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 for Improved Efficiency

One of the most significant benefits of AI is its ability to improve efficiency. By automating repetitive tasks, businesses can save time and money while also reducing errors. For example, AI programs perform various tasks, from scheduling appointments to analyzing data sets. The reliability and efficiency of these tools are why more and more business leaders are embracing AI to streamline their operations and stay competitive.

AI for Data Analysis

Data analysis is another area where AI is proving to be a game-changer. With the vast amounts of data, businesses generate daily, making sense of it can take time. AI algorithms can quickly analyze this data, identifying patterns and insights humans might miss. The ability of artificial intelligence to rapidly search, organize, and analyze extensive data is why more and more business leaders are turning to AI for data analysis.

AI for Cybersecurity

As businesses become more dependent on technology, the risk of cyber-attacks increases. AI tools can detect and prevent cyber-attacks by analyzing network traffic and identifying suspicious activity. Its level of proactive monitoring and quick analytics is why cybersecurity is one of the areas where AI is seeing the most significant growth. As a result, business leaders realize the importance of protecting their data and turning to AI to improve their cybersecurity measures.

AI for Enhanced Customer Experience

Another area where AI is making a significant impact is customer experience. Businesses can gain valuable insights into customer behavior and preferences by analyzing customer data, allowing them to tailor their products and services accordingly. Sasan Goodarzi understands the importance of customer experience and has been using AI to enhance the customer experience at Intuit. Intuit’s TurboTax, for example, now uses AI to provide personalized tax advice to customers, making it easier for them to file their taxes accurately.

The Role of Business Leaders in AI

Business leaders have a crucial role to play in the adoption of AI. It is up to them to ensure that their organizations are ready to embrace this technology and have the resources to make the most of it. Integrating AI requires a willingness to invest in AI technology and to prioritize it in their business strategy. One example of a business leader vocal about the importance of AI is Sundar Pichai, CEO of Google. Under his leadership, Google has made significant investments in AI research and development, and the company is now a leader in this field. With a willingness at the executive level to integrate AI for better business, companies can become competitive in the digital marketplace as consumers search for faster, more personalized experiences.

The Future of AI in Business

The potential of AI in the business world is enormous, and it is clear that we are only scratching the surface of what this technology can do. As AI continues to evolve, we can expect to see even more innovative applications emerge. From self-driving cars to virtual assistants, the possibilities are endless. Business leaders who are willing to embrace AI and invest in its development are likely to be the ones who thrive in the years to come.

AI is rapidly changing the business world, and business leaders who embrace this technology will likely reap significant rewards. The benefits of AI are evident, from improved efficiency to enhanced customer experience. However, business leaders must invest in AI and prioritize it in their business strategy to stay ahead of the competition. As AI continues to evolve, the opportunities for businesses will only grow, and those willing to embrace this technology will likely be the ones who succeed in the years to come.

Also Read: The Four Pillars Of Data Integrity

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The need for Edge Computing will continue to increase in the current era of hyperconnectivity that we are experiencing. Instead of relying on distant cloud data centers, edge computing architecture optimizes bandwidth usage. It reduces round-trip latency costs by processing data at the network’s edge, ensuring users end a positive experience with available applications that always work quickly.

Forecasts indicate that the global edge computing market will grow from $4 billion in 2020 to $18 billion in four years. Driven by digital transformation initiatives and the proliferation of IoT devices, Gartner forecasts that more than 15 billion IoT devices will be connected to enterprise infrastructure by 2029, where innovation at the edge will capture imaginations – and budgets. – of the companies.

Therefore, companies need to understand the current state of edge computing, where it is headed, and how to be prepared to improve applications. Harnessing the power of the edge to simplify the management of decentralized architectures. The earliest edge computing implementations were custom hybrid clouds with applications and databases running on local servers backed by a cloud backend. Typically, it was a rudimentary batch file transfer system that handled data transfer between the cloud and local servers.

In addition to the capital expense (CapEx), the operating expense (OpEx) of managing these distributed server installations in custom facilities can be daunting. With the batch file transfer system, apps and services on edge could be running on stale data. In addition, there are deployments where hosting a rack of on-prem servers is impractical (for example, space, power, or cooling limitations on offshore oil rigs or construction sites, and even on aircraft).

To mitigate OpEx and CapEx challenges, the next generation of edge computing usages should take advantage of managed infrastructures at the edge of cloud providers, such as AWS Outposts, AWS local Zones, Azure Private MEC, and Google Distributed Cloud, that can significantly reduce the operational overhead of managing distributed servers. These cloud-edge locations can host storage and compute on behalf of multiple on-prem locations, thereby reducing infrastructure costs while providing low-latency access to data. In addition, edge computing developments can take advantage of the high-bandwidth and ultra-low-latency capabilities of 5G access networks with managed private 5G networks, with proposals such as AWS Wavelength.

The future of edge strategies goes through databases that are prepared for it. In a distributed architecture, data storage and processing can occur at various levels: in central cloud data centers, at cloud-edge locations, and the client/device level – a mobile, a computer, or custom embedded hardware. Each class offers more excellent guarantees of service availability and response capacity concerning the previous story. The co-location of the database on the device ensures a higher level of availability and responsiveness without relying on network connectivity.

A key aspect of database organization is maintaining data consistency and synchronization across tiers, depending on network availability. Data synchronization is not about the bulk transfer or duplication of data across these distributed clouds. It’s about the ability to transfer only the relevant subset of data at scale in a resilient way to network outages. For example, only store-specific data may need to be transferred to your facility in a retail store. Or, in healthcare, only aggregated (and anonymous) patient data may need to be sent from hospital data centers.

The challenges related to data control are exacerbated in a decentralized environment and must be one of the primary considerations to consider in an edge strategy. For example, the data platform must be able to streamline the enforcement of data retention policies down to the device level.

PepsiCo Leverage Edge Computing to Drive Innovation. For many companies, a decentralized database and data synchronization solution are critical to the success of edge computing. Take PepsiCo, a Fortune 50 conglomerate with employees worldwide, some of whom operate in environments where they don’t always have an Internet connection. Their sales reps needed an offline solution to do their jobs properly and efficiently. To do this, the company leveraged an offline-ready database integrated into applications that its sales reps could use in the field, regardless of Internet connectivity. As long as the Internet connection is available,

Similarly, the medical company, which provides software solutions for mobile clinics in rural communities and isolated towns around the world, often operates in locations with little or no Internet access, which affects its ability to use traditional cloud-based services. 

The edge will drive the future of business innovation. In 2023, according to IDC, 50% of the new enterprise IT infrastructure deployed will be at the edge rather than in corporate data centers, and by 2024 it forecasts that the number of applications will increase by 800%. This suggests that as enterprises streamline their next-generation application workloads, it will be essential to consider edge computing to augment cloud computing strategies.

Also Read: Edge Computing Requires Further Standardization

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The data center campus in Tallion, the capital of Estonia, will have a set of modern technologies whose objective will be to optimize the facilities’ energy consumption as much as possible, helping to improve sustainability and reduce costs. These technologies are Building Management Software (BMS), EPMS (Energy and Power Management System) software, and White Space Cooling Optimization (WSCO) systems, all from the German firm Siemens.

These technologies are expanding in the industry with similar bets from large equipment providers for data centers, such as Schneider Electric. With this example, the technology company wants to demonstrate the potential of artificial intelligence to make data centers more sustainable. As explained in their announcement, these solutions can automatically adjust the cooling systems of the server rooms to take advantage of every last watt consumed, improving the adaptability of the systems to any change without generating excessive consumption.

These tools are managed through a dashboard that makes it easy to monitor and control power distribution systems and employs machine learning software to optimize cooling. The creators of these systems claim that their solution has allowed them to achieve a PUE of 1.2 in the data center, while the industry average is currently 1.6.

In his announcement, Kert Evert, director of development of Greenergy Data Centers, explains that “this new complex meets the highest international security standards and aims to operate with 25% more energy efficiency than the market average”. For his part, Dave Hopping, executive director of solutions and services at Siemens Smart Infrastructure, commented that “as the demand for data center services continues to increase globally, digital tools will play a key role in mitigating the environmental impact of data.

With the two future expansions planned by its creators, these facilities could become the most significant data center campus in the Baltic countries. It is expected that they consume energy from 100% renewable sources. Adding this to the strategy of using innovative technologies to optimize consumption, this project will be an example to follow in other geographies where the industry wants to continue expanding, following sustainable development objectives.

Also Read: 7 Risks Of Artificial Intelligence That We Must Face To Manage It Effectively

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Profiles That Do Not Fit The Vacant Position

Correctly defining the characteristics of the profile to be incorporated is a key job and, at the same time, arduous: experience, skills and abilities, training, expectations. Nowadays, thanks to the different communication platforms that we have within our reach, the volume of job applications can be very high.

However, the number of applications that fit the desired profile should be minimal. With the application of Artificial Intelligence, we will be able to establish filters, analyze and categorize requests in an agile way which, in addition to saving resources, will result in the recruitment of more suitable candidates.

Confusion In The Onboarding Processes

For those companies with a large flow of professionals and great size, their incorporation and adaptation must occur with maximum fluidity. The use of machine learning technologies and their application in internal communication platforms substantially facilitates this process, avoids dysfunctionalities, and, above all, generates an environment of greater trust in newly hired employees.

Deficiencies In Training Programs

Artificial Intelligence offers the possibility of developing applications that facilitate employee learning with a high degree of personalization and future needs. Adaptation of curricular programs, support of voice assistants and chatbots, evaluation reports with proposals for improvement, application of game dynamics to educational environments, the possibilities in this area are diverse.

Negative Employee Experience

Each professional is different and different in their perception of their environment, how they relate, their level of expectations. Controlling each of these parameters individually to design an employee experience with a high degree of personalization is a challenge for Human Resources departments. This is possible thanks to extensive data analysis, the interpretation of this enormous amount of data, and its automatic application in differentiated action models.

Template Oversizing

Correctly sizing a template is a determining aspect of the income statement of many companies. But it is also essential to organize schedules, paid leave and rest periods, periods of medical leave. It is no longer just a matter of knowing in detail what is happening today but also being able to predict future scenarios that could compromise the company’s proper functioning. The structure here Artificial Intelligence becomes our best ally and considerably reduces the allocation of human resources to this task, focusing on others of more excellent value.

Errors In Performance Appraisal

Knowledge, analysis, and interpretation of performance and productivity indicators based on objective parameters is a valuable tool when assessing the performance of a work team. Even more so in the case of extensive staff and companies with flexible remuneration formulas and subject to the achievement of objectives. And not only that, this knowledge will allow those responsible for the area to implement measures that limit the impact of absenteeism, demotivation, or excessive turnover.

Also Read: Machine Learning Is Coming As Close As Possible To Humans

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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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Most organizations have switched to remote work mode for their employees and then converted the removed restrictions to mixed mode. Companies are dedicated to digitization and focus on transfer on a practical level. Therefore, customers are more interested in all these products and services that contribute to the development of digitization, including decision-making in artificial intelligence and machine learning.

A Matter Of Trust

Organizations must learn to trust the data obtained. For many people, this is a serious and important topic. Why should we trust the results of work? For two very specific things, experts point out. Artificial intelligence allows companies to increase their competitiveness by adding customer service, sales and marketing positions. The first task is to explain why the model made this or another decision and answer why the data has the greatest impact on the result. 

The second step is to answer the additional question: is a single process possible without human participation, is it not slowed down to verify the working model’s correctness manually? And second, as part of the increase in the confidence metric of the machine learning model, solutions exist and are presented in the market. Also, in several large projects, try to recreate business processes and their optimization under computer control.

Importance Of Innovation

Due to the pandemic and business transformation, new artificial intelligence technologies have come to the fore. In the retail, financial and telecommunications industries, artificial intelligence enables companies to increase their competitiveness by adding customer service, sales and marketing positions.

Recently, there is an integrated digital twin creation in the industry: the system can not only display the current status and history. Still, it can also use flexible and machine learning models to predict those parts of the digital twin. The predictions of those parts of the digital twin have not been strictly described. Due to this hybrid method, good construction of the situation can be done.

Last year, due to the massive development of artificial intelligence algorithms in the retail industry, augmented reality and intersection technology began introducing virtual mirror technology. For example, using a smartphone camera to try on clothes or collecting laundry baskets, as if you were moving inside the store. Some banks immediately announced the creation of virtual assistants.

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Some come from expanding internal knowledge, others from direct interactions with customers. Although companies with Big Data do not struggle with having access to all this information in any of the cases, they also need to implement some data management strategy. For example, cleaning, profiling, and normalizing data lead to better business intelligence. Without these types of actions, visibility is significantly reduced, and the analysis may not be as reliable as it is based on duplicate, incomplete or inconsistent data.

How Can Big Data Add Value To Companies?

There are several ways in which knowledge helps the business. First, companies with Big Data see it promoted and, with this, they benefit from advantages such as:

• Make Better Decisions: They gain in precision because the risk of error decreases when relying on objective data.
• Understand Your Customers: When the vision is not biased or divided into silos, a global perspective facilitates this understanding.
• Provide More Brilliant Services or Products: That is achieved by optimizing internal processes and, at the same time, incorporating the feedback that comes from the different points of contact abroad.
• Improved Operations: The updating of information, the depth of knowledge, and the centralization of data favor efficiency within each area and interdepartmental alignment.
• Generate Income: Companies with Big Data find the basis for innovation in data. Then, based on it and incorporating the latest advances in some cases, they develop innovative products and services.
• Also Read: Concepts Of BigData That Benefits To Your Business?

What Are The Risks That Affect Companies With Big Data?

The benefits of using big data have to do with improved operational efficiency, improved customer satisfaction, driving innovation, and maximizing profits. But companies with Big Data are exposed to risks that do not affect others. Some of them have to do with:

• Lack of organization of data 7 and a data management strategy.
• Data storage-related problems.
• Challenges derived from the analysis.
• Data privacy and regulatory compliance issues.

In the future, we will see that more and more data sources are incorporated into processes. At the same time, it is noticeable that the capacity of IT equipment continues to increase, so it is to be expected that companies and other entities will depend on a broader source of data in the future. Today, artificial intelligence can extract data from sources previously unavailable to computers, making the ability to collect and process data on a large scale available to more companies. 

Tomorrow there will be more companies with Big Data, taking advantage of more data sources and accessing new opportunities. This also leads us to think that big data careers are another exciting trend to watch out for. Does your organization invest in training and development programs to equip the team with sufficient digital skills.

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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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