Today, let us discuss AI in video games and the revolution it brings to the gaming industry and also the future of video gaming with the involvement of AI.

AI in Video Games

Artificial intelligence (AI) aims to simulate human intelligence. It represents techniques and theories allowing the creation of machines. These machines can be used in several sectors of activity, such as video games. Artificial intelligence in video games was used in its early days to create virtual opponents for strategy games like chess and checkers. But this has come so far now. Now, we are in a stage where we cannot defeat an AI in video gaming.

Even though I had performed and showcased its abilities in many games, In games like chess, which can be called a mind game, the AI can predict the human’s next move to win the Game. Sometimes, it will lose these games too. But it still cannot defeat humans in some games like Racing games and firing games because they cannot predict the human’s next move in the Game. In games like chess, which can be called a mind game, the AI can predict the human’s next move to win the Game. Sometimes, it will lose these games too.

Below, we mentioned some games where we can compete with the AI and beat them.

Video Games Where Human Can Beat AI

Although the AI players are tough competitors in the games mentioned below, we can still defeat them if we play with our full potential and bold moves in the Game. According to ExpressVPN, AI still struggles to beat humans in games where it is necessary to understand chronology, a core element in perception of novelty.

The Last of Us Part II: The Last Part of Us Part 2 is an adventure game developed by Naughty Dog. This Game is released in 2020. In this Game, we must fight human enemies and zombie-like characters with fired arms and weapons.

Dota 2: Data is a multiplayer action-packed computer-based Game which is an online battle game. This Game is a sequel to Defense of the Ancients, normally called DOTA. It is designed by Icefrog. Here in this Game, you can see the player vs player combat.

Red Dead Redemption 2: Released in 2018, Red Dead Redemption 2 is an action-adventure game that Rockstar Games designed. This Game includes fights, robberies, racing and player-to-player interactions. Most of the competitors are AI bots. We have to compete with them to survive and win.

Game Go: This is one of the world’s hardest games and one of the oldest games that originated in China and spread to other countries. These games have complex moves and many combinations. In this Game, AI had to work hard to beat humans. If a pro gamer played with an AI with his full potential, he could beat AI.

Left 4 Dead: This is a shooting-based game which is published by valve. This Game can be played as a group, and we have to fight other creatures with the help of bombs and fired arms.
All the above video games are tough ones. In some of these games, we can win with the help of AI, and sometimes, we have to compete with the strongest AI player to win the games.

Conclusion

Artificial intelligence in video games has undergone great developments, but these are only the beginnings of this IT advance. The machine just beat the human, but artificial intelligence in video games still has a long way to go to reach its true potential.

While AI continues to push the boundaries of gaming capabilities, there are still video games where human players can showcase their skills and triumph over their digital counterparts. The combination of human ingenuity and technological advancements creates an exciting gaming dynamic, where humans and AI have their respective strengths.

Also Read: Effective Marketing With Content Personalization

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AI In Healthcare: Four Sketches And Concepts

For three days, the participants brooded over their ideas in working groups, discussed applications of AI in healthcare, and developed possible solutions. This resulted in four extremely promising project outlines.

The AI-based prediction tools aim to improve the clinic’s processes and apply measures that are in line with guidelines preventively to react proactively to complications in the clinic and minimize the risk of rehospitalization. After evaluating various data, the risk assessment tool is intended to provide a prediction before an operation as to whether a person to be operated on has a high risk of certain complications. With this support, certain aspects can already be considered during the treatment.

In addition, the experts developed a solution for predictive workforce management to make more precise and automated predictions about the personnel requirements in the emergency room of clinics. Critical overload situations should be avoided through the forecast and the personnel planning that is coordinated with it. This, in turn, leads to higher employee satisfaction, which ultimately improves the quality of treatment and leads to a reduction in costs.

Another group worked on the Easy Docu project to simplify the interactions between doctor and patient and thus achieve data-driven healthcare. This is a tool for automated documentation using Natural Language Processing (NLP), i.e., the processing of natural language. In the doctor-patient discussion, there is more time for the patient, and communication becomes more personal. The associated evaluations and the consistent transparency of the documentation across the entire patient path also enable a better understanding of the treatment and, thus, high-quality therapy. Patient safety is also increased due to lower susceptibility to errors.

Another project related to AI-based support in the personalized selection of therapy. Based on historical data on a patient, an AI-based prediction is to be made as to how likely it is that a complex therapy will be adhered to (adherence). This allows the doctor to adjust the therapy or take other measures, such as a more intensive care plan. This way, the success of therapy, satisfaction, and quality of life of the patients can be improved, and the severity of side effects minimized. A more efficient therapy can also reduce costs since, for example, additional treatments are no longer necessary. Thus, the entire health system can be relieved by more successful therapies.

Also Read: What Is Facebook Social Network?

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To understand the importance of Machine Learning, the first thing is to know what we mean. Machine Learning is about what the word itself indicates, endowing a machine with the ability to learn, just as we humans do. It is true that hearing the statement that a machine can learn to manage various tasks as if it were a human being can cause a bit of fear, but let’s hope that it is only beneficial. There is no ‘reveal of the machines’ as speculated many times.

Machine Learning Applications

If the power to equip a machine with learning is so important because it is not used, you will think. Even if you don’t realize it, Machine Learning is used in many fields or for many tasks that you had never considered how they were carried out. For example, we have the case of voice assistants. When you use your voice to give a command to the device, what it does is translate those words into text. Once he understands what you mean, he does an internet search or even responds to you. This can only be done if the machine has been learning, for example, what ‘Play a YouTube video’ or ‘Tell me the time’ means.

And it is not only present in the fields of entertainment. It can also be used to create a 3D model of various medical tests and, with increasing accuracy, to be able to detect and prevent tumors, for example. Suppose the machines can recognize tumors in a still very reduced state. In that case, it may be possible to eradicate them from the body without further problems, which detecting them, thanks to the fact that the machines have learned to do so, can save many lives.

How Does It Work

What is done is to provide the machine with certain information as a base. Once that is done, the next thing is to enter data so that it can develop a task and learn from mistakes. That is to say, it acts as the human mind. The main idea would be the following: Introduced to the machine with the label that it is a cat, then different photographs such as number 2 are passed to it to learn what it is a cat. When he has enough information, you can send him a picture of any cat, and he recognizes it. On the other hand, if you send him one of the dogs, he has to detect that it is not a cat.

It is a fundamental way of explaining how it works, but enough to understand it. Things get more complex in much more complicated tasks, such as a car driving autonomously, but the basis is the same: obtain information and learn from it and its mistakes. To conclude, I have to say that even though a significant advance has been made in Machine Learning, there is still a long way to go and who knows, it may be that in the future, many the important diseases will be eradicated or the driver’s license it is only used in circuits since cars will have the ability to circulate autonomously. It is a future that may terrify some, but it is also true that it will be exciting to know to what extent life can be improved.

Also Read: Why Machine Learning Projects Fail- 7 Reasons that can Take Your Efforts for a Ride?

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Communicating with customers efficiently is more important than ever, especially in a sector like logistics, which is experiencing turbulent times due to rising fuel prices. Thanks to the rise of eCommerce, the industry generated 2.3 billion in revenues in 2021, 24% more than the previous year. One of the significant challenges it faces is the last mile, representing up to 40% of total logistics costs. This is where the companies in the sector have to be closer to their customers, offering them quick and simple forms of communication in the event of any questions or problems.

Digitization and automation of logistics processes allow the logistics sector to respond effectively and scalably to changes in market needs. One of the technologies that facilitate this process is chatbots. Through a chat interface, programs simulate fundamental interactions with users, driven by rules and artificial intelligence (AI). we points out eight reasons to bet on a chatbot in the logistics sector as support in that last mile:

• Save costs and time: Implementing chatbots is an investment that will help optimize customer service costs over time by reducing employee hires, which require additional infrastructure, training, and salary expenses. Chatbots act as a first line of defense and make it easy to resolve common customer support issues by routing only the most complex matters to agents, saving both the customer and the business time.
• 24/7 customer service: During the lockdown and in the current situation, the logistics sector had to continue providing a highly available service. A chatbot allows you to optimize the number of staff needed to work during difficult times such as nights, weekends, and holidays.
• Multilingual service: For those companies in the logistics sector that operate internationally, a chatbot allows conversations to be held in different languages, thus facilitating customer service outside.
• It is scalable: If the company is expanding rapidly, chatbots can scale your customer service efforts efficiently, handle millions of inquiries, and save investment in training new employees and infrastructure.
• It allows personalizing the customer experience: The interaction with a chatbot is becoming less robotic since they are programmed to learn on the fly and offer the most satisfactory experience possible.
• Collect crucial information: The implementation of a chatbot allows you to collect information about the logistics company’s service through surveys, allowing you to identify where changes are necessary.
• Reduce human error: Automating predefined responses helps reduce human error by providing approved answers for customer interactions. Online chat can create problems if workers offer inaccurate information about products or services, which a chatbot would not.
• Its multi-channel: Besides adding chat features to a website, a logistics company can get permission from its customers to chat with them on apps they’re already using and are more comfortable with, like WhatsApp or Facebook Messenger. Communicating in apps that customers are familiar with brings comfort and trust to their chatbot interactions.

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Big Data And Pricing. Price Monitoring, Modelling And Management.

One of the main reasons consumers usually make their purchases online is to benefit from more adjusted prices than in physical establishments. This fact makes us think that having a price management and control tool on the Internet is an essential task to make decisions on online pricing strategies.

With a platform for price monitoring, uncertainty in decision-making around a pricing strategy can be reduced. There are tools such as the net price that collect many fees and information about products and services, both internal and from the competition (big data), for subsequent analysis of their fluctuations thanks to their artificial intelligence models. This facilitates a better understanding of the market and allows us to anticipate demand, thanks to alerts of both opportunities and threats concerning the prices of our products/services.

Why extract prices? The massive extraction of fees (in some cases more than 150 million prices per month) and its modelling to detect those threats and opportunities should require the attention of any company. This price monitoring allows focusing the effort only on those prices (own and those of the competition) that can impact the company’s income statement.

In those sectors with solid dependence on price search engines (e.g. insurance), the massive monitoring of prices, own and of the competition in these search engines, through the creation of descriptive and predictive mathematical models, allow a company to be ahead of the competition obtaining a differential advantage thanks to the knowledge that this modelling provides them.

Optimizing the price of the product/service is essential to maximize revenue. A tool like Net worth makes it easy for the commercial department to establish pricing policies based on Revenue or Yield Management techniques, or what is the same, to optimize its price management based on demand and availability of items. Having a platform with these characteristics has become a necessary element for obtaining total control of managing large volumes of prices on the Internet.

Geomarketing: Optimal Locations And Intelligent Consumer Segmentation

One of the advantages of joint analysis of demographic, economic and geographic data is the possibility of conducting market studies to make the marketing strategies carried out by companies profitable. The analysis of this type of data can be carried out through Geomarketing. As its name suggests, Geomarketing is a confluence between geography and marketing. It is an integrated information system – extensive data of various kinds -, statistical methods and graphic representations to give answers to marketing questions quickly and easily.

An example could be projected from different sectors whose objective is to create advanced geospatial models that allow predicting how certain elements (competition, attraction poles, demographics, schedules.) influence the billing of businesses to detect potential areas for improvement.

Also Read: Artificial Intelligence From Analysis To Creativity

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Applications Of Artificial Intelligence In Customer Service

Artificial Intelligence is being used in many sectors and has really interesting applications in areas such as caring for the environment or industrial development, among others. But, as we mentioned before, the help of AI in the Customer Service division is invaluable. These are some of the most useful applications in this field:

• Analysis Of Customer Reactions: One of the functions of Artificial Intelligence is based on the analysis of user interactions. Thus, for example, if the person, in a phone call attended by AI, says words such as supervisor, dissatisfaction, or any other that indicates their dissatisfaction, the system will alert the person in charge of the corresponding department, who could listen to the call, and intervene. if necessary. The main advantage of this functionality is that the analysis is carried out in real-time, so the problem can be solved on the spot, improving the customer experience.
• Detection Of Work Peaks: The use of Artificial Intelligence does not replace humane treatment, but rather complements it. Although more and more users choose to use machines for certain services, such as ATMs, take-out, or tolls, for example, when solving problems or dealing with more sensitive issues, we all continue to prefer humane treatment. For this reason, the detection of work peaks, coming from the analysis of the calls received in a call center, will facilitate the work of the agents and allow the coordinators a much more effective use of resources, which will have a direct impact on better service.
• Direct Interactions With Clients: Interaction with clients is one of the most used functionalities of Artificial Intelligence. Through it, users get answers to frequently asked questions, without the need for an agent to be available. This not only reduces call queues and waits considerably, but also frees workers to take calls that require human intervention.
• Get Ahead Of The User’s Wishes: Prediction is the future of AI and a lot of financial and human resources are being invested to perfect this quality. Artificial Intelligence is called like this, because in many aspects it simulates a human brain, obviously saving distances, but like this one, it is capable of learning through experience

This is especially useful in certain circumstances, and although it may sound very complicated, it is not that complicated. Thus, for example, in a routing system for calls or frequently asked questions, the machine learns from the responses of customers, offering more and more valid options. In the same way, it can be used for other purposes. Imagine that a supermarket customer regularly consults the offers at the end of the month, the system detects it and the brochure could be sent to him by mail, on the 25th, for example, anticipating his action.

Advantages Of Using Artificial Intelligence In Customer Service

In addition to all the functionalities of Artificial Intelligence, the use of this through the virtual switchboard offers us a series of additional benefits. These are the three most interesting for the company:

• Permanent Service: One of the main advantages of using Artificial Intelligence is that it satisfies the customer’s need for immediacy. On the one hand, it can serve a large number of users at the same time, and in addition, the service will operate 24 hours a day, 365 days a year, without the need for the employer to make a large investment.
• Greater Efficiency: Another great benefit of AI is that it increases efficiency, since it allows agents to dedicate themselves to the really important tasks, giving them more time and fewer users to attend to, so those that are derived, from the automatic system, will be treated in better conditions.
• Great Data Analysis Capacity: The data analysis of every call attended by Artificial Intelligence in real-time is very useful. This will allow us to learn about our target audience and the buying habits, concerns, and concerns of our customers.
• Also Read: What Applications Does Artificial Intelligence Have

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However, they will have to be aware of the challenges these innovative technologies entail and remove obstacles to get the most out of technology. The difficulties of harnessing AI and machine learning AI/machine learning technologies improve many industries and areas, from product development to employee productivity to cybersecurity. However, some challenges prevent organizations from taking full advantage of their benefits. You have to understand these challenges to overcome them.

First, identifying and prioritizing the projects that deliver the most business value and going into production quickly is often problematic. In the CCS Insight study, more than 20% of companies noted that the time taken to benefit from AI was one of the biggest challenges. The change in the pandemic’s business environment has meant that companies can no longer afford to have frozen investments in long-term projects and proofs of concept. According to the UK Office for National Statistics, as of November 2020, a staggering 30% of businesses are operating with less than three months of cash reserves.

Another of the main challenges for deploying machine learning projects is the lack of technical knowledge in data science, development, and engineering in this area. Research shows that 30% of companies struggle to meet the challenges posed by data because they lack the fluency and experience to make business and operational decisions. This implies that we must consider the significant gaps in applied fields such as transposing business requirements, quantifying corporate results, and operating and corporate governance practices.

Lastly, organizations are becoming increasingly aware of security, compliance, and ethics in their business. Therefore, companies are willing to apply principles, practices, and technologies that allow ethical, transparent, safe, and responsible AI projects. To overcome these challenges and advance their AI and digital transformation strategies, business leaders must consider the growing set of business AI solutions that have emerged in the last 18 months.

Enterprise-Centric AI Services Enable Simple Yet Powerful Solutions

Business leaders must consider the growing set of business AI solutions or AI applications packaged and focused on solving everyday business and industry problems to advance digital transformation strategies. They require little or no machine learning expertise and can reduce costs and radically accelerate the time it takes to realize business value from AI.

• Demand Forecast: Forecast accuracy is a critical business requirement, especially given the changing demand that most industries are experiencing. Solutions like Amazon Forecast leverage machine learning to deliver personalized forecasts in cash flow, product demand, and resource planning.
• Personalization: Amazon Personalize enables companies to use learning to create personalized services, such as product recommendations, product ratings, and direct marketing. The Bundesliga, the German soccer league, uses Amazon Personalize to improve the fan experience, offering real-time statistics and personalized content during live matches on all its digital platforms. Viewers can also personalize the content they are interested in by tailoring video clips and search results to their favorite clubs, players, or matches.
• Fraud Detection: Organizations lose tens of billions of dollars annually to online fraud around the world. The CCS study found that 49% of organizations currently creating AI solutions focus on security applications targeting fraud. Amazon Fraud Detector is a fully managed service that uses machine learning and more than two decades of Amazon fraud detection experience to identify potential fraudulent activity so businesses can detect fraud online more quickly. With AWS Cloud, customers can automate time-consuming and costly actions to create, train, and implement a machine learning model designed to detect fraud to take advantage of technology.
• Smart Search: This has long been a drag on the productivity of large organizations due to difficulties in locating and accessing information housed in multiple operational systems and silos. By integrating with commonly used repositories such as file systems, applications, intranets, and relational databases, Amazon Kendra uses machine learning to index internal data sources such as documents, intranet content, files, and notes. She makes the information searchable through natural language processing.
• Reliable Operations And Responsible Deployment: Transparency in the way AI arrives at decisions is one of the main factors in encouraging senior managers to adopt these solutions. More and more companies prioritize vital issues such as model explicability, fairness, security, and privacy to build trust and minimize business risk. In that sense, AWS offers a complete machine learning lifecycle platform on SageMaker to address these challenges. They must understand how each factor contributes to the decision of the model. Amazon SageMaker’s Clarify feature gives Zopa data scientists visibility into the reasoning of the machine learning model and trust their stakeholders, both internal and external. 
• Transformation Of Contact Centers: In the past year, Amazon Connect has helped companies modernize their customer contact centers, especially during the pandemic. For example, WebHelp, the European leader in customer relations, had to migrate 36,000 employees to a telecommuting model in the 35 countries in which it operates in just two weeks. Using the cloud and Amazon Connect enabled several thousand “voice” jobs to be activated in less than 72 hours, helping agents be more productive and improving customer experience.

The time has come for companies to seize the opportunities offered by AI and machine learning. Services based on these technologies are on the rise and allow companies, regardless of their sector, to improve their performance and their results. Companies like AWS are accompanying many organizations as they prepare for the future with AI starting today. Executives should seize this opportunity by implementing AI in their business, especially in the post-COVID-19 economy.

Also Read: What Is The Relationship Between Digital Transformation And Organizational Culture?

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This relevance is due to the multiple benefits that its application provides, both in public and private entities and in our sphere. Artificial Intelligence improves the services, the customer’s experience, facilitates the search and management of information, and no helps predict behavior.

This combination of algorithms allows machines to mimic the cognitive functions of humans, automating activities such as problem-solving, decision-making, and learning. This practice makes it possible for organizations to offer a more intelligent and efficient service to their clients. On a personal level, thanks to AI, we solve many procedures quickly and easily. In short, it makes our lives easier.

Artificial Intelligence, A Valuable Tool

One of the most common applications of Artificial Intelligence is Machine Learning. Through different comparisons, an application can learn by itself according to the patterns it finds. An example of this is Google Photos. This application is capable of understanding the faces of each person and automatically categorizing the photos.

Solve a problem with your bank or insurance company by speaking to a virtual assistant; access a device through face detection; run the vacuum while you’re at work, or making a quick selection of hotels for your vacations are also possible thanks to the integration of Artificial Intelligence in many of the mobile applications that we use every day.

If you look at organizations, the potential that Artificial Intelligence provides is enormous. Let’s think about sectors like health. Given the current situation and the high volume of data generated in this industry, AI is of great help for the prognosis of diseases or predicting future demands for services. This enables professionals to make better decisions about service planning or resource allocation.

Online shopping, have you noticed how in just a few years, the attention you receive had improved considerably? You access the page, and immediately a chatbot appears. It is your virtual assistant with which you can solve any doubt in real-time. You start a purchase process and add an item of clothing to the cart. If it is not the size you usually wear, a message will immediately remind you of the size you should purchase, based on your previous buying behavior.

This personalized attention is possible thanks to Artificial Intelligence programs that allow the ‘machine’ communication with the user or make recommendations, emulating human behaviors. Technological evolution has also facilitated the emergence of more intelligent, faster, and more efficient viruses. This brings upside down the IT services of many entities that must protect themselves against the threat of cyberattacks. AI also offers a solution to this problem since it reinforces security by identifying suspicious behaviors, based on previous patterns and specific behaviors.

Artificial Intelligence And RPA

On the other hand, the convergence of RPA software robots with AI allows companies to create new functionalities, increasing their business value and competitive advantages concerning their competition. In the case of RPA technology, the use of Artificial Intelligence amplifies its power. The combination of these tools reduces manual work and allows the execution of more complex processes.

It is the step towards Intelligent Automation that increasingly sophisticated automated decision-making, finding new business processes, and empowering advanced analytics. It is an advance towards complete digital transformation, managing to improve the quality of the service.

As we indicated at the beginning of this article, AI is one of the areas of technology growing the most in recent years. All forecasts indicate that these tools will continue to evolve and become increasingly sophisticated. They will be applied in different areas of information, such as video, images, or voice; thus, and also to the activity of network traffic and sensor data. The world is increasingly hyperconnected, and Artificial Intelligence is a valuable tool that will help us better interpret and understand future challenges.

Also Read: App Design – How To Improve The Design Of Your App

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At the same time, machine learning is a little less glamorous because it tends to sit beneath the bonnets of the tools that we use. In fact, when machine learning is used well, the end-user rarely even realises that ML is a part of the algorithm they’re interacting with. They just think it’s a very well-written algorithm.

Here are just a few of the most notable real-life use cases for machine learning.

Seven Best Real-Life Use Cases for Machine Learning (ML)

1. Content recommendation

Machine learning sits under the bonnet of most of the tools that we use on a daily basis and helps to make recommendations about the content that we might like to consume. It powers recommendations on Spotify, YouTube, Netflix and other popular content providers and streaming sites.

2. Enhancing development

Machine learning and AI development is one of the most exciting current trends in both web development and software development because it can save developers a huge amount of time and thus cut down on the cost of builds. It can automate simple tasks such as searching for and repairing broken links on huge websites.

3. Smarter cities

Machine learning could be used to create smarter connected cities that gather huge amounts of data for the ML algorithm to process. The idea is that every road can gather traffic data and every public building can monitor usage patterns. The city can then re-route roads to cut down on traffic jams or make amendments to staffing rotas to cover peak times in public amenities.

4. Better healthcare

In the healthcare industry, machine learning is being used to analyze huge quantities of patient data and to identify better ways of treating people. In a similar way to how ML is used to make more accurate content recommendations, it can also provide more accurate treatment options based on what worked for other, similar patients.

Also Read: How Companies Are Benefiting From Machine Learning

5.Translation and accessibility

Machine learning does a pretty good job of localising content and making it more accessible, whether that’s by providing text-to-voice functionality for those with visual impairments or whether that’s by allowing people to automatically translate your content into their first language. It’s not as good as a human translator, but it’s close.

6. Fraud prevention

Machine learning can help to combat fraud prevention because it can automatically flag up unusual transactions. In fact, machine learning models can help to increase fraud detection accuracy by as much as 40-50%, and the customer doesn’t even need to know that it’s being used to benefit.

7. Self-driving cars

Self-driving cars use machine learning to function as they cruise around our streets. In fact, they work by analysing the data created by hundreds of thousands of hours of human driving and then basically act by predicting what a human being would do in any given situation.

Conclusion

Now that you know just a few of the best use cases for machine learning in our modern world, it’s over to you to carry out further research or to continue the discussion in the comments. One thing’s for sure, though – artificial intelligence (AI), machine learning (ML) and natural language processing (NLP) are likely to become increasingly important to us as people and as a society as a whole in the years to come.

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Data Intelligence

Vast amounts of data are generated every minute that puts the internal architecture of all companies and Cloud services to the test. Also, there have been changes in both the generation and consumption of data at a global level in the current context.

In 2020, each person produced 1.7 MB of data every second and this figure is estimated to double each year, according to data from Domo’s ‘Data never Sleeps’ report. The reality is that so much information is produced that, used well, it can be the differential value of companies. Therefore, the ability to make decisions based on data is crucial to obtain the maximum potential of Big Data.

Data is the most strategic asset of companies since it contains the strength and great value of companies. A Gartner study affirms that 90% of corporate strategies will consider data their most critical asset in 2022. Both internal data generated by the company itself and external ones, the so-called Open Data provided by public bodies or governments, and our competitor’s data, is precious and requires careful study.

The volume of data we are faced with today is infinite. It is estimated that from the year 2025, around 175 Zettabytes will be generated annually. 1 ZB equals one billion Terabytes, according to IDC data. Faced with this overwhelming scenario, those organizations that take advantage of their data and scale their businesses towards advanced analytics will have a competitive advantage and will ensure the survival of their companies in the long term.

Also Read: Cisco Meraki Cloud-managed Networking Benefits

From Descriptive Analytics To Intelligent Prescriptive Analytics

Today it is necessary to evolve towards smart prescriptive analytics, which guides the steps to follow or adopt strategies. The transformation of companies towards a data-driven model implies a new vision, a new mentality to adapt processes and collaborative models to build and add value to end-users.

Thanks to Artificial Intelligence and Machine Learning models, advanced analytics will allow making predictions, recommendation models, and automation that will help process efficiency. All of this should lead to establishing a Data-Driven Company, an organization focused on turning its data into valuable information, which will allow more strategic decisions to be taken to generate new business models.

When it comes to becoming a data-driven company, some roadblocks can come your way:

• Lack of knowledge and talent for data analysis
• Data can be sealed or unreliable.
• Legacy systems may be incompatible with centralizing information. 

From Planning To Progress

To grant the power of transformation to data, it must be critical for the business. The information must be accessible, interpretable and actionable so that the technology used can drive Data projects. For this, four fundamental pillars are recommended:

• Match data to business priorities
• Create a data-driven culture
• Make the most of the information.
• Implement the technology and infrastructure necessary to undertake this series of initiatives.

Success depends on matching technological innovation with each company’s strategic priorities and specific needs to establish its objectives. Data-driven organizations are not those that have a large amount of data and cutting-edge analytical capabilities but have evolved to take advantage of their data, monitor it, generate insights and make a significant difference among their customers.

Data Ops From Laboratory To Production In An Agile Way

Most companies are still not able to define the appropriate strategy in managing their data since the lack of a centralized data export system can be immobilized to some extent. Therefore, a realistic plan and the establishment of specific technologies will facilitate the search for new opportunities based on data to face this transformation of Data Intelligence projects. 

It is essential to centralize the information in a single system to evolve towards analytical maturity developing far-reaching projects. Thanks to the cloud, we will have computing capabilities to analyze all the information, monitor data in real-time, alert notifications or collaborative analytical systems. An innovative vision of data processing will set the pace for the new era of Big Data and Business Intelligence, a vast ecosystem to explore full of opportunities for organizations.

Also Read: Most Of The SMEs Rely On Artificial Intelligence To Gain Visibility

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