Article | April 2, 2020
Data analytics has many purposes in the banking industry, ranging from improving cybersecurity to reducing customer churn. Every interaction from ATM withdrawals to loan applications — provides FIs with valuable data about customers’ financial lifestyles. Banks can even harness external regulatory, trading and social media engagement data, all of which can be processed and analyzed to benefit their operations.Financial data is useful in helping banks develop wide-reaching marketing campaigns, but social data is critical to developing offers for specific customers. Santa Rosa, California-based Redwood Credit Union, for example, found that social data was particularly important when offering auto loans. It initially extended preapproval for such loans every two years based solely on members’ credit scores and vehicle purchase histories, but it soon discovered that there was a much more reliable indicator and updated its preapproval frequency accordingly.
Article | April 2, 2020
The continuous advancements in technology and the increasing use of smart devices are leading tremendous growth in data. Considering reports, more than 2.5 Quintilian bytes of data are generated on a daily basis and it is expected that 1.7 Mb of data will be produced every second in the near future. This is where data scientists play an influential role in analyzing these immense amounts of data to convert into meaningful insights. Data science is an overriding method today that will remain the same for the future. This drives the need for skilled talent across industries to meet the challenges of data analytics and assist delivering innovation in products, services and society.
Article | April 2, 2020
In 2020, the gaming market generated over 177 billion dollars, marking an astounding 23% growth from 2019. While it may be incredible how much revenue the industry develops, what’s more impressive is the massive amount of data generated by today’s games.
There are more than 2 billion gamers globally, generating over 50 terabytes of data each day. The largest game companies in the world can host 2.5 billion unique gaming sessions in a single month and host 50 billion minutes of gameplay in the same period.
The gaming industry and big data are intrinsically linked. Companies that develop capabilities in using that data to understand their customers will have a sizable advantage in the future. But doing this comes with its own unique challenges.
Games have many permutations, with different game types, devices, user segments, and monetization models. Traditional analytics approaches, which rely on manual processes and interventions by operators viewing dashboards, are insufficient in the face of the sheer volume of complex data generated by games.
Unchecked issues lead to costly incidents or missed opportunities that can significantly impact the user experience or the company’s bottom line. That’s why many leading gaming companies are turning to AI and Machine Learning to address these challenges.
Gaming Analytics AI
Gaming companies have all the data they need to understand who their users are, how they engage with the product, and whether they are likely to churn. The challenge is gaining valuable business insights into the data and taking action before opportunities pass and users leave the game.
AI/ML helps bridge this gap by providing real-time, actionable insights on near limitless data streams so companies can design around these analytics and act more quickly to resolve issues. There are two fundamental categories that companies should hone in on to make the best use of their gaming data:
The revenue generating opportunities in the gaming industry is one reason it’s a highly competitive market. Keeping gamers engaged requires emphasizing the user experience and continuous delivery of high-quality content personalized to a company’s most valued customers.
Customer Engagement and User Experience
Graphics and creative storylines are still vital, and performance issues, in particular, can be a killer for user enjoyment and drive churn. But with a market this competitive, it might not be enough to focus strictly on these issues.
Games can get an edge on the competition by investing in gaming AI analytics to understand user behaviors, likes, dislikes, seasonality impacts and even hone in on what makes them churn or come back to the game after a break.
AI-powered business monitoring solutions deliver value to the customer experience and create actionable insights to drive future business decisions and game designs to acquire new customers and prevent churn.
AI-Enhanced Monetization and Targeted Advertising
All games need a way to monetize. It’s especially true in today’s market, where users expect games to always be on and regularly deliver new content and features. A complex combination of factors influences how monetization practices and models enhance or detract from a user’s experience with a game.
When monetization frustrates users, it’s typically because of aggressive, irrelevant advertising campaigns or models that aren’t well suited to the game itself or its core players. Observe the most successful products in the market, and one thing you will consistently see is highly targeted interactions.
Developers can use metrics gleaned from AI analytics combined with performance marketing to appeal to their existing users and acquire new customers. With AI/ML, games can use personalized ads that cater to users’ or user segments’ behavior in real-time, optimizing the gaming experience and improving monetization outcomes.
Using AI based solutions, gaming studios can also quickly identify growth opportunities and trends with real-time insight into high performing monetization models and promotions.
Mobile Gaming Company Reduces Revenue Losses from Technical Incident
One mobile gaming company suffered a massive loss when a bug in a software update disrupted a marketing promotion in progress. The promotion involved automatically pushing special offers and opportunities for in-app purchases across various gaming and marketing channels. When a bug in an update disrupted the promotions process, the analytics team couldn’t take immediate action because they were unaware of the issue.
Their monitoring process was ad hoc, relying on the manual review of multiple dashboards, and unfortunately, by the time they discovered the problem, it was too late. The result was a massive loss for the company – a loss of users, a loss of installations, and in the end, more than 15% revenue loss from in-app purchases.
The company needed a more efficient and timely way to track its cross-promotional metrics, installations, and revenue. A machine learning-based approach, like Anodot’s AI-powered gaming analytics, provides notifications in real-time to quickly find and react to any breakdowns in the system and would have prevented the worst of the impacts.
Anodot’s AI-Powered Analytics for Gaming
The difference between success and failure is how companies respond to the ocean of data generated by their games and their users. Anodot’s AI-powered Gaming Analytics solutions can learn expected behavior in the complex gaming universe across all permutations of gaming, including devices, levels, user segments, pricing, and ads.
Anodot’s Gaming AI platform is specifically designed to monitor millions of gaming metrics and help ensure a seamless gaming experience. Anodot monitors every critical metric and establishes a baseline of standard behavior patterns to quickly alert teams to anomalies that might represent issues or opportunities.
Analytics teams see how new features impact user behavior, with clear, contextual alerts for spikes, drops, purchases, and app store reviews without the need to comb over dashboards trying to find helpful information.
The online gaming space represents one of the more recent areas where rapid data collection and analysis can provide a competitive differentiation. Studios using AI powered analytics will keep themselves and their players ahead of the game.
Article | April 2, 2020
If you want an explicit answer without having to know the extra details, then here it is: Yes, there is a possibility that quantum computers can replace supercomputers in the field of high performance computing, under certain conditions.
Now, if you want to know how and why this scenario is a possibility and what those conditions are, I’d encourage you to peruse the rest of this article. To start, we will run through some very simple definitions.
If you work in the IT sector, you probably would have heard of the terms ‘high performance computing’, ‘supercomputers’ and ‘quantum computers’ many times. These words are thrown around quite often nowadays, especially in the area of data science and artificial intelligence. Perhaps you would have deduced their meanings from their context of use, but you may not have gotten the opportunity to explicitly sit down and do the required research on what they are and why they are used. Therefore, it is a good idea to go through their definitions, so that you have a better understanding of each concept.
High Performance Computing: It is the process of carrying out complex calculations and computations on data at a very high speed. It is much faster than regular computing.
Supercomputer: It is a type of computer that is used to efficiently perform powerful and quick computations.
Quantum Computing: It is a type of computer that makes use of quantum mechanics’ concepts like entanglement and superposition, in order to carry out powerful computations.
Now that you’ve gotten the gist of these concepts, let’s dive in a little more to get a wider scope of how they are implemented throughout the world.
High performance computing is a thriving area in the sector of information technology, and rightly so, due to the rapid surge in the amount of data that is produced, stored, and processed every second. Over the last few decades, data has become increasingly significant to large corporations, small businesses, and individuals, as a result of its tremendous potential in their growth and profit. By properly analysing data, it is possible to make beneficial predictions and determine optimal strategies.
The challenge is that there are huge amounts of data being generated every day. If traditional computers are used to manage and compute all of this data, the outcome would take an irrationally long time to be produced. Massive amounts of resources like time, computational power, and expenses would also be required in order to effectuate such computations.
Supercomputers were therefore introduced into the field of technology to tackle this issue. These computers facilitate the computation of huge quantities of data at much higher speeds than a regular computer. They are a great investment for businesses that require data to be processed often and in large amounts at a time. The main advantage of supercomputers is that they can do what regular computers need to do, but much more quickly and efficiently. They have an overall
high level of performance.
Till date, they have been applied in the following domains:
• Nuclear Weapon Design
• Medical Diagnosis
• Weather Forecasting
• Online Gaming
• Study of Subatomic Particles
• Tackling the COVID-19 Pandemic
Quantum computers, on the other hand, use a completely different principle when functioning. Unlike regular computers that use bits as the smallest units of data, quantum computers generate and manipulate ‘qubits’ or ‘quantum bits’, which are subatomic particles like electrons or photons. These qubits have two interesting quantum properties which allow them to powerfully compute data –
• Superposition: Qubits, like regular computer bits, can be in a state of 1 or 0. However, they also have the ability to be in both states of 1 and 0 simultaneously. This combined state allows quantum computers to calculate a large number of possible outcomes, all at once. When the final outcome is determined, the qubits fall back into a state of either 1 or 0. This property iscalled superposition.
• Entanglement: Pairs of qubits can exist in such a way that two members of a pair of qubits exist in a single quantum state. In such a situation, changing the state of one of the qubits can instantly change the state of the other qubit. This property is called entanglement.
Their most promising applications so far include:
• Drug Designing
• Financial Modelling
• Weather Forecasting
• Artificial Intelligence
• Workforce Management
Despite their distinct features, both supercomputers and quantum computers are immensely capable of providing users with strong computing facilities. The question is, how do we know which type of system would be the best for high performance computing?
High performance computing requires robust machines that can deal with large amounts of data - This involves the collection, storage, manipulation, computation, and exchange of data in order to derive insights that are beneficial to the user. Supercomputers have successfully been used so far for such operations.
When the concept of a quantum computer first came about, it caused quite a revolution within the scientific community. People recognised its innumerable and widespread abilities, and began working on ways to convert this theoretical innovation into a realistic breakthrough.
What makes a quantum computer so different from a supercomputer? Let’s have a look at Table 1.1 below.
From the table, we can draw the following conclusions about supercomputers and quantum computers -
1. Supercomputers have been around for a longer duration of time, and are therefore more advanced. Quantum computers are relatively new and still require a great depth of research to sufficiently comprehend their working and develop a sustainable system.
2. Supercomputers are easier to provide inputs to, while quantum computers need a different input mechanism.
3. Supercomputers are fast, but quantum computers are much faster.
4. Supercomputers and quantum computers have some similar applications.
5. Quantum computers can be perceived as extremely powerful and highly advanced supercomputers.
Thus, we find that while supercomputers surpass quantum computers in terms of development and span of existence, quantum computers are comparatively much better in terms of capability and performance.
We have seen what supercomputers and quantum computers are, and how they can be applied in real-world scenarios, particularly in the field of high performance computing. We have also gone through their differences and made significant observations in this regard.
We find that although supercomputers have been working great so far, and they continue to provide substantial provisions to researchers, organisations, and individuals who require intense computational power for the quick processing of enormous amounts of data, quantum computers have the potential to perform much better and provide faster and much more adequate results.
Thus, quantum computers can potentially make supercomputers obsolete, especially in the field of high performance computing, if and only if researchers are able to come up with a way to make the development, deployment, and maintenance of these computers scalable, feasible, and optimal for consumers.