. It also enables teams to know when consent is required when collecting new data or using it for different purposes. This in turn reduces risk and lets teams operate with greater confidence.

10. Iubenda

Iubenda makes apps and websites legally compliant across multiple legislations and languages, and GDPR is a main focus. 

With Iubenda, you can access helpful services like a privacy and cookie policy generator, a terms and conditions generator, and a consent solution. The company also offers a cookie solution to manage consent preferences for GDPR and other similar regulations.

11. Enov8 Data Compliance Suite 

Enov8’s Data Compliance Suite uses automated intelligence to identify security exposures and address issues before they lead to major incidents. 

The platform gives IT teams clean production-like data for developing and testing platform changes, eliminating complex and time-consuming manual work. 

Simply put, Enov8 enables teams to work faster and with less risk while eliminating costly remediation efforts and compliance issues. 

GDPR Compliance Can Be a Breeze with Enov8

Achieving GDPR compliance doesn’t have to be a nightmare. With the right software in place, your team can continue developing and using data at a fast pace while avoiding costly fines and penalties. 

To learn how Enov8 can help your organization achieve and maintain GDPR compliance, take the platform for a spin.

Post Author

This post was written by Justin Reynolds. Justin is a freelance writer who enjoys telling stories about how technology, science, and creativity can help workers be more productive. In his spare time, he likes seeing or playing live music, hiking, and traveling.


SQL Versus NoSQL: What Is the Difference?

SQL vs. NoSQL? Which database architecture should you use for your next project? Which one is the “best”? Some argue that one is always better than the other. But they are very different technologies that solve different problems.

Let’s take a look at them and see where they differ and where they overlap.

SQL Databases

SQL databases support Structured Query Language (SQL), a language for working with data in relational databases. Broadly speaking, “SQL database” and “relational database” refer to the same technology.

A relational database stores data in tables. These tables have columns and rows. The columns define the attributes that each entry in a table can have. Each column has a name and a datatype. The rows are the records in the table.

For example, a table that holds customers might have columns that define the first name, last name, street address, city, state, postal code, and a unique identification code (ID). You could define the first six columns as strings. Or, the postal code could be an integer if all the clients are in the United States. The ID could be a string or an integer.

The relationships between the tables give SQL its power. Suppose you want to track your customer’s vehicles. Add a second table with vehicle ID, brand, model, and type. Then, create a third table that stores two columns: vehicle ID and customer ID. When you add a new vehicle, store its ID with the customer that owns it in this third table. Now, you can query the database for vehicles, for customers, for customers that own certain vehicles, and vehicles owned by customers. You can also easily have more than one vehicle per customer or more than one customer per vehicle.

Three common examples of SQL databases are SQLite, Oracle, and MySQL.

NoSQL databases

NoSQL database means many things. They’re databases that, well, don’t support SQL. Or they support a special dialect of SQL. Here’s a non-exhaustive list of the more popular NoSQL databases.

Key-Value Databases

Key-Value (KV) databases store data in dictionaries. They can store huge amounts of data for fast insertion and retrieval.

In a KV database, all keys are unique. While the keys are often defined as strings, the values can be any datatype. They can even be different types in the same database. Common examples of values are JSON strings and Binary Large Objects (BLOBs).

Two popular examples of KV databases are Redis and Memcached.

Document Stores

A document store operates like a KV database but contains extra capabilities for manipulating values as documents rather than opaque types.

The structures of the documents in a store are independent of each other. In other words, there is no schema. But, document stores support operations that allow you to query based on the contents.

MongoDB and Couchbase are common examples of document stores.

Column-Oriented Databases

Relational databases use rows to store their data in tables. What sets column-oriented databases apart from them is — as the name suggests — storing their information in columns. These databases support an SQL-like query language, but they store records and relations in columns of the same datatype. This makes for a scalable architecture. Column-oriented databases have very fast insertion and query times. They are suited for huge datasets.

Apache Cassandra and Hadoop HBase are column-oriented databases.

Graph Databases

Graph databases work on the relationships between values. The values are free form, like the values in a document database. But you can connect them with user-defined links. This creates a graph of nodes and sets of nodes.

Queries operate on the graph. You can query on the keys, the values, or the relationships between the nodes.

Neo4j and FlockDB are popular graph databases.

SQL vs. NoSQL Databases: Which One?

So, when you compare SQL and NoSQL databases, you’re comparing one database technology with several others. Deciding which one is better depends on your data and how you need to access it.

Your Data Should Guide Your Decision

Is there a perfect fit for every data set? Probably not. But if you look at your data and how you use it, the best database becomes apparent.

Relational Data Problems

Can you break your data down into entities with logical relationships? A relational database is what you need, especially when you need to perform operations with the relationships.

Relational databases are best when you need data integrity. Properly designed, the constraints that relational databases place on datatypes and relations help guarantee integrity. NoSQL databases tend to be designed without explicit support for constraints, placing the onus on you.

Caching Data Problems

Caching is storing data for repeated access. You usually identify cached data with a single key. NoSQL databases excel at solving caching problems, while relational databases tend to be overkill.

Key-Value stores are an obvious choice for caching problems. Many websites use Redis and Memcached for data and session information.

But a document store that saves documents for historical purposes or reuse is an example of a caching solution, too.

Graph Data Problems

If a graph database stores data with relationships between data, why isn’t it a relational database? It’s because in a graph database relationships are just as important as the data. The relations have fields, names, and directions. Graph queries may include relationships and their names, types, or fields. Relation queries also use wildcards, which account for indirect relationships.

Suppose a database represents rooms in several hosting facilities. It stores buildings, rooms, racks, computers, and networking equipment. This is a relational problem since you have entities with specific relationships.

There could be a table for each entity in a relational database and then join tables representing the relationships between them. But now imagine a query for all the networking equipment in a given building. It has to look in the buildings, find the rooms, look in the rooms for racks, and finally collect all the equipment.

In a graph database, you could create a relation called “contains.” It would be a one-way relation reflecting that one node contains another. Each item in each facility is a node contained by another, except for the buildings. When you query the database for networking gear, a wildcard could combine relationships between the buildings, room, and racks. This query models real life, since you say “Give me all of the gear in building X.”

Scalability: SQL vs. NoSQL

Which technology scales better? NoSQL may have a slight edge here.

Relational databases scale vertically. In other words, data can’t extend across different servers. So, for large datasets, you need a bigger server. As your data increases in size, you need more drive space and more memory. You can share the load across clusters, but not data.

Column-oriented databases were created to solve this problem. They provide horizontal scalability with a relational model.

Key-Value, document, and graph databases also scale horizontally since it’s easier to distribute their datasets across a cluster of servers.

SQL vs. NoSQL: Which One?

SQL and NoSQL are effective technologies. SQL has been around for decades and has proven its worth in countless applications. NoSQL is a set of technologies that solve a variety of different problems. Each of them has its own advantages and tradeoffs.

The question is, which one is best suited for your application? Take the first step by carefully modeling your data and defining use-cases to learn how you need to store and retrieve it. Then, pick the right technology for your application.

Author – Eric Goebelbecker

Eric has worked in the financial markets in New York City for 25 years, developing infrastructure for market data and financial information exchange (FIX) protocol networks. He loves to talk about what makes teams effective (or not so effective!).

what is data analytics internal audit

What Is a Data Analytics Internal Audit & How to Prepare?

No one wants to deal with a data audit. You haven’t invested so much time into putting everything together just to have someone else come in and start raising questions. Hopefully, an audit will never happen to you. Still, the possibility that an audit could happen tomorrow is there, and this post is about what a data analytics internal audit is and how to prepare for it.

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How to Organize a Test Data Management Team

So, you’ve recently learned about what Test Data Management is and why it’s amazingly valuable. Then, you’ve decided to start a TDM process at your organization. You’ve read about what Data Management includes, learned how TDM works, and finally went on to start implementing your Test Data Management strategy. But then you got stuck, right at the start. You’ve got a question for which you don’t have an answer: how to organize a Test Data Management team?

Well, fear no more, because that’s precisely what today’s post is about.

We start with a brief overview of Test Data Management itself. Feel free to skip, though, if you’re already familiar with the concept. We won’t judge you for that; we’re just that nice.

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what is data provisioning in test data management

What Is Data Provisioning in Test Data Management?

This post aims to answer a simple question. Namely, what is data provisioning in the context of Test Data Management (TDM.)

Socrata’s glossary of technical terms defines data provisioning as:

The process of making data available in an orderly and secure way to users, application developers, and applications that need it.

But remember what we want here is to understand what data provisioning is in TDM. While the question itself is—seemingly—simple, you’ll see that it can quickly generate a lot of other questions that need answering if we are to see the big picture.

We start by taking a look at the current state of affairs in the software development world. You’ll understand why applying automation to the software development process is vital for modern organizations and what roles the automated testing plays in this scenario.

We then give an overview of TDM. You’ll learn what Test Data Management is and why it is essential for a healthy testing strategy.

With all of that out of the way, it’ll be time for the main section of the post, where we’ll see what data provisioning is and how it fits into the TDM puzzle.

Let’s get started.

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what is the primary objective of data security controls

What Is the Primary Objective of Data Security Controls?

When you develop a data security architecture and strategy for your organization, your main objective is to protect the organization’s data.

To do that, you first need to identify all threats and vulnerabilities associated with that data and inform the business about the security risks you identified. Next, you need to introduce appropriate countermeasures to manage those risks based on the risk appetite of the organization. To do that successfully, you need data security controls and you need to have a firm grasp on what the primary objective of data security control is. Today, I want to help by answering these questions in this post.

First, we’ll cover the definition of data security controls, what their main goal is, and why understanding security control objectives are important. Then, we’ll review the seven main security control types and their primary objectives. Following that, we’ll dive into security control categories that allow us to further define these controls. Let’s start by first defining data security controls.

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What Does Data Management Include? Introductory Guide

Data management helps you and your organization capture data in a structured and organized way. Also, data management helps improve data quality and makes the data easier to discover. Correct data management implementation brings many advantages to your organization, allowing you and your team to make more informed decisions and improve inefficient processes. But what does data management include?

Data management tackles topics such as data collection and data processing. Let’s take a deeper look at data management. In this article, you’ll find out some of the most important data management best practices and pitfalls.

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How to Perform a Data Quality Audit, Step by Step

A data audit helps you assess the accuracy and quality of your organization’s data. For many organizations, data is the most valuable asset because it can be deployed in so many ways. Organizations can use their data to improve existing processes or services, make important business decisions, or even predict future revenue. And of course, it’s of great value for the marketing team.

However, when your organization doesn’t adhere to standards or processes related to data accumulation and storage, you might end up with poor-quality data. By regularly conducting a data quality audit, you make sure the quality of your data stays high. Even if the quality decreases at some point, you can take immediate action to fix or improve problematic processes.

This article will help you understand how to get started with a data quality audit. First, let’s discuss the importance of a data quality audit.

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what is a data pipeline in hadoop

What Is a Data Pipeline in Hadoop? Where and How to Start

Did you know that Facebook stores over 1000 terabytes of data generated by users every day? That’s a huge amount of data, and I’m only talking about one application! And hundreds of quintillion bytes of data are generated every day in total.

With so much data being generated, it becomes difficult to process data to make it efficiently available to the end user. And that’s why the data pipeline is used.

So, what is a data pipeline? Because we are talking about a huge amount of data, I will be talking about the data pipeline with respect to Hadoop.

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how to build a data management platform

How to Build a Data Management Platform: A Detailed Guide

Does your business need to gain better data insights? Would you like to collect, organize, and activate data from any source, be it online, offline, mobile, and more? Then you need a data management platform, or DMP.

Let’s start with a brief introduction to DMPs. Data management platforms allow you to organize, collect, and activate audience data from any source. Through this, a DMP will add value to your business by providing insights about your customers.

Today, you can buy a DMP from a number of vendors. However, the cost usually ranges from $80K to over $1M for large implementations.

But don’t fret—you have another option. You can build one yourself.

In this post, I’m going to explain how a data management platform works, features of a DMP, and the architecture for building a DMP.

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