What is Data Integrity and Why is it Important?


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In an era of big data, data health has become a pressing issue when more and more data is being stored and processed. Therefore, preserving the integrity of the collected data is becoming increasingly necessary. Understanding the fundamentals of data integrity and how it works is the first step in safeguarding the data.

Data integrity is essential for the smooth running of a company. If a company’s data is altered, deleted, or changed, and if there is no way of knowing how it can have significant impact on any data-driven business decisions.

Data integrity is the reliability and trustworthiness of data throughout its lifecycle. It is the overall accuracy, completeness, and consistency of data. It can be indicated by lack of alteration between two updates of a data record, which means data is unchanged or intact. Data integrity refers to the safety of data regarding regulatory compliance- like GDPR compliance- and security. A collection of processes, rules, and standards implemented during the design phase maintains the safety and security of data.

The information stored in the database will remain secure, complete, and reliable no matter how long it’s been stored; that’s when you know that the integrity of data is safe. A data integrity framework also ensures that no outside forces are harming this data.

This term of data integrity may refer to either the state or a process. As a state, the data integrity framework defines a data set that is valid and accurate. Whereas as a process, it describes measures used to ensure validity and accuracy of data set or all data contained in a database or a construct.

Data integrity can be enforced at both physical and logical levels. Let us understand the fundamentals of data integrity in detail:

Types of Data Integrity

There are two types of data integrity: physical and logical. They are collections of processes and methods that enforce data integrity in both hierarchical and relational databases.

Physical Integrity

Physical integrity protects the wholeness and accuracy of that data as it’s stored and retrieved. It refers to the process of storage and collection of data most accurately while maintaining the accuracy and reliability of data. The physical level of data integrity includes protecting data against different external forces like power cuts, data breaches, unexpected catastrophes, human-caused damages, and more.

Logical Integrity

Logical integrity keeps the data unchanged as it’s used in different ways in a relational database. Logical integrity checks data accuracy in a particular context. The logical integrity is compromised when errors from a human operator happen while entering data manually into the database. Other causes for compromised integrity of data include bugs, malware, and transferring data from one site within the database to another in the absence of some fields.

There are four types of logical integrity:

Entity Integrity
A database has columns, rows, and tables. These elements need to be as numerous as required for the data to be accurate, but no more than necessary. Entity integrity relies on the primary key, the unique values that identify pieces of data, making sure the data is listed just once and not more to avoid a null field in the table. The feature of relational systems that store data in tables can be linked and utilized in different ways.

Referential Integrity
Referential integrity means a series of processes that ensure storage and uniform use of data. The database structure has rules embedded into them about the usage of foreign keys and ensures only proper changes, additions, or deletions of data occur. These rules can include limitations eliminating duplicate data entry, accurate data guarantee, and disallowance of data entry that doesn’t apply. Foreign keys relate data that can be shared or null. For example, let’s take a data integrity example, employees that share the same work or work in the same department.

Domain Integrity
Domain Integrity can be defined as a collection of processes ensuring the accuracy of each piece of data in a domain. A domain is a set of acceptable values a column is allowed to contain. It includes constraints that limit the format, type, and amount of data entered. In domain integrity, all values and categories are set. All categories and values in a database are set, including the nulls.

User-Defined Integrity
This type of logical integrity involves the user's constraints and rules to fit their specific requirements. The data isn’t always secure with entity, referential, or domain integrity. For example, if an employer creates a column to input corrective actions of the employees, this data would fall under user-defined integrity.

Difference between Data Integrity and Data Security

Often, the terms data security and data integrity get muddled and are used interchangeably. As a result, the term is incorrectly substituted for data integrity, but each term has a significant meaning.

Data integrity and data security play an essential role in the success of each other. Data security means protecting data against unauthorized access or breach and is necessary to ensure data integrity.

Data integrity is the result of successful data security. However, the term only refers to the validity and accuracy of data rather than the actual act of protecting data. Data security is one of the many ways to maintain data integrity. Data security focuses on reducing the risk of leaking intellectual property, business documents, healthcare data, emails, trade secrets, and more. Some facets of data security tactics include permissions management, data classification, identity, access management, threat detection, and security analytics.

For modern enterprises, data integrity is necessary for accurate and efficient business processes and to make well-intentioned decisions. Data integrity is critical yet manageable for organizations today by backup and replication processes, database integrity constraints, validation processes, and other system protocols through varied data protection methods.

Threats to Data Integrity

Data integrity can be compromised by human error or any malicious acts. Accidental data alteration during the transfer from one device to another can be compromised. There is an assortment of factors that can affect the integrity of the data stored in databases. Following are a few of the examples:

Human Error

Data integrity is put in jeopardy when individuals enter information incorrectly, duplicate, or delete data, don’t follow the correct protocols, or make mistakes in implementing procedures to protect data.

Transfer Error

A transfer error occurs when data is incorrectly transferred from one location in a database to another. This error also happens when a piece of data is present in the destination table but not in the source table in a relational database.

Bugs and Viruses

Data can be stolen, altered, or deleted by spyware, malware, or any viruses.

Compromised Hardware

Hardware gets compromised when a computer crashes, a server gets down, or problems with any computer malfunctions. Data can be rendered incorrectly or incompletely, limit, or eliminate data access when hardware gets compromised.

Preserving Data Integrity

Companies make decisions based on data. If that data is compromised or incorrect, it could harm that company to a great extent. They routinely make data-driven business decisions, and without data integrity, those decisions can have a significant impact on the company’s goals.

The threats mentioned above highlight a part of data security that can help preserve data integrity. Minimize the risk to your organization by using the following checklist:

Validate Input

Require an input validation when your data set is supplied by a known or an unknown source (an end-user, another application, a malicious user, or any number of other sources). The data should be validated and verified to ensure the correct input.

Validate Data

Verifying data processes haven’t been corrupted is highly critical. Identify key specifications and attributes that are necessary for your organization before you validate the data.

Eliminate Duplicate Data

Sensitive data from a secure database can easily be found on a document, spreadsheet, email, or shared folders where employees can see it without proper access. Therefore, it is sensible to clean up stray data and remove duplicates.

Data Backup

Data backups are a critical process in addition to removing duplicates and ensuring data security. Permanent loss of data can be avoided by backing up all necessary information, and it goes a long way. Back up the data as much as possible as it is critical as organizations may get attacked by ransomware.

Access Control

Another vital data security practice is access control. Individuals in an organization with any wrong intent can harm the data. Implement a model where users who need access can get access is also a successful form of access control. Sensitive servers should be isolated and bolted to the floor, with individuals with an access key are allowed to use them.

Keep an Audit Trail

In case of a data breach, an audit trail will help you track down your source. In addition, it serves as breadcrumbs to locate and pinpoint the individual and origin of the breach.


Data collection was difficult not too long ago. It is no longer an issue these days. With the amount of data being collected these days, we must maintain the integrity of the data. Organizations can thus make data-driven decisions confidently and take the company ahead in a proper direction.

Frequently Asked Questions

What are integrity rules?

Precise data integrity rules are short statements about constraints that need to be applied or actions that need to be taken on the data when entering the data resource or while in the data resource. For example, precise data integrity rules do not state or enforce accuracy, precision, scale, or resolution.

What is a data integrity example?

Data integrity is the overall accuracy, completeness, and consistency of data. A few examples where data integrity is compromised are:

• When a user tries to enter a date outside an acceptable range
• When a user tries to enter a phone number in the wrong format
• When a bug in an application attempts to delete the wrong record

What are the principles of data integrity?

The principles of data integrity are attributable, legible, contemporaneous, original, and accurate. These simple principles need to be part of a data life cycle, GDP, and data integrity initiatives.



At Mintigo, we master data science to revolutionize the way people market and sell. Our Predictive Marketing Platform for enterprise enables insight-driven customer engagement to win and retain customers…


What are the Benefits of Data Modeling for Businesses?

Article | January 21, 2022

Businesses that are data-driven are well-known for their success, as data is widely considered to be a company's most valuable asset. Understanding data, its relationships, and the law requires the use of data modelling techniques. Sadly, people who are not familiar with data modelling best practises see them as a pointless documentation exercise. In the eyes of others, it is a hindrance to agile development and a waste of money. A data model is more than just documentation because it can be implemented in a physical database. Therefore, data modelling is not a bottleneck in the development of an application. Due to these benefits, it has been proven to improve application quality and reduce overall execution risks. Data modeling reduces the budget of programming by up to 75%. Data modeling typically consumes less than 10% of a project budget. Data Modelling- Today’s Scenario Data models methodologies for data modelling have existed since the dawn of time. At the very least, it's been around since the dawn of the digital age. In order for computers to deal with the bits and bytes of data, they need structure. Structured and semi-structured data are now part of the mix, but that doesn't mean we've reached a higher level of sophistication than those who came before us in the field of computing. As a result, the data model lives on and continues to serve as the foundation for the development of advanced business applications. Today's business applications, data integration, master data management, data warehousing, big data analytics, data Lakes, and machine learning require a data modeling methodology. Therefore, data modeling is the foundation of virtually all of our high-value, mission-critical business solutions, from e-Commerce and Point-of-Sale to financial, product, and customer management, to business intelligence and IoT. "In many ways, up-front data design with NoSQL databases can actually be more important than it is with traditional relational databases [...] Beyond the performance topic, NoSQL databases with flexible schema capabilities require more discipline in aligning to a common information model." Ryan Smith, Information Architect at Nike How is Data Modelling Beneficial for Businesses A data model is similar to an architect's blueprint before construction begins. The visual manifestation of a development team's understanding of the business and its rules is data modeling. The data modeling methodology is the most efficient way to collect accurate and complete business data requirements and rules, ensuring that the system works as intended. In addition, the method raises more questions than any other modeling method, resulting in increased integrity and the discovery of relevant business rules. Finally, its visual aspect makes it easier for business users and subject matter experts to communicate and collaborate. Let us look into some of the core benefits of data modeling for businesses. Enhanced Performance Following Data modeling techniques and best practices prevents the schema from endless searching and give results faster, resulting in a more efficient database. The data model's concepts must be concise to ensure the best performance. It's also crucial to accurately convert the model into the database. Higher Quality Data Data modeling techniques can make your data precise, trustworthy, and easy to analyze. Inaccurate data and corruption are even worse than application errors. Data can be adequately understood, queried, and reported on as a good data model defines the metadata. Developers can foresee what can lead to large-scale data corruption before it happens because of the visual depiction of requirements and business rules. Reduced Cost Effective data modeling techniques detect flaws and inconsistencies early in the process, making them significantly more accessible and less expensive to fix. As a result, data models allow you to design apps at a reduced cost. Data modeling often takes less than 5%-10% of a project's budget, and it can help lower the 65-75 percent of a project's budget that is usually allocated to programming. Better Documentation By documenting fundamental concepts and language, data model methodologies lay the groundwork for long-term maintenance. The documentation will also aid in the management of staff turnover. As an added bonus, many application providers now provide a data model upon request. For those in the information technology field, it's common knowledge that models are a powerful tool for explaining complex ideas in a simple and straightforward manner. Managed Risk An application database that contains numerous related tables is more complex and thus more prone to failure during development. On the other hand, data model techniques quantify software complexity and provide insight into the development effort and risk associated with a project. Therefore, the model's size and the degree of inter-table connectivity should be considered. Summing up Any business can benefit greatly from data modelling methods and techniques. To the untrained eye, data modelling may appear to be distinct from the type of data analytics that actually add value to a company. In order to make data storage in a database easier and have a positive impact on data analytics, data modelling is an essential first step. Frequently Asked Questions What is data modeling? In software engineering, data modelling refers to the use of specific formal techniques to develop a data model for an information system. This is used to communicate between data structures and points. Which are the five crucial data modeling types? The five crucial data modeling types are Conceptual data model Physical data model Hierarchical data model Relational data model Entity-relationship (ER) data model

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7 Apps to Learn and Practice Data Science Effectively

Article | January 19, 2022

Rapid digitization is generating an enormous amount of data every second. The field of data science employs highly unstructured data to draw meaningful insights that can help businesses make smart decisions. It is one of the ever-evolving technologies that is reshaping the landscape of the digital world. To be a data scientist, one must have the skills, methods, and knowledge of top-notch algorithms that process and execute data efficiently. This article highlights seven apps that can help you grow in your career as a data scientist. 1. SmartWindows SmartWindows is a productivity app for Windows 10 and Windows 11 users. It is a handy desktop application for data scientists when it comes to getting a 360-degree view of multiple desktop files. A data scientist works on multiple data files at a time. Owing to this, excessive switching between apps becomes very inefficient and time-consuming. SmartWindows eliminates that overhead by allowing users to arrange multiple desktop apps and data files on-screen and save the screen arrangement in the SmartWindows profile. Once saved, users can restore the profile at any time, whereby all files will be restored at the exact same position and same windowsize on the desktop screen will be a single click. SmartWindows allows users to create unlimited profiles and up to six displays. It auto-restores the screen arrangements on one or many screens. You can work on multiple work zones and save and restore them at any time. SmartWindows supports multiple browsers such as Google Chrome, Firefox, Microsoft Edge, etc., and restores dozens of tabs all with one click. It also restores Smart Layouts on Windows 11 without the hassle of creating new layouts before getting started on work. 2. QPython QPython is a script engine that runs Python programs on Android. This mobile app contains all the essential components that will help you learn python. Data scientists need to play a lot of data that is hard to interpret and evaluate using manual methods. For complex unstructured data, it is difficult to make decisions, and therefore, for all the preprocessing and advanced techniques, Python is a must-learn programming language to study. QPython gives users leverage to learn Python using their mobile phones. It contains an editor, a console, an interpreter, a terminal, an explorer, and the SL4A library for Android. The app comes with two basic modules, of which one is for beginners called QPython OL and the other is for experienced Python users called QPython 3x. If you are a beginner, you must choose QPython OL as it provides all the basic understanding and programming techniques to get started with. The app does not require an active internet connection, which means you can open and learn it anytime anywhere you want. 3. DataCamp DataCamp is a learning platform to enhance data science skills online. The platform has a variety of programs and skills you can learn from. The platform follows a comprehensive hands-on experience in which you learn, practice, and apply the techniques of data science to real-world problems. It follows a complete curriculum of advanced and non-coding essentials that can help users learn data science and machine learning. It offers hundreds of video courses on different subjects. Practice can be done through various interactive exercises that can help you improve your data science skills. 4. Elevate Elevate is a cognitive training mobile app for Android and iOS. It helps users enhance their mind’s processing speed, speaking abilities, and problem-solving skills. They offer personalized training sessions that include exercises. It is a suitable tool for data scientists to strengthen their analytical and communication skills. Elevate consists of more than 35 games that enhance self-confidence, productivity, math, writing, and learning skills. 5. Math Workout It is an educational application to test the analytical skills of users, keeping their minds healthy. It involves multiple exercises and tests to increase the processing power of the mind through mathematics. Mental arithmetic helps enhance data science skills. The Math Workout has some cognitive games and mental exercises in more than ten languages that interpret the cognitive psychology of users and train their brains accordingly. The app would make you perform calculations at your fingertips, allowing you to process tough calculations in a matter of seconds. 6. Programming Hub The Programming Hub is a platform developed for developers. It consists of more than 5000 computer programs and 20+ courses for programmers to practice and learn programming languages. Whether you are a beginner or an expert, the Programming Hub offers a wide range of programming courses to help you with problem-solving skills with multiple programs and software frameworks. The app specializes in providing programs for artificial intelligence, R programming, C, C++, C#, Java, and more. 7. NeuroNation As the name suggests, NeuroNation is a brain training app that is designed to improve the brain activity of users through various exercises. It enhances the processing power of the brain and the logical thinking capability of users. It has a set of 60 different exercises, games, and activities. It is a website and an app for both Android and iOS. It has extraordinary features to increase scientific basics, personalization, fun, and motivation among the learners.

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How Data Science Industry is changing - A view from 2022

Article | January 12, 2022

The industry of Data Science has been popular since a decade or so. The aim and workflow of the field has undergone a lot of changes since then. From basic reporting and analytics to predictive and cognitive analytics, data science revolutionized the concept of “Computers that can think”. As of today, Data Science and its subfields are one of the greatest in demand and has a great competition in the industry. Apart from improving businesses, Analytics has proved its capability in various sectors and applications. This has changed the overall structure of the field of Data Science and the opportunities available. The large amount of data and its wide scope comes with plenty of various opportunities and developments. AI, AI everywhere AI started as a boom few years back wherein it saw great potential, but now it’s everywhere. From Research Labs to Education, Healthcare and even in personal devices, AI has taken up various forms, solving many problems and improving various products and services. Even now, experts states that the full potential of AI is not completely utilised and is expected to be used in a few years’ time. The wide use of Artificial Intelligence has motivated many start-ups to focus on the use of AI to build solutions and products. Industries at all scales have taken a move to include AI in their services / products to increase efficiency through intelligent behaviour. The “Mimicabilty” of human brain functionality comes with great potential – Namely Automation and Optimization of various tasks. Data Centric is the trend With a large amount of data generated daily on servers, there comes a need of shifting from model-centric to data-centric. Let us have a glimpse into what each approach means. In model-centric approach, data is kept constant while the model is tweaked to adjust to the data and get a good result. What’s the drawback of this? Not performing good on real world data. But, then why the model gave good results? That is because, model completely got adjusted to the data you gave instead of generalizing to the real-world problem. This issue came in the recent past, thus the trend of Data-centric came in. Experts like Andrew Ng often hosts talks and campaigns to shift the focus on ML Practitioners and Industries to Data rather than model. According to him, “data is like food for the model”. Data Engineers on Rise As a trailing topic to the previous one, Data Engineers will have a good rise in the coming future. As seen before, there is a copious amount of existing data and is getting generated at an un-imaginable rate. This might sound good since, “more is good” for analytics, but comes with the disadvantage of difficulty in ensuring data quality. As more data comes in, maintaining quality can be challenging. One reality here is that not all times a cloud pipeline can be used to ensure data quality and automated cleaning. There are times where raw data is logged as it is. This calls for more data engineers to perform cleaning and tuning of data to ensure it meets quality standards. With the oncoming of Data-Centric AI approach, this will have a great hype in the coming recent time. And speaking from the Industry Point of View, Data is one of the core part of Data Science and has no replacement. No good data, no good results and the crash of Data Science. “Artificial” becoming more on the point for AI AI started as a research topic long back. It all started like a big-bang – Basic linear regression to Neural Networks. The latest AI algorithms can now see like a human (Computer Vision), speak like a human (Natural Language Processing) and assist in decision making (Inferencing using Models). The newer models are much more advanced that AI seems to be – Natural Language Processing algorithms can now carry out tasks on speaking, predict a sentence and even tell what sentiment it has. Computer Vision Algorithms are now assisting doctors to detect defects and diseases through X-rays and MRI images. This all points to the immense capability in the field of AI. In future it is predicted that setting up an AI system will be equivalent to setting up the mind of a human being – A situation called singularity. More opportunities coming up As seen previously, there are a lot of opportunities coming up in Data Science field. With its vast number of applications in every sector, there are a lot of openings coming up. Start-ups and developed industries are now shifting to AI solutions because of its dynamic nature and intelligent decision-making capability. The number of jobs in Data Science is expected to rise in the coming years. Especially Post COVID, organizations experienced a surge in various technologies, out of which Data Science is one of the major fields. Data Science and Artificial Intelligence is one of the most demanded fields for research to happen. Aspiring researchers have a good demand in the AI and ML field. Tech giants have marked their presence in improving AI algorithms, making the systems more efficient and “Intelligent”. Artificial General Intelligence is one of the applications of AI which is focused on varied problem solving rather than a restricted problem domain and is expected to bring a significant change in the AI focused problem solving.

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Effective Ways to Prevent Data Breaches

Article | November 26, 2021

Data breach prevention is going to be the need of the hour as cybercrime continues to grow. Cybercrime is a growing threat to businesses of all sizes. Due to this unprecedented time many companies shifted to work-from-home model. Statics show data breaches are on a rise and can have devastating long-term financial set-back or reputational repercussions to your organization. As a result, businesses must ensure that their data is secure to avoid substantial loss or theft. As data breaches happens in different ways, there is no such thing as a one-size-fits-all remedy. Security needs a multifaceted approach to be effective. In this article we’ll find out different ways prevent data breaches. Impact of a data breach on businesses A data breach can destroy a business, especially for small and medium-sized businesses (SMB). Data is a valuable asset for any business especially, the data related to customers and payments. Cybercriminals find this data valuable. Lack of planning and security creates vulnerabilities for criminals to exploit. It is estimated that 60% of small and medium-sized enterprises will close within six months after the attack. Larger businesses or agencies, on the other hand, will survive. Nevertheless, they too will suffer the consequences. A data breach can impact businesses in the following ways; Financial Businesses must compensate for both immediate and hidden fines (fines, public relations, legal fees, and punitive regulatory measures) for a data breach. In addition, business needs to compensate customers, refund any stolen funds, and bear a share value loss. A smart organization will use this opportunity to develop data security and disaster recovery strategies, which entails financial investment. Fines and fees – The PCI Security Standards Council may impose fines or penalties for a data breach. Both regulatory organisations and card network brands will have different fines. Forensic investigations – Major consequences of a data breach include, the business that was attacked will be accountable to perform a forensic investigation to determine the causes of the data breach. These investigations are costly and often yield valuable evidence and insights to prevent future data breaches. Future security costs – Victims of a data breach may have to bear costs of credit monitoring for customers whose data was compromised. This may also include the costs of identity theft repair, card replacement, and additional compliance requirements from the PCI. Reputation Having a good reputation is the most prized asset for any organization. As a business, one must constantly put effort into building and maintaining brand integrity. A single compromising episode like a data breach can trash the best of reputations. According to a PwC report, 85% of customers won't shop at a business if they have concerns about their security policies. Customers value their privacy, and a data breach will be perceived as a lack of regard for their data and privacy. Furthermore, 46% of businesses reported that security breaches harmed their reputation and brand value. Intellectual Property The product blueprints, business strategies, and engineered solutions are some of your most valuable assets for any organization. Your trade secret gives you an added advantage over your competitors. Hence it needs to be protected as some may not hesitate to use breached intellectual property. Other significant consequences of a data breach include; A data breach can pit the CEO against the CISO Poisoned search results on your corporate brand Loss of sales after a data breach Unexpected expenses Less attractive to new employees, especially in tech positions Legal penalties after a data breach Understanding the aftermath of a data breach is an important step to safeguarding your business. The next step is to create an action plan is to protect what you've worked so hard on. How does a Data breach happen? Data breaches sometimes can be traced back to planned attacks. But, on the other hand, it can result from a simple oversight by individuals or flaws in the infrastructure. Accidental Insider For instance, an employee uses a co-worker's computer and reads files without proper approval or permission. However, the access is unintentional/accidental, and no personal information is revealed. The data was breached, however, because it was read by an unauthorised person. Malicious Insider This person deliberately accesses/shares data with the intent of causing harm to an individual or company. The malicious insider may have genuine authorization to use the data, but the intent is to use the info in nefarious ways. Lost or Stolen Devices Any laptop or external hard drive with important information on it that is not encrypted or unlocked goes missing. Malicious Outside Criminals These are hackers who attack several vectors to collect information from a network or an individual. Global cost of data breach According to the Ponemon Institute's Cost of a Data Breach Report, global data breaches cost $3.86 million on average in 2020. The amount in 2020 was somewhat lesser compared to 2019 when it hit $3.92M. The same report found that the average cost of a data breach in 2020 totaled $8.64M. Ways to prevent a data breach Conduct employee security awareness training Control access to data sensibly Update software regularly. Require secure passwords and authentication Simulate phishing attacks Evaluate accounts Limit access to your most valuable data. Review your user account lifecycle processes Insist on complex and unique passwords Protect against authentication bypass Store sensitive personal information securely and protect it during transmission Consider implementing a secure SSO solution Secure all endpoints Segment your network and monitor who's trying to get in and out Manage Vendors - Third-party vendors must comply. Conclusion Protecting against data breaches may appear to be a time-consuming procedure. You will be in a better position if you take an encrusted step to secure your data using various methods, policies, and procedures to ease security threats. FAQ’s How does a data breach impact an organization? Depending upon the company and data type, the consequences may include destruction or corruption of databases, leaking of confidential information, the theft of intellectual property, and regulatory requirements to inform and possibly compensate those affected. What is the most common data breach? Hacking attacks are the most common cause of a data breach. However, it is often a weak or lost password that is the vulnerability that the opportunist hacker is exploiting. { "@context": "https://schema.org", "@type": "FAQPage", "mainEntity": [{ "@type": "Question", "name": "How does a data breach impact an organization?", "acceptedAnswer": { "@type": "Answer", "text": "Depending upon the company & data type, the consequences may include destruction or corruption of databases, leaking of confidential info, the theft of intellectual property, and regulatory requirements to inform and possibly compensate those affected." } },{ "@type": "Question", "name": "What is the most common data breach?", "acceptedAnswer": { "@type": "Answer", "text": "Hacking attacks are the most common cause of a data breach. However, it is often a weak or lost password that is the vulnerability that the opportunist hacker is exploiting." } }] }

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At Mintigo, we master data science to revolutionize the way people market and sell. Our Predictive Marketing Platform for enterprise enables insight-driven customer engagement to win and retain customers…