Governments, the scientific community and the population, in general, are increasingly concerned about the effects of climate change that are beginning to be evident. They are promoting a shift in the industry model towards sustainability. This is primarily focused on the sectors that consume the most energy, and data centers have been placed in this category, leading operators to change their focus toward sustainability.

The industry seems to have reached a consensus on the need to move towards sustainability, which is being seen in statements from the most important segments of the industry, such as cloud service providers. But reducing carbon emissions from data centers is a complicated undertaking. As companies explore the options available to them and the requirements to achieve them, they encounter new challenges that are more complex than anticipated.

According to experts at the Uptime Institute, many of the methods proposed so far are more complicated and may seem counterintuitive and counterproductive to the industry. For example, most of the commitments adopted by the members of the sector are voluntary, which has led to a certain laxity in the definitions, the objectives and the very terminology used when talking about the sustainability of data centers, as well as in the level of deepening of the strategies to be adopted.

What does seem clear to experts is that sustainability reporting requirements for data center operators will become increasingly mandatory, either through legislation or as a result of commercial pressure. And that the lack of publication of data or the failure to meet the objectives will be accompanied by sanctions and other harmful consequences for the business. This will lead companies in the sector to almost necessarily move towards greater sustainability, but the lack of consensus on how to do it is generating discord and problems.

The most vital driver of this transition to date through regulations is the European Union’s Energy Efficiency Directive, which has achieved a 55% reduction in carbon emissions by 2050, but new ones are expected. Principles will try to speed up this process. Regulations like this will force complete control of the environmental footprint of industries in the region, including data centers. An increase in public audits is expected to verify compliance, affecting operators smaller (from 300 to 400 kilowatts of total load per installation).

But all these changes that are wanted to be promoted in the sector face numerous difficulties since the large companies, especially cloud and colocation service providers, have more capacity to move towards sustainability. At the other extreme are the more modest companies, whose scope for action and resources are more limited. At the same time, large companies are lobbying to relax regulations on sustainability and emission reductions in the EU while relying on compensatory measures and compensation to balance their energy efficiency, something that smaller companies are not in a position to do

But experts believe that these measures only act as ‘make-up’ to hide what is, in many cases, a lack of commitment to real change towards greater energy efficiency. And they believe that in the coming years, they will not save highly energy-consuming companies, such as colocation companies, from adopting a strategy truly focused on reducing consumption. They also undermine the accusations of the big players in the sector, who claim that the most remarkable inefficiency occurs in the facilities of the smaller data center operators.

To achieve the change that the industry needs, the Uptime Institute believe that it is necessary for IT services, colocation and cloud client companies to advance in strategies to reduce their Scope 2 carbon footprint, which includes indirect emissions—derived from the services they subcontract. This will force providers to be more transparent and put more effort into making this change. Although they point out that many colocation clients currently classify their provider’s emissions as Scope 3, which requires more lax oversight and is not covered by costly carbon offsets.

Much of the data center community, including the Uptime Institute as the certification body, believes that IT owners should take responsibility for Scope 2 but acknowledges that this is problematic. In this sense, IT owners and operators should take responsibility for the carbon emissions resulting from the purchase of energy made by cloud or colocation providers concerning the services provided. However, many are still unwilling to do so. In addition, this responsibility would be associated with others, which generates excellent reluctance.

Everything seems to indicate that disagreements over the model that should be adopted by the transformation of the data center industry towards greater environmental sustainability are going to worsen this year. And fundamental problems such as the lack of a standardized metric to measure IT efficiency based on current criteria of helpful work per watt, which is intended to apply to any workload, regardless of its nature, still need to be resolved.

Also Read: Data Centers Drive Adoption Of ARM-Based Servers

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All this information is organized in a database and analyzed to offer potential customers content, products, and services based on their needs and interests. In this way, we can provide a relevant and satisfactory customer experience that increases the chances of conversion and loyalty.

Benefits Of Database Marketing

• It helps you better segment your marketing messages. Database creation allows for an accurate, centralized record of each customer, including information such as demographics, purchasing preferences, and brand-related behaviors. This information will enable you to create precise customer segments to send them the most relevant offers.
• Improve your marketing results. Segmenting marketing messages ensures that each person receives the information best suited to their needs. This increases the chances of a response and ensures that impressions are not wasted on users who are unlikely to convert, ultimately leading to more effective marketing.
• Personalize the customer experience. According to a study by Epsilon & GBH Insights, 80% of US consumers want brands to personalize their experience. Your database can help you send messages to your customers relevant to them and personalize their experience on your website, for example, tailoring navigation based on the pages that interest them the most.
• It helps you make better recommendations. The information collected through database marketing makes it easier to advise customers and offer them products tailored to their interests and needs.
• Improve the service. Database marketing enables customer-centric marketing, which translates into better service by prioritizing their preferences rather than sending standardized messages. In addition, as the interactions with these messages also become part of the database, we can use all this information to send more and more adapted messages.
• Improve the relationship with your customers and long-term loyalty. Analyzing your marketing database helps you identify your most loyal and profitable customers. You can then use this information to offer them special incentives, create loyalty programs tailored to them, and increase the average amount of each order through cross-selling and up-selling actions.

How To Start Implementing Database Marketing In 3 Steps

1) Create A Database

To start collecting data about our customers, we need to have the right software to collect and organize it. Typically, this is done through a CRM (Customer Relationship Management) program.

Having done this previous step, we need to think about what our strategy will be like to collect customer data, always with their permission. There are different options, from encouraging them to fill out a form after having an experience with our brand to offering downloadable content in exchange for email and other essential information.

The next question to solve is: what data will we collect? It is important not to overwhelm the user with excessive requests for information, especially in the first points of contact. Therefore, we must prioritize what information is most important for the brand. Here are some of the data brands collect most often:

• Contact data: essential information to communicate with the user, such as name, telephone or email.
• Demographic data: age, gender, city of residence, profession, level of education.
• Acquisition data: how they have come into contact with your brand.
• Data on preferences and lifestyle: attitudes, values, interests, motivations.
• Behavioral data: which pages of your site they browse, their favorite products, and how much they have spent on each purchase.

2) Segment Your Customers

The information in your database identifies different groups of consumers and sends them additional messages. The most relevant factors to carry out this segmentation depend on each brand, but these are some of the most common:

• Interests: For example, if you have a brand related to sports, you can distinguish between fans of running and swimming.
• Buying Behaviors: For example, you can distinguish between users who haven’t bought from you yet, occasional buyers, and regular buyers.
• Location: significant in the case of businesses with physical establishments.
• Personal situation: for example, families with children, teleworkers, people who have just moved.
• Preferred communication channel: an essential element, as it will help you decide which is the best channel to reach the audience you are looking for with your campaigns.

3) Adapt Your Content And Offers

Based on the consumer segments you’ve identified in the previous step, you’ll be able to create marketing campaigns tailored to their unique needs, from actions aimed at achieving the first conversion among hesitant users to loyalty programs for your most valuable customers.

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Data centers are constantly evolving to respond to the needs of companies, which are becoming more particular and changing as digitization progresses. This forces operators to implement new technologies and expand their reach into the cloud, hybrid IT, and edge locations, changes that introduce significant complexity in managing operations.

As explained in a recent article published by Data Center Frontier, written by Experts, Being able to scale seamlessly is critical, and they depend on the commercial objectives of each moment. they ponders whether it makes sense to outsource data center management services, given local facilities’ extensive operations management needs.

He affirms that in the current scenario, the way of thinking about REITs, data centers, and the management of large data center campuses is changing. To help a company in the data center sector become a more flexible company, rather than a collection of properties, it is necessary to consider the specific needs and particularities of each facility, data center provider, or REIT business. And these companies can bring a lot to enterprise customers who need more flexible, faster, and cheaper solutions to managing their infrastructure and operations.

Experts points to the latest Fortune Business Insights report, which indicates how the demand for more efficient IT is increasing, as data centers need to scale the size and power of IT to serve new applications. Given this situation, experts predict that the data center infrastructure market will reach a value of 142,310 million dollars by the year 2027. The pandemic has been a spur of the need for large colocation providers and providers. Cloud companies have seen a considerable increase in demand and have invested more in IT.

At the same time, data networks are expanding and diversifying to meet the demand for higher bandwidth and global reach connectivity. In response, data centers are upgrading their networks’ capacity to handle increased traffic from their customers. The data center market is expanding, with large operators and emerging companies finding their footing in significant needs. Many companies are abandoning the traditional data center to outsource infrastructure and management services, which they leave from cloud and colocation providers.

In this context, data center management is evolving to respond to increased workloads without excessively growing costs. According to a CBRE report, an average 5-megawatt data center in the United States costs about $270.1 million to operate over ten years, a figure that specific locations like Boston, Chicago, or Silicon Valley could rise to as much as $368. 4 million. And in the regions where it is cheaper, the cost does not fall below 248.3 million.

The constant pressure to reduce the general operating costs of companies, including those associated with IT operations, is driving new management models in data centers. experts believes that working with facilities management experts will positively impact operating models, EBITDA, and the ability of organizations to become more agile. And he thinks these types of partnerships will enable REIT and data center leaders to become technology companies in their own right, abandoning the old idea of ​​being a collection of stand-alone locations and properties that are rented out.

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1. The Data Fabric is consolidated as a basis for well-distributed companies: with the rise of eCommerce and teleworking due to the pandemic, a more complex ecosystem of devices, applications and infrastructure has been generated, without a single solution that integrates all the data provided by each one. In 2022, the Data Fabric will be more widespread as an integration model to improve your analytics and automate many of your data integration, preparation, and exploration tasks. These data fabrics will also reduce their delivery times and be the preferred data management approach for the following year.
2. Data intelligence makes its way to help in decision-making: intelligence tools in decision-making are making their way in companies, with the usual Business Intelligence platforms being complemented by support systems based on Artificial Intelligence and Machine Learning. Thanks to this, they can predict the results of a particular set of actions and decide on the most appropriate measures based on these. In 2022, decision intelligence will be able to do better analysis and evaluation faster.
3. Data Mesh architectures become more attractive- As organizations grow in size and complexity, those that manage data centrally must interact with a wide range of units and users. The handicap is that this makes it difficult to understand the data needs of each department, the interdepartmental information, and which should be the most appropriate to serve its customers. Data Mesh is a new decentralized data architecture approach to data analysis that aims to eliminate these “bottlenecks” and bring decision-making closer to those who understand the data in each area. Starting in 2022, larger companies with distributed data environments will increasingly implement a Data Mesh architecture than by using a unified infrastructure,
4. Organizations embrace composable data:  Monolithic architectures are a thing of the past, but their presence is expected to be even smaller. Multinational companies handle data distributed across regional borders, in the cloud, and from data centers. Consolidating that data in a central location is virtually impossible. This is where the composable data architecture comes in, thanks to which they can choose specific tools to build parts or all of their data infrastructure. In 2022, they are expected to accelerate the creation of their integrated analytics and data environments, giving them more flexibility in creating a data infrastructure that meets their needs. 
5. Small and large-scale data analysis is becoming fashionable: Artificial Intelligence / Machine Learning is transforming the way organizations operate, but to be successful, they also depend on studying their historical data, also known as Big analysis. Data. But in 2022, they will also combine it with small-scale data analytics to create more personalized experiences for their customers. This combination of small and large-scale analysis will gain more traction throughout the year.

Also Read: 6 Advises That You Must Listen Before Investing in ECommerce Business

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The Top Benefits of Using an Effective Data Strategy to Organize and Streamline Your Business

First, what is a data strategy? To best organize your business and streamline processes that can typically take manpower and costly resources, you can use this strategy to increase productivity, efficiency, and the workload of your company.

A data strategy is a method for businesses to organize their data, storing their data, and collecting important information that lets them best utilize their business. By coming up with various channels on how to best integrate their business processes into one streamlined method, they can avoid costly resources being used daily. Using a data strategy can help companies facilitate quicker business decision-making, identifying weak areas in their company, lessening the risk and security failures, strengthening their security, and facilitating faster data transfer.

Helps Your Business Make Better Decisions

One of the main benefits of using a data strategy to help streamline your business processes is to help your company make smarter and faster decisions. By providing your management staff with better information and the ability to analyze your data, you can help those in charge of your company make more informed decisions regarding their data and information.

By using real-time metrics and real-time data to see what is working, what is not working, and where your business is lacking, companies can find out how to best alter their daily processes to become more effective in the long run.

Understand Business Risks

The second benefit of using a data strategy to help streamline your business is to help understand, fix, and mitigate the business risk that can be associated with daily processes or data breaches. Using data analysis to see the risks that your business is facing can help companies come up with new plans and strategies to audit their company, minimize risk, and manage any current security situations.

Figuring out The Anticipated Value

The last benefit of using a data strategy to help organize your business is the ability to figure out how much you may gain from a financial transaction. Since data analysis is one of the best processes when it comes to figuring out how much you might make from a transaction, businesses that have a great level of insight into their potential earnings have a greater ability to see their current strengths, weaknesses, and what to improve on their business.

By figuring out how much you can make from a transaction, businesses can then see if there are any discrepancies in their accounting process or if they should adjust their pricing structure.

Conclusion

As you can see, there are numerous benefits of using a data strategy for your business to be more organized and streamline each process. A data strategy allows businesses to anticipate how much they will learn from a transaction, reduce security risk, and help your management staff make better decisions.

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This is one of the main obstacles to developing artificial intelligence technology in the field of small and medium-sized enterprises. There are significant differences across industries. The situation in some has improved, and others have not changed at all. Changes in laws and regulations are necessary. Without this, in certain fields, such as medicine, the data will not be more accessible.

In the less sensitive area of data, a lot is available. Still, another factor comes into play: the company that manages to collect this data correctly sees its value and competitive advantage and is not eager to share it. So the main driver of progress is institutions. Their data sets are generally not used for business purposes, only for research, and the largest IT company publishes valuable and necessary data sets, but it is not enough.

Every year there is more information, and the data is also enriched. As a result, more and more companies are entering the market, collecting diverse information about users on the Internet, and then selling the data or allowing other companies to use it for free.

Every year there is more information, and the data is also enriched, But the priority has always been and continues to be big business. On the other hand, experts recall that many tagged data sets, frames, and previously trained neural networks have appeared in the public domain that can be used as prototypes for certain tasks.

There are more and more opportunities to see how data works. However, these are test models that can be used to get acquainted with technology but may not be suitable for solving real-life business problems. Today, artificial intelligence technology has matured and developed to such an extent that we cannot help but wonder: Is this a new programming method or a new model for processing data? The future of the field of artificial intelligence depends on the correct understanding of the questions posed.

It is surely not a new way of programming but a new way of solving certain types of problems that have traditionally been solved through programming. This is just a new and quite effective way to process data. Programming is still a broader term, and it is a subset of it. For some tasks, before writing a complex system for a long time, filled with a large number of conditions and rules, we now train a neural network, which produces approximately the same result.

This does not save us from implementing the program itself that receives such data and does something with its processing results, not to mention the business logic on the product side, interfaces, etc. Do not forget that artificial intelligence technology is based, first and foremost, on a classic code that someone previously wrote. Almost all vendors are now trying to include machine learning modules in their solutions to be used for rapid model development and productivity.

In addition, there is a set of tools for data scientists themselves, who can automate the process of creating models, verifying them, testing them, and putting them into production. This is the address of the MLOps, and it may turn out to be one of the most promising addresses. Many manufacturers are looking in this direction. This year there have been big changes in this area.

Also Read: Database Marketing: What Is It And How To Do?

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What Is An Address API?

An address API is an interface that processes postal addresses. The acronym API stands for application programmatic interface. More than one type of address API exists. For example, an address correction API searches address information and compare it against database information to correct user input.

For example, you might see one in action as you complete an application for credit or some service. The application might return address information that’s slightly different from what you entered and then ask you if you’d like to update the information to match what’s in the database.

An address enrichment API adds additional information to improve accuracy. It might add longitudinal coordinates and other information. Other types of address APIs include address autocomplete APIs and geocoding APIs.

Why Do People Use Fake Phone Numbers?

People use fake numbers for a variety of reasons. Sometimes, they use fake numbers because they want to maintain anonymity. Other times, people use fake numbers because they want to take advantage of a free offer or service, and they don’t want to be traced.

In other situations, people use fake numbers with the intent to annoy or harass another person. Fortunately, there’s a way to tell if someone is using a fake number if you need to preserve your business assets.

Can You Trace a Fake Number?

It can be challenging to trace a fake number unless a crime is happening. If a crime is happening, you can contact the police. However, there is no guarantee that they will be able to trace the fake number.

Fortunately, you can usually find a service to help you spot fake numbers so that you can avoid them.

Some websites are dedicated to outing fake numbers. You could use such a website to determine if a number that called you is spam, spoof, or fake. You can attempt to trace a legit number by using a combination of a phone number verification service and an address API service.

How to Check if a Phone Number Is Real?

If you want to check to see if a phone number is real, it’s best to use a phone validator. A phone validator is a program that checks legitimate proprietary and public databases to look for the most accurate and current information about a phone number.

You can always call a phone number to see if it’s real. However, you can use a good phone validator in the same amount of time, and you’ll get all the information you desire, as well.

Now you know about phone number validity and how you can determine if a number that calls you is real. Use this information to ensure that all your business and personal contacts are legitimate and can help your business to thrive.

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What Is Database Marketing

A marketing database is a form of direct marketing based on consumer data collected for later use this information to deliver personalized experiences. On the one hand, the company is dedicated to collecting information: name, contact forms, previous transactions with the company. It is then analyzed to provide offers according to the interests of the consumer and attract them to close the transaction.

Although traditional database marketing was based on physical shipments (brochures, catalogs, coupons, samples.) currently, this can also be completed with the delivery of promotions and information online. In short, it is about creating a relevant experience for the consumer, based on the information the company has about him.

How To Do Database Marketing

1. Create Your Database

The first step is to choose the right software to create your database. CRM programs are standard, which allow data to be stored and provide facilities for later use. At this point, it’s essential to choose a tool that makes it easy to segment customers and organize their information. 

There are different strategies to obtain your clients’ data, always with their consent: from creating their file by asking for them on the premises to generating online content that they can only download if they give you their data. 

Some data that you can request or collect in an automated way (through forms, loyalty programs, etc.) are:

• Facts Contact: Necessary information such as name, address, phone, or email.
• Acquisition Data: By what channel or advertising campaign they reached your business. 
• Demographic Data: Age, educational level, place of residence.
• Preferences and Motivation: What values ​​do they have, what are their interests and motivations.
• Transaction Data: If they consume again in your business, how often they do it, what products they buy, and how much they spend.

2. Segment Your Customers

Second, it is essential to identify consumer groups and divide them. Categories can track demographics or personal interests. They can also be based on shopping behaviors, including loyal consumers, occasional shoppers, or first-time shoppers. 

3. Generate Appropriate Content

Based on these groups and segments, create marketing campaigns to meet the needs of these customer groups. For example, you can generate a campaign aimed mainly at people who will buy one of your products or services for the first time. You can also specifically target the participants in your loyalty program. 

Personalized campaigns, based on customer data, sell more: Database marketing makes the return on investment (ROI) between 5 and 8 times more profitable, according to at this point, it will also be essential to establish which communication channel your customers prefer to send them these content or offers. 

What Is Database Marketing For

Identify the most valuable customers. You must identify and take care of your most valuable customers (those who buy repeatedly and frequently). Through the database, detect who your valuable customers are and send them specific campaigns that reward them for their loyalty or recommend related products (cross-selling and up-selling).

Recommend suitable products after analyzing the information collected in the database, it is easy to recommend products according to the clients’ interests and their needs. For example, a travel agency can segment its clients according to interests and budget and then recommend backpacking experiences to some and luxury trips to others. It is thus adjusted to the reality of each client and improves their probability of selling.

Provide unique experiences, also in customer service the information collected by database marketing can be useful to all departments of a company. Combining this data with customer service, it is possible to retain customers through an extraordinary and personalized experience.

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CloudMounter can be termed as the system utility that would allow the mounting of the different cloud storage and web servers to the Mac device as local disks. The user can also connect to other devices like Microsoft, Onedrive, Google Drive, Amazon S3, FTP, WebDav servers, etc. like they are located on the user’s machine.

Cloud Mounter has some excellent features handy that makes this very much versatile and add much-supported cloud storage. The user can also have total control over the files that are available online.

Features Of CloudMounter

Let us now discuss the Features of CloudMounter:

With CloudMounter, the user can have storage space on their devices that can be increased if required and avail the same features like the hard drive. This tool helps in connecting the cloud storage to the various accounts of the user like Google Drive, Dropbox, Amazon S3, OneDrive, etc. to Mac’s finder to help the user get services similar to the local Drive. The user, if desired can also add encryptions if they want an extra layer of security and not want to run out of storage anymore.

This tool also helps in connecting to the leading cloud services of the world having various gigabyte plans and also unlimited ones. This tool is very much apt for laptops having very low storage or the hard drives that have been renewed. CloudMounter as a tool also doesn’t sync the files to the local drives and ensures that there is enough space for other important files to stack.

CloudMounter also offers a faster way to access a cloud storage drive from the user’s Mac’s Finder. This will help the user to search, drag, drop, and access all sorts of files in a faster process. This tool also suggests the user to not access the files by logging into the systems through heavy web browsers.

This tool helps the users access the files that are available in the user’s cloud storage account. It also helps in setting up the logins and authorizations for the cloud storage providers from its menu. CloudMounter now supports Dropbox encryption, along with encryptions for Google Drive, Microsoft OneDrive, Amazon S3, etc. More services like OpenStack Swift, Box, Backblaze B2, WebDAV, etc are yet to be provided by this server.

CloudMounter checks the development of the transfers, uploads, and many such updates happening along with the information icons. This tool allows the user to move the files between the cloud platforms right from the finder and check the development as they happen as daily file tasks.

This tool also helps in securing the cloud data encryption and helps in encrypting sensitive data on the cloud storage of the user. CloudMounter, with the help of the AES-256 standard encryption protocol, ensures the securities of the files. This guarantees the user to check if their files are free during an attempted hack or breach.

We would appreciate your feedback on the CloudMounter tool. Let us know your experience below.

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Data Integrity is the process by which it guarantees that the stored information in a database is consistent and accurate.

Today, maintaining the integrity of data is essential for any company given the value they represent. Even more so if this information is fully validated and optimized.

Also, working with correct and reliable data saves time and ensures that the information stored is true and secure.

Why Is It Important To Maintain Data Integrity

During its life cycle, data is replicated, transferred, modified, and endlessly altered. That is why it is necessary to generate methods, rules, and standards for verifying errors and validation processes so that the integrity of the data used in the company is reliable.

Today there are computerized or management systems in the cloud that allow impeccable data integrity, in an organized and structured way.

Also Read: Artificial Intelligence To Enhance Security In Data Centers

There Are Two Types Of Data Integrity

Physical Integrity

To protect the physical integrity of the data, error detection tools must be available to guarantee the integrity and precision of the data in circumstances that may put them or their storage at risk. This includes hardware failures, natural disasters, power outages, human error, or any other extreme factor.

Logical Integrity

For this type of Data Integrity, different criteria are used to protect the information in relational databases from human errors, software errors, or computer attacks. 

There are Four types Of Logical Integrity

• Entity Integrity: It is based on the fact that each table needs a primary key or a unique value that allows the data to be distinguished. This ensures that the tables are not duplicated or have the same identifier.
• Referential Integrity: This type of Data Integrity allows processes to be created through rules embedded in the database, using foreign keys that ensure that the information is properly used and stored. 
• Domain Integrity: The domain is a set of correct values ​​found in the database. Processes are created in the domain integrity that allows each value to be accurate. Also, rules are included that define all categories, values, and formats for entering, restricting, and reading data.
• User-Defined Integrity: When the integrity of the entity, the reference, and the domain is not enough to safeguard the data, the same users create their own rules and restrictions.

Principles Of Data Integrity

• A: Attributable. This refers to the fact that all data created or modified must be documented. It is important to know who created them, when, and why. In other words, the data must be attributable to the person who created it.
• L: Legible (Legible). All information must be able to be read and understood by the human being. For this, structures and values ​​that are easy to interpret must be implemented.
• C: Contemporaneously Recorded. Records must be made at the time the work is carried out and the information must be stored in order of execution.
• O: Original or A True Copy (Original). This means that each of the records must be original.
• A: Accurate (Exact). Each data has to be exact, it cannot contain any error, human or automatic. Also, all of them must reflect the changes, without hiding the old information.

Risks In Data Integrity

With digitization, there are increasing risks to data integrity. These are transferred to different devices and are mostly stored in the cloud, so working to keep them intact is crucial for their proper use in the company.

Registration, storage, modifications, distribution, backup copies, and other actions on data carry the risk of corrupting its integrity, both accidentally and maliciously.

• Human errors.
• Errors in data transfer inadvertently.
• Inappropriate settings and security flaws.
• Computer attacks, malware, or internal threats in the management software.
• Hardware that does not have adequate security protections.

All of this can put data at risk, threatening its integrity. If these are accidentally altered, there may be a significant loss of true information, which means that the data is no longer reliable for use or decision making.

Another fatality can occur if the data is threatened by external computer attacks, something often uncontrollable at the time of its execution. Implementing Data Integrity measures can prevent this entry of malicious agents that affect the distribution, modification, and reliability of the data and, in many cases, spread confidential information.

How To Secure Data Integrity In The Cloud

The platform cloud data integration is an affordable alternative for all companies providing insight into real-time of all agile data, optimum and safe. 

Today, cloud integration tools allow multiple secure applications to connect to and access all stored company data from any location. 

To ensure that these have security that guarantees Data Integrity, you must verify that the provider you choose for storage complies with a series of measures. How?

• Verify the operation of the technology used.
• Request the corresponding security certifications.
• Perform data storage and access tests.
• It monitors that good practices in security and maintenance are observed.

A company that does not take the necessary actions to protect and ensure the integrity of its data is making a serious mistake that can mean the loss not only of important information but also of time and money. To avoid this, evaluate the needs of your company and get down to work on Data Integrity now.

Also Read: The Basic Aspects Of The Business Digitization

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