Big Data Analytics Big Data & AI
Big data and artificial intelligence (AI) are two sides of the same coin when it comes to extracting meaningful insights from vast amounts of information. They complement each other in a way that drives innovation and decision-making across industries.
Understanding the Basics
How They Work Together
AI, particularly machine learning, is the engine that powers big data analytics. Here's how:
Real-World Applications
The combination of big data and AI is revolutionizing various industries:
The Future of Big Data and AI
The future holds even more exciting possibilities for big data and AI. Advancements in computing power, data storage, and AI algorithms will continue to drive innovation. We can expect to see breakthroughs in areas like natural language processing, computer vision, and augmented intelligence.
In a recent Discovery Event, participants reflected on how data analytics and artificial intelligence are transforming organizations, industries and society in general. Participants also explored how they can utilize these techniques to ensure their company succeeds in today’s business environment.
In today’s world, disruption from unlikely competitors is omnipresent; industry changes occur in faster and shorter cycles; regulations, such as for data protection, are just around the corner; and time to market is forever shrinking. Virtual assistants are also completely changing the way consumers buy products, acting as gatekeepers by restricting choice through specific recommendations – and allowing people to buy products they have not even seen. Does this mean product packaging still matters? Will we still have some control over what we buy? Well, as research indicates that people actually like these algorithmic recommendations and often follow them, the use of such platforms is likely to keep growing and will continue to raise more and more questions of this kind.
Game of Go
But do not fret – there is light at the end of the tunnel. Take Amazon. It knows what customers are going to buy before they go anywhere near the checkout, thanks to predictive analytics and tons of customer data. Data analytics and artificial intelligence make it possible to link data to gain insights on customers, grow the business, and optimize the speed and quality of logistics. And these new technologies are no longer the prerogative of “tech” firms. More and more companies are integrating such tools to navigate the turbulent waters and turn their ship around.
Does this mean you should collect more data?
Data are necessary to feed algorithms, but avoid falling into the trap of “simply” collecting and storing more data. You need to be able to transform any data you collect into useful information, otherwise it is more likely to just waste resources and add even more complexity. A useful process to draw causal inference from big data could be divided into the following stages:
1.Find interesting patterns in the data
2.Explain those patterns (possibly using experimental manipulation in order to understand the link between cause and effects)
3.Use those patterns and explanations.
The second step is probably the most important and most challenging one. As almost everything in the real world interacts with everything else, researchers have tremendous difficulties explaining patterns of correlation in the data. Could the advent of artificial intelligence (AI) and machine learning algorithms help?
AI and machine learning: Much more than fancy data analytics
World chess champion Gary Kasparov claimed he was the first person to have lost his job to AI when IBM’s Deep Blue beat him at chess. But Deep Blue was not really an AI. Rather, it was a supercomputer with the capacity to compute more moves ahead (200 million positions/second) than a human. It would thus be more accurate to say that Kasparov lost his job to brute force and Moore’s law rather than AI. Artificial intelligence is much more than brute force; it refers to a system able to imitate human intelligence. To highlight the difference, consider the paradox “We know more than we can tell” from Michael Polanyi, a mathematician philosopher. Indeed, many of the tasks we perform rely on intuition and tacit knowledge that are difficult to explain. A typical example is the capacity to recognize one’s mother. Babies and animals can do it effortlessly, but it is difficult to verbalize how we do it. So, if we cannot explain it, how can we teach a computer to do it?
Google DeepMind found a way around Polanyi’s paradox. In January 2016, the London-based company published a research paper explaining how AlphaGo, a Go-playing application it developed, used a combination of machine learning and tree search techniques to master the game of Go. In this ancient two-player board game, each player must try to capture more territory than their opponent on a 19×19 grid (see Figure 1). Despite relatively simple rules, the number of possible combinations in Go (2×10170) is larger than the number of atoms in the observable universe (2×1082). At present, no computer is powerful enough to solve this game using brute force and nobody can explain the game. Even the best Go players cannot articulate their tactics; they rely on intuition and heuristics such as “Do not use thickness to make territory.” This game is considered the most challenging of classic games for artificial intelligence.
Hence, when Lee Sedol, a professional Go player considered the best player in the world in the early 21st century, was challenged by AlphaGo, he was pretty confident: “… I think I will win the game by a near landslide.” But, AlphaGo is fundamentally different from earlier Go programs. It received very little direct instruction and was designed to learn on its own using thousands of past games played, thus developing what we could call a human-like form of intelligence. The game attracted intense interest: over 200 million people worldwide watched the best-of-five match between AlphaGo and Sedol in March 2016. AlphaGo defeated Sedol 4-1. On top of being a landmark scientific achievement, this match also offered striking examples of a computer developing intuition. Move 37 in game 2 was the most famous one. It was a highly inventive move that puzzled all Go players and experts at the time. It was only later in the game that it became apparent that it had been the critical move. Since then, it has been extensively examined by players and has brought a new perspective and knowledge to the game.
Robotics, AI and machine learning (will) have social, political and business effects, transforming many modern industries and displacing jobs. A research paper published in 2017 estimated that 47% of all US jobs are at “high risk” of being automated in the next 20 years. Does that mean technology will be a net job destroyer? Past revolutions have in fact brought increased productivity and resulted in net job creation. Of course, the nature of work has constantly evolved over time; some tasks have been delegated to technology while new tasks have emerged. It is still unclear what tasks AI will create, but current trends predict some jobs are reasonably “safe” in the short term, particularly those requiring:
? Extensive human contact
? Social skills
? Strategic and creative thinking
? Being comfortable with ambiguity and unpredictability.
It is common to view new technolo
gies as competitors rather than complementary to humans, especially amidst growing fear that AI threatens our employment. The reality is that machines are better than us at crunching numbers, memorizing, predicting, and executing precise moves; robots relieve us of tedious, dangerous and physically demanding tasks. A study has even shown that computer-based personality judgements are far superior to those of humans. Based on “Facebook likes,” a computer-based prediction tool was able to beat a human colleague after just 10 likes. It needed 70 likes to beat a friend or roommate, 150 to beat a family member, and 300 to beat a spouse. But AI cannot (yet) replace humans when creativity, perception and abstract thinking are required. Hence, AI systems can serve as partners that can augment and improve many aspects of work and life. Table 1 provides examples of products and AI-related technologies and their potential relevant industries. In a data-centric world, these systems can synthesize tons of information and help us make better-informed decisions. They can also free up time that we can then spend doing what is valuable to us.
In this context, Universal Basic Income is often cited as a way to redistribute the benefits of these technologies, but it is still unclear how that would be funded.
Table 1: Examples of technologies, products and industries
Opening the AI black box
As more and more tasks and decisions are delegated to algorithms, there is growing concern about where responsibility will lie. For example, who is responsible when an algorithmic system, initially implemented to improve fairness in employee performance assessment, ends up reinforcing existing biases and creating new forms of injustice? And who is accountable for the algorithmic decisions when human lives are at stake, as in recent accidents involving self-driving cars? Should we differentiate between decisions taken by an AI vs. a human being? Humans are not expected to justify all their decisions, but there are many cases where they have an ethical or legal obligation to do so.
And that is where the shoe pinches. Advanced algorithms can be so complex that even the engineers who created them do not understand their decision-making process. Consider deep neural networks, a type of machine learning method inspired by the structure of the human brain. All you do is feed the algorithms with some inputs and let the algorithm figure out the output. We have no idea what goes on in between. As illustrated in Figure 2, there are many different pathways that could lead to the outcome, and most of the “magic” happens in the hidden layers. Moreover, this “magic” could even imply a process of information that is completely different from that of the human brain. A famous illustration of this reality is Facebook’s experience with negotiating bots, which, after several rounds of negotiations, realized that it was not necessary to use a human language to bargain.
Coming back to the question of algorithmic accountability: How do we assess the trustworthiness of algorithmic decisions when the algorithm’s decision-making process is a black box? What kind of technical/legal/policy-oriented mechanisms should we implement as a solution? A straightforward option is to design these algorithms so that their “thought process” is “human-readable.” If we could understand how these algorithms make their decisions, we could also potentially adjust their “thinking” to match humans’ legal, moral, ethical and social standards, thus making them accountable under the law.
Deep neural network
From narrow to general AI
So far, our discussion on AI has focused on “narrow AI,” which is specialized by design to perform a specific task. But what about artificial general intelligence (AGI), which could perform any cognitive task as well as a human. What would that look like? Would it have its own character/emotions? Imagine this AGI has been trained by looking at all human histories. Have we always been kind to each other? Have we always treated people equally? If this machine’s input is our history, why would it behave differently to us? Imagine we simply asked an AGI to calculate the number pi. What would prevent this machine from killing us at some point to create a more powerful machine to calculate pi (i.e. to carry out our instruction)? After all, in creating past civilizations, most humans did not really care about the ants they killed on the way, so why would an AGI care about humans and their rules? “Just pull the plug,” we hear you say. But if the AGI is smarter than you, it will have anticipated that and found a way around it – by spreading itself all over the planet for example. For now, this question is, of course, highly philosophical. Max Tegmark, author of the book Life 3.0 identified the following schools of thought depending on one’s opinion on what AGI would mean for humanity and when (if ever) it comes to life:
? The Techno Skeptics: The only type that think AGI will never happen.
? The Luddites: Strong AI opponents who believe AGI is definitely a bad thing
? The Beneficial AI Movement: Harbor concerns about AI and advocate AI-safety research and discussion in order to increase the odds of a good outcome.
? The Digital Utopians: Say we should not worry, AGI will definitely be a good thing.
Where would you situate yourself?
So, what next?
The question is not whether you are “for” or “against” AI – that’s like asking ancestors if they were for or against fire. (Max Tegmark)
Throughout history, we have kept our technologies beneficial. These new technologies will be part of our daily lives, so the best strategy is to be proactive and learn how to control and manage them. And who knows, maybe AI will soon be your colleague.
There is no doubt that we produce more data in a day than we did in decades of history. We most likely don’t even realize that we produce such a large amount of data simply by browsing on the Internet, so you will be surprised. Keep an eye out for the future trends in Big data analytics and you won’t be caught off guard by future technologies.
Over the past decade, global data has been growing exponentially, and it continues to do so today. It is mainly aggregated via the internet, including social networks, web search requests, text messages, and media files. IoT devices and sensors also contribute huge amounts of data propelling Big data analytics trends.
Throughout various industries, Big data has evolved significantly since it first entered the technical scene in the early 2000s. As Big data has become more prevalent, companies must hire experts in data analytics, capable of handling complex data processing to keep up with the latest trends in Big data analytics.
Data fabric
On-premises and cloud environments are supported by data fabrics, which provide consistent functionality across a variety of endpoints. Using Data Fabric, organizations can simplify and integrate data storage across cloud and on-premises environments, providing access to and sharing of data in a distributed environment to drive digital transformation & new trends in Big data analytics.
Through a data fabric architecture, organizations are able to store and retrieve information across distributed on-premises, cloud, and hybrid infrastructures. Enterprises can utilize data fabrics in an ever-changing regulatory environment, while ensuring the right data is securely provided in an environment where data and analytics technology is constantly evolving.
As opposed to being generated by real-world events, synthetic data is information created artificially. Synthetic data is produced algorithmically, and it can be used as a substitute for production or operational data as well as to validate mathematical models and, more often than not, to train machine learning algorithms.
As of 2022, more attention is being paid to training machine learning algorithms using synthetic data sets, which are simulations generated by computers that provide a wide variety of different and anonymous training data for machine learning algorithms. In order to ensure a close resemblance to the genuine data, various techniques are used to create the anonymized data, such as general conflicting networks and simulators.
Although synthetic data concepts have been around for decades, they did not gain serious commercial adoption until the mid-2000s in the autonomous vehicle industry. It is no surprise that synthetic data’s use in autonomous vehicles began there. It is often the sector that is catalyst for the development of foundational technologies like synthetic data because it attracts more machine learning talent and investment dollars than any other commercial application of AI, further accelerating Big data analytics and the future of marketing and sales.
AI developers can improve their models’ performance and robustness by using synthetic data sets. In order to train and develop machine learning and artificial intelligence (AI), data scientists have developed efficient methods for producing high-quality synthetic data that would be helpful to companies that need large quantities of data.
Data as a service
Data was traditionally stored in data stores, which were designed for particular applications to access, however, when SaaS (software as a service) gained popularity, DaaS was a relatively new concept. As with Software-as-a-Service applications, Data as a Service uses cloud technology to provide users and applications with on-demand access to information, regardless of where the users or applications are located.
In spite of the popularity of SaaS for more than a decade, DaaS has only recently begun to gain broad acceptance. The reason for this is that generic cloud computing services were not originally built to handle massive data workloads; instead, they were intended to host applications and store data (instead of integrating, analyzing, and processing data).
Earlier in the life of cloud computing, when bandwidth was often limited, processing large data sets via the network was also challenging. Nonetheless, DaaS is just as practical and beneficial as SaaS today, thanks to the availability of low-cost cloud storage and bandwidth, combined with cloud-based platforms designed specifically for managing and processing large amounts of data quickly and efficiently.
Active Metadata
The key to maximizing a modern data stack lies in the enrichment of active metadata by machine learning, human interaction, and process output. In modern data science procedures, there are several different classifications of data, and metadata is the one that informs users about the data. To ensure that Big data is properly interpreted and can be effectively leveraged to deliver results, a metadata management strategy is essential.
A good data management strategy for Big data requires good metadata management from collection to archiving to processing to cleaning. As technologies like IoT, cloud computing, etc., advance, this will be useful in formulating digital strategies, monitoring in the purposeful use of data, & identifying the sources of information used in analyses to accelerate the Big data analytics future scope. Data governance would be enhanced by the use of active metadata, which are available in a variety of forms.
Edge Computing
This term describes the process of running a process on a local system, such as the system of a user, an IoT device or a server, and moving that process there. Edge computing allows data to be processed at the edge of a network, reducing the number of long-distance connections between a server and a customer, making it a major trend in Big data analytics.
This enhances Data Streaming, such as real-time data streaming and processing without causing latency; devices respond immediately as a result. Computing at the edge is efficient because it consumes less bandwidth and reduces an organization’s development costs. It also enables remote software to run more efficiently.
Many companies use edge computing to save money alone, so cost savings are often the driving force for their deployment. In organizations initially embraced the cloud, bandwidth costs may have been higher than anticipated, and if they are looking for a less expensive alternative, edge computing might be a good fit.
In recent years, edge computing has become increasingly popular as a way to process and store data faster, which can allow companies to create more efficient real-time applications. The facial recognition algorithm would have to be run through a cloud-based service if a smartphone scanned a person’s face for facial recognition before edge computing was invented, which would take a lot of time and effort.
Hybrid clouds
With the orchestration of two interfaces, a cloud computing system combines a private cloud on-premises with a public cloud from a third party. With hybrid cloud deployment, processes are moved between private and public clouds, which allows for great flexibility and more data deployment options. For an organization to be adaptable to the aspired public cloud, it needs a private cloud.
This requires building a data center, which includes servers, storage, a LAN, and load balancers. VMs and containers must be supported by a virtualization layer or hypervisor. A private cloud software layer must also be installed, enabling instances to transfer data between the public and private clouds through the implementation of software.
A hybrid cloud setup uses traditional systems as well as the latest cloud technology, without a full commitment to a specific vendor, and adjusts the infrastructure accordingly. Businesses work with a variety of types of data in disparate environments and adjust their infrastructure accordingly. The organization can migrate workloads between its traditional infrastructure and the public cloud at any time.
Data center infrastructure is owned and operated by an organization with a private cloud, which is associated with significant capital expenditures and fixed costs. In contrast, public cloud resources and services are considered variable and operational expenses. Hybrid cloud users can choose to run workloads in the most cost-effective environment.
Data service layer
An organization’s data service level is critical to providing data to customers within and across organizations. Real-time service levels enable end-users to interact with data in real-time or near-real-time changing the Big data analytics future scope.
In addition to providing low-cost storage to store large quantities of raw data, the data lakehouse system implements the metadata layer above the store in order to structure data and improve data management capabilities similar to a data warehouse. A single system lets multiple teams access all company data for a variety of projects, such as machine learning, data science, and business intelligence, using one system.
Data mesh
An enterprise data fabric is a holistic approach for connecting all data within an organization, regardless of its location, and making it accessible on demand. A data mesh, on the other hand, is an architectural approach similar to and supportive of that approach. With a data mesh, information about creating, storing, and sharing data is domain-specific and applicable across multiple domains on a distributed architecture.
Using data mesh approaches is a great way for businesses to democratize both data access and data management by treating data as a product, organized and governed by experts. Taking a data mesh approach is a great way to increase scalability of the data warehouse model as well as democratize both data access and data management.
Natural language processing
Among the many applications of artificial intelligence, Natural Language Processing (NLP) enables computers and humans to communicate effectively. It is a type of artificial intelligence that aims to read and decode human language and create meanings. The majority of the software developed for natural language processing is based on machine learning.
By applying grammar rules, algorithms can recognize and extract the necessary data from each sentence in Natural Language Processing. The main techniques used in natural language processing are syntactic and semantic analysis. A syntactic analysis takes care of sentences and grammatical problems, whereas a semantic analysis analyzes the meaning of the text or data.
XOps
A key objective of XOps (data, machine learning, model, platform) is to optimize efficiency and achieve economies of scale. XOps is achieved by adopting DevOps best practices. This will reduce technology, process replication, and automation, ensuring efficiency, reusability, and repeatability. These innovations would allow prototypes to be scaled, with flexible design and agile orchestration of governed systems.
A growing number of algorithms for solving specific business problems is being deployed as AI continues to increase, so organizations will need multiple algorithms for attacking new challenges. By removing organizational silos to facilitate greater collaboration between software engineers, data scientists and IT staff, companies can effectively implement ModelOps and ensure it becomes an integral part of AI development and deployment.
As the name implies, Big data refers to a large amount of information that needs to be processed in an innovative way to improve insight and decision-making. With the use of Big data technologies, organizations can gain insight and make better decisions, leading to greater ROI for their investments. It is critical to understand the prospects of Big data technology, however, to decide which solution is right for an organization given so many advancements.
Organizations that use data-driven strategies are those that succeed in today’s digital age and are looking to invest in data analytics. As a result of digital assets and processes, more data is being gathered than ever before, and data analytics is helping businesses shape themselves. Here are the latest trends in Big Data Analytics for 2022 and beyond.
? Discover more about Professional Big data engineers
? Explore our Big Data Consulting Services and Solutions
Data analytics: questions answered
What are the future trends in data analytics?
AI and machine learning are being embraced heavily by businesses as a means of analyzing Big data about different components of their operations and strategizing accordingly. This is especially the case when it comes to improving customer service and providing a seamless customer experience.
What will be the future of Big data industry?
The future of Big data may see organizations using business analytics to create real-world solutions by combining analyses from the digital world with the analyses from the physical world.
What is the next big thing in data analytics?
Using artificial intelligence, machine learning, and natural language processing technologies, augment analytics automates the analysis of large amounts of data for real-time insights.
What is the next big thing after Big data?
Several sources claim that Artificial Intelligence (AI) will be the next big thing in technology, and we believe that Big Data will be as well.
What are the top trends of data analytics ?
? AR; VR
? Driverless Cars
? Blockchain
? AI
? Drones.
What are the key data trends ?
? Using Big data for climate change research
? Gaining traction for real-time analytics
? Launching Big Data into the real world
What is the scope of Big data analytics?
In today’s world, there is no doubt that Big data analytics is in high demand due to its numerous benefits. This enormous progress can be attributed to the wide variety of industries that use Big data analytics.
Is Big Data Analytics in demand?
The wide range of industries that are using Big data analytics is undoubtedly a major reason for the growth of the technology.
What are the critical success factors for Big data analytics?
? Establishing your mission, values, and strategy,
? Identifying your strategic objectives and “candidate” CSFs
? Evaluating and prioritizing them
? Communicating them to key stakeholders
? Monitoring and measuring their implementation.
Data analytics is the process of preparing, processing, mining, and interpreting data to extract valuable insights and empower informed decision-making in business.
Big data has become an indispensable tool for businesses, but it also means that organisations now face an unprecedented deluge of information. While this immense volume of data holds the potential to unlock valuable insights, managing such vast and complex data sets can be an arduous task.
This is where the integration of artificial intelligence (AI) and machine learning algorithms in data management comes into play.?
What is big data?
Big data refers to the massive amounts of structured, semi-structured, and unstructured data generated from various sources, such as:
The characteristics of big data are often defined by the 3Vs:
What is data management?
Data management is the process of collecting, storing, organising, and analysing data. It’s an important area of data science, especially within businesses.
Traditional database management approaches, however, often involve human intervention in handling and processing the data. These methods are difficult to apply to big data management due to the sheer volume and complexity of information.
This is where artificial intelligence steps in.
How is AI used in data management?
AI’s ability to process, analyse, and draw actionable insights from large amounts of data is reshaping data management – and businesses that leverage AI effectively can gain a competitive advantage, unlocking unprecedented opportunities for growth and innovation.
This is because artificial intelligence algorithms enable systems to learn from data patterns and even make data-driven decisions with minimal human intervention.?
Key applications of AI in data management include:
The advantages of using AI for big data management
There are many reasons to use artificial intelligence in big data management. These include:
What are the challenges of using AI for big data management?
Artificial intelligence is a powerful tool, but it’s not without challenges. These can include:
The future of data management and artificial intelligence
The future of data management is intertwined with artificial intelligence, and as AI technology continues to advance, its ability for handling big data will become even more sophisticated.?
Areas of potential future developments may include:
Surviving in the market pushes many organizations to master data analytics trends as they promise higher efficiency and offer growth-hacking scenarios. This evokes vigorous interest in the value of data analytics as a prerequisite for optimization in enterprise management, identifying business opportunities, and driving organizational change in the right direction.
With the help of data analytics, business executives can achieve the following effects:
Find data patterns and trends in the business metrics and outcomes (like seasonal fluctuations in sales) and make pattern-based predictions;
Identify business bottlenecks and shape strategies to overcome them;
Determine normal and superfluous levels for different operational metrics to formulate what’s good and what’s bad in more precise and reality-focused terms;
Build complex correlative models that describe exact relationships between factors/causes and business outcomes;
Define data abnormalities and phenomena in business operation results;
Turn all findings and conclusions into efficient business management reactions, methods, and decisions.
Why is it necessary to embrace the modern trends in data analytics for business success? There are several reasons for popularizing data analytics:
Informed decisions are confirmed to be more efficient than pure reliance on intuition or professional experience. This is because data analytics excludes different emotional factors and cognitive distortions specific to humans;
The wide availability of powerful computers and mobile devices;
A wide selection of data analytics applications and methods;
The possibility for business data to be accumulated in enterprise systems;
Decreased requirements for mathematical skills in users.
The last point is extremely important, as modern data analytics can run automatically or semi-automatically. Thanks to auto-generated analytical reports and takeaways delivered by modern software products, business managers and executives don’t need to possess advanced statistician skills to perform data research.
The latest techniques and trends in analytics include machine learning, artificial intelligence, big data, data governance, and data visualization. All these methods can be used either alone or in combination. Let’s learn more about each of them.
Can ML Technology Learn as Organic Brains Do?
The data analytics future can hardly be imagined without machine learning (ML.) This methodology of modern data science involves the use of intelligent algorithms and statistical matrix models empowering computer systems to “learn” and accumulate experience to improve their performance on a specific task or problem.
ML role in data analytics
The basic idea behind ML is to let machines acquire experience automatically instead of being explicitly programmed. ML replicates natural learning abilities specific to all living beings. This requires a machine “training” routine: the computer consumes large amounts of data and learns from them according to certain instructions.
After many iterations of learning, the machine becomes able to recognize patterns and relationships in this data. Once the machine spots certain data patterns, structures, or relations on a new data set, it can generate warnings, predictions, and/or make decisions based on prior experience.
Illustration of office life
Machine learning is highly popular in the following industries:
Image and speech recognition;
Natural language processing;
Predictive analytics;
Fraud detection;
Healthcare;
Finance;
Manufacturing;
Robotics.
Most popular ML data analytics trends & examples
Here are some of the latest advances in machine learning technologies and how they are used to improve data analytics in business and IT:
Deep learning: This method involves neural networks that learn from vast amounts of data before they mature. It is particularly effective for analyzing unstructured data, such as images, audio, and text. Depending on the organization of deep learning results, the ML solution can be used for reproducing or imitating the data it was taught, including creative processes similar to those of the human brain.
Transfer learning: This ML technique enables ML-based applications to reuse the knowledge learned from one task to amplify performance on another task. The major advantage of the transfer learning method is reducing the amount of data required by neural networks for training. For more accurate results, it’s necessary to involve skilled ML engineers who can curate the process of transfer learning in ML.
Reinforcement learning: This ML paradigm is based on the natural principle of rewarding desired behaviors and punishing undesired ones. This type of machine learning involves an agent (usually, a human operator) interacting with an environment to learn through trial and error. This practice is being actively used in areas such as robotics, gaming, business operation automation, and transportation & logistics.
AutoML: This approach covers several techniques and tools that automate machine learning operations, from data collection and preparation to ML model selection, including a very specific ML process called hyperparameter tuning. AutoML can help businesses with limited resources to embrace the power of machine learning more effectively. However, the AutoML environment must be designed and deployed by experienced ML engineers, so it can deliver the best results possible.
Federated learning: As the term suggests, this is a distributed, collaborative machine learning model that allows multiple devices or applications to cooperate on an ML task without sharing their specific data and violating its privacy. It is particularly efficient in situations where data security is a primal concern, like healthcare, law practices, or advanced financial technologies.
In other words, ML is required to prepare and train the “electronic brain” for augmented, automated decision-making. Machine learning is a necessary prerequisite for the successful functionality of AI systems. The better a machine is trained on big volumes of valid data, the better advice and insights it can give you in the future.
Successful examples of ML projects include two famous products: MidJourney and ChatGPT. If you want to create an ML-based system or application for fin-tech, eLearning, entertainment, logistics, or any other industry, make sure to contact the Forbytes ML team for a consultation.
Is AI Capable of Replacing Human Professionals?
While an ML mimics the process of organic learning, artificial intelligence (AI) is designed to imitate the process of organic thinking. The goal of AI is to teach a computer to replace human reasoning with similar automated processes and let a machine perform certain tasks on its own, without constant human intervention. Most of the AIs are based on neural networks, which are architected after the biological capabilities of the human brain.
on illustration people in data center
Thanks to rapid electronic processes and high-capacity microelectronics, AI solutions can seriously accelerate many tasks traditionally performed by humans, yet not replace professionals completely. In other words, AI for data analytics allows human decision-makers to do more in less time and process tremendous volumes of information, which previously required many hours of manual operation and big numbers of costly specialists.
Here are some of the latest advances in data analytics and AI application examples:
Natural language processing (NLP): talking to robots
With the help of ML and other related techniques, AI can be trained to understand human language, either written or spoken, including emotional tones and hidden meanings. NLP methods empower AI to analyze large arrays of unstructured (user-produced) data automatically, such as social media posts, customer letters, and support tickets. This may include automated answers or other AI-triggered reactions.
For example, the elevation of certain crucial or complicated requests to human operators. NLP and AI can help companies enhance their chatbots and virtual assistants to interpret and answer human messages more organically instead of through hardwired algorithms. The NLP method is widely used in innovative marketing practices for measuring brand reputation, “vox populi” research, and finding out other important customer metrics.
CTA Forbytes
Computer vision: Big Brother is watching you
This is another important trend in AI engineering and development. It enables computers to interpret and investigate visual data in areas such as video surveillance, self-driving cars, facial recognition, and quality control in manufacturing.
Computer vision is also widely used to analyze optical data from the web (like user-uploaded photos) and satellite images to gain insights into a great variety of realms: from geospatial research or ecology situation monitoring to studying enemy military unit disposition or customer interests. Computer vision can also be integrated into modern gaming apps.
Generative adversarial networks (GANs): helpful discrimination
Technically speaking, GAN belongs to the AutoML methods, however, it is AI-driven and used by computers to self-educate. GANs are two opposing neural networks where one is a generator and another is a discriminator. While the first one generates synthetic data objects, the second one tries to distinguish them from real data samples.
In this way, both neural networks compete with each other in a game-like manner. This method is common for Augmented Reality projects and can be used in AR app development for business.
Predictive analytics: AI tells your fortune
Humanity has always been dreaming about an ideal oracle who can flawlessly tell one’s fortune. Finally, AI holds a true chance to become an unbiased and efficient predictor. Thanks to a wide toolset of machine learning algorithms including data fabrics, AI technology is potent to analyze data and make quite precise data-based predictions about future events and outcomes. Predictive analytics can help business executives optimize their business operations, improve sales, customer experience, and mitigate potential organizational risks before they ever emerge.
If you want to develop any of these solutions in data analytics and AI domains, make sure to contact our AI application development engineers. We’ll accurately learn the requirements of your project and provide you with an efficient project conception. Our professional approach to AI system architecture will let you shortly obtain a system design and implementation plan for cost-efficient data analytics, educated business predictions, and more.
Big Data Analytics & Trends
Big data is a specific type of business or technical data that features the following key characteristics:
Enormous volume: Big data sets are typically huge, with database sizes ranging from terabytes to petabytes, on average.
Velocity in collection: Big data sets can be generated and captured at a high pace, often in real-time or in nearly real-time modalities, like a high-resolution video being streamed by dozens and hundreds of cameras.
Variety and inconsistency: Big data sets can include structured, semi-structured, and unstructured data arrays.
Veracity: Big data sets can be raw, noisy, and contain a lot of errors, artifacts, or uncertainties, which makes it challenging to process and validate.
The data warehouses used to collect big data are called “data lakes.” These huge storages mostly include raw data collected from smartphones, cameras, sensors, medical devices, industrial robots, etc. Here are some of the latest trends in big data analytics applications:
people in data center
Cloud computing: big data for everyone
Cloud technology enables businesses to store and operate large amounts of data on remote servers in third-party data centers, which is more cost-efficient than keeping corporate infrastructure onsite. Cloud computing is quite an affordable strategy that can be used by many small & medium organizations to access, store, and manage big data.
Thanks to easy accessibility, a wider range of organizations can reserve cloud capacities and make use of real-time data analysis. This trend stimulates the explosive development of SaaS analytical platforms and custom solutions, like SaaS solutions for hotels, medical practices, e-commerce, and more industries or business sectors. If you want to migrate your databases or applications to the cloud, contact our IT engineers for a consultation on bid data and cloud options.
Internet of Things (IoT): how they make big data
IoT is a network of interconnected devices and sensors that can collect and exchange data to coordinate their operations. IoT hardware of higher layers (hubs) synchronizes and collects data from smart cameras, wearables, robotic gear, and other connected devices in real time. These IoT device networks can generate considerable amounts of big data.
Since it’s very large and poorly structured, it’s necessary to send this data to the cloud and apply multiple algorithms to clean, normalize, aggregate, and prepare it for use in analytics. Examples are facial recognition or dynamic GIS navigation for unmanned vehicles.
Sometimes, this process has to be executed very fast, so cloud computing connected to data lakes can be the right solution. Data analytics in IoT, supported by cloud platforms, helps to improve and orchestrate supply chains, autonomous (unmanned) transport routing, self-reliant manufacturing processes, and device user experiences.
Data Visualization: The Future of Data Analytics Born in the Past
Data visualization techniques are not new and have been out there for centuries. However, in the previous epochs, they required well-developed math and graphical skills to be applied.
Nowadays, most data visualizations can be created with a simple click or two. This approach enables organizations to represent and communicate complex information in a comprehensive and engaging way, through pictures, infographics, graphs, charts, and so on. Here are some of the latest techniques in data visualization:
Interactive visualization: just click to get an insight!
This type of data visualization describes software interfaces that enable users to dig into databases, explore records, and interact with data via dashboards. Users can filter, sort, and zoom in on data to gain a better understanding of patterns and trends. Interactive visualization can include numerous types of charts and graphs that can be immediately built at the user’s request.
Any person with at least an intermediate level of education, who can read graphs, can make great gains from using such data analytic interfaces. Interactive visualizations can be an integral part of any popular product or user access portal providing overviews of user activities, like purchase history in e-commerce applications.
Creating efficient interfaces takes serious UI/UX skills — if you need any help with UI design, please consider our user interface development services.
Data storytelling: once upon a time…
Data storytelling is the use of narrative techniques and artistic scenarios to convey insights and tell a story through creative data visualizations. Data storytelling can include rich representations that go far beyond “slide shows” or motion graphics. That could be a short movie or a commercial where you showcase personal stories and amplify your narratives with powerful visual content.
This type of data analytics & visualization works well with specific target audiences: investors, potential consumers of premium goods, politicians, media representatives, and more. Data storytelling helps engagingly highlight important information and persuade stakeholders to take action based on beautifully represented data insights (for example, stimulate them to invest in your new project.)
Augmented reality visualization: breakthrough in eLearning
Augmented reality (AR) visualization uses digital overlays to display virtual data in the context of a real-world environment to boost the organic experience with computer-generated objects. Augmented reality visualization is being used to associate data analytics and representation with real settings. This enables users to envision information in a more immersive and interactive way.
AR can be extremely helpful when it comes to e-learning software development. It can work with special AR devices like smart glasses or usual smartphones and other handheld devices.
Data Governance and Best Practices in Trend Data Analysis
Data governance refers to the policies, best practices, and procedures that organizations must use to manage their data assets and prevent serious violations.
Privacy regulations
There exist numerous laws that you must obey when doing your data analytics and AI/ML research. Just to name a few: CCPA, HIPAA, GDPR, and many more. They are designed to prevent data breaches and eliminate concerns around data privacy.
According to these regulations, you must obey serious constrictions in operations with personally identifiable data. For example, you must anonymize all demographic data before sharing it with, let’s say, your marketing analytics providers until you obtain explicit consent from the owner and follow many more requirements.
Data quality management and stewardship
Data quality management ensures that data is accurate, complete, and consistent. In addition to this, data stewardship is the practice of assigning responsibility for managing data assets to specific executives within an organization. Both practices are required to improve operations with protected or sensitive data and ensure that no data breaches can happen (remember that legal penalties for data security violations can be very painful.)
25 use cases & examples of real-time analytics
Today’s businesses rely on data analytics more than ever before, and to keep up with the competition, it’s necessary to gather, analyze, and take action on information the moment it’s received — while interactions and conversations are still happening. This blog offers 25 real-time analytics examples to illustrate how this business intelligence technology is impacting practically every facet of modern life. We’ll discuss real-time analytics use cases such as:
? Real-time credit scoring
领英推荐
? Predictive equipment maintenance
? Route optimization
? Next best action and next best offer
? Dynamic pricing
? Omnichannel marketing
? Medication adherence
? Wildlife conservation
? …and more
We’ll discuss these and other real-time analytics examples later in this article. First, let’s review what real-time analytics is and how it works.
Whitepaper
The Power of Real-Time Conversation Analytics and Agent Guidance
Learn how conversation analytics can deliver real-time guidance
What is real-time analytics?
You have certainly encountered something in life that’s driven by real-time analytics — the process of capturing, analyzing, and acting on data in the moment, as it happens — but you may not have realized it at the time. The real-time element of real-time analytics refers to the immediacy with which data is analyzed when it’s received. The analytics element of real-time analytics involves aggregating data from various sources, interpreting it, and transforming it into something actionable that humans can understand and take action on.
By analyzing data the moment it’s collected – such as a conversation in a contact or customer service center – businesses can take immediate action to drive outcomes, seize opportunities, and even identify and resolve problems before they actually happen.
Advances in technology now enable businesses to collect massive volumes of data from many disparate sources, leveraging powerful analytical tools to combine the information and apply algorithms and logic, artificial intelligence (AI), and machine learning (ML) to produce actionable and contextual insights mere moments after data is received. Done right, businesses can gain a competitive advantage by extracting value from their data as it’s being collected.
Real-time analytics is used by every business sector, from manufacturing to healthcare, marketing, public safety, and customer service. It’s also used in many business processes, from raw materials sourcing to production planning, logistics, and customer service. It’s even used to identify new untapped markets and inform new product development and innovation.
For example, real-time analytics is used to analyze conversations in a contact center, identifying signals that the conversation is heading in a positive or negative direction and providing immediate, in-call guidance to help agents steer those interactions in the right direction. It’s also used to analyze consumer behaviors and interactions to identify signs that a customer is about to churn, giving businesses the opportunity to take action and right the ship before losing a customer to the competition.
Examples of real-time analytics in action
Now that you have an idea of what real-time analytics is and how it works, you can gain a deeper understanding of this technology by learning about its many use cases across various industries. Keep reading to learn some of the many ways real-time analytics is being used to shape various facets of modern life.
Real-time analytics examples in financial services
1. Regulatory compliance. “The mortgage industry is highly regulated, both at the federal and state level. This means that certain disclosures must be communicated to consumers. And when that doesn’t happen, it doesn’t just erode consumer trust, it can result in serious fines. The right technology can help mortgage lenders understand how and when the right (or wrong) disclosures are being said across 100% of consumer communications. Particularly in the cases where required disclosures aren’t being said, mortgage lenders can find those interactions and mitigate the issue before audits. They can also better coach and train employees who are regularly missing the required statements. Meeting compliance regulations and reducing risk benefits your bottom line, which is always important, but even more so as the industry changes.” - Chris Stanley, Four key conversation intelligence use cases in the mortgage industry, CallMiner; Twitter: @CallMiner
2. Credit scoring. “For example, Equifax introduced machine learning modeling (neural network) into an explainable artificial intelligence credit score method to generate actionable explanations that are tailored to individual consumers. Equifax is not the only bureau dabbling in machine learning solutions. Experian augmented their analytics tools with machine learning functionalities to generate deeper, on-demand insight. TransUnion and FICO also incorporated machine learning to spot high risk identity behaviors and generate more accurate, understandable scorecards for credit applications. The more recent VantageScore uses machine learning to assess risks and assign scores, even for ‘credit invisible’ consumers without recently updated credit files. Other bureaus such as Creditinfo are working on machine learning model generation platforms.” - Nan Jiang and Nadia Novik, Leveraging big data and machine learning in credit reporting, World Bank Blogs; Twitter: @WorldBank
3. Financial trading. “In rapidly changing capital markets, it is no longer adequate to measure risk as an end of day process. Trading decisions can significantly alter exposures in a millisecond as traders with exposures to Bear Stearns found out the hard way in March 2008. In order to assess risks to market portfolios and take corrective measures in real-time, capital markets are now moving towards intra-day value at risk computations.
“Streaming analytics can be leveraged to support these risk computations and aide banks to minimize and manage risk. With streaming analytics, banks can obtain a low latency, high-performance solution that listens to market prices as well as real-time changes to portfolios and compute value at risk on the fly. By employing risk calculations in a streaming fashion, financial institutions can stay several steps ahead of its competition by ensuring that portfolios are safe from intraday market fluctuations.” - Seshika Fernando, Real-Time Analytics in Banking & Finance: Use Cases, WSO2; Twitter: @wso2
4. Detecting and blocking fraudulent transactions. “Catching fraud can be done by leveraging real-time analytics. Prominent banks and capital markets have started to deploy real-time analytics, in addition to machine learning, for risk management, fraud detection, compliance, consumer metrics, and, of course, to distinguish themselves from the competition. Artificial intelligence has great potential when it comes to reducing financial fraud. A great many practical ideas involving AI, in particular, machine learning, have been developed. Speaking of machine learning, it’s getting better and better at identifying potential cases of deception. Machine learning makes it possible to establish which transactions are likely to be fraudulent and to reduce false positives.” - Kristijan, How Real-Time Analytics Tackles Fraud Detection, The Future of Things; Twitter: @Future0fThings
Real-time analytics examples in manufacturing & logistics
5. Responsive processes in transport logistics. “There is a growing need for better visibility along transportation lines in the industry. Shipping managers are changing how they manage the lines to ensure better output. Real-time freight analytics is mandatory from the starting point to the final destination. Managers can use the continuous flow of real-time data to identify any gaps and inefficiencies and correct them quickly.
“At the same time, the data systems provide detailed and more regular updates, which results in automatic updating and notifying all the concerned parties. The supply chain can be made fully disruption-proof by providing an automated and responsive process that works right through real-time freight analytics.” - Why Real-Time Data Is Important In Shipping And Logistics,
6. Predictive maintenance. “Predictive maintenance analytics is a tool that enterprises in the industrial sphere can use to better anticipate equipment failure and avoid unnecessary downtime. Predictive maintenance analytics marries real-time equipment data with data analytics and machine learning to evaluate current and future equipment performance. By monitoring equipment conditions in real-time, companies can gain a deeper understanding of equipment status to identify factors that may indicate a malfunction is about to occur. In turn, companies can utilize this information to develop a data-driven maintenance strategy and reduce equipment downtime to improve productivity.” -
7. Inventory management. “Predictive analytics is a subset of advanced analytics. In most cases, the term is used interchangeably with machine learning. A large quantity of data about a given question is collected and an answer is spit out. Take this example:
? Based on all of the data about this customer, they will unsubscribe within 3 months.
? Based on all of the data about your product and the market, you should focus your marketing efforts on new moms in urban areas who hold advanced degrees.
? Based on all of the data about this account holder, their behavior indicates they will or have already committed fraud.
“Naturally, this extends to inventory management. Based on what we know about this product, online interactions, and your historical data, demand will go up by 40 percent over the next quarter.” - Real Time Warehouse Inventory Management Software Tools, Logiwa;
8. Production planning. “Real-time analytics drive overall equipment effectiveness (OEE), which is one of the most powerful measurement tools in an industrial environment. It exposes all manufacturing losses, so automation professionals can make objective business decisions that improve the performance, capacity, and utilization of plant assets. It can help companies achieve desired outcomes, such as reduced changeover time, improved quality and throughput, greater supply chain predictability, and reduced costs. When measured and adopted correctly, OEE allows managers to make effective, accurate, and objective decisions in real time.” - Jennifer Bennett, How to Use Real-Time Analytics to Achieve Operational Excellence, ISA Interchange; Twitter: @ISA_Interchange
9. Fleet management and driver safety. “Fleet management data analytics is based on a variety of data types, such as telematics data, data from various cloud or edge devices, GPS, vehicle cameras, traffic cameras, and driver monitoring applications. The collected data can reveal the most common causes of accidents and give insight into how to avoid them. For example, if the major cause of accidents is risky driving behavior, the problem can be addressed with a suitable training program. Or, if accidents are being caused by the vehicles themselves, fleet managers can predict accidents before they occur and prevent them from happening through predictive maintenance.” - How to Use Predictive Analysis in Fleet Management?,
10. Route optimization. “It goes without saying that route optimisation is essential to finding cost efficiencies in transportation while maintaining margins. You’re probably already using a routing system to calculate where you’re going. Big Data does this based on shipment data, traffic situations, weather, holidays, delivery sequences and other factors.
“Intelligent route optimization also plays a crucial part in the case of determining which vehicles to choose over possible routes and junction points in order to optimize the flow throughout the chain in terms of cost and time. Truly intelligent solution optimises many hundred thousand master route stops in one optimisation. Second by second, it optimises highly dynamic and real-time-based routes.” - Wim Hoek, The impact of Big Data on route planning,
Real-time analytics examples in retail & eCommerce
11. Omnichannel experiences. “For many retailers, there is not enough time or resources to invest in integrating and unifying their data across all customer interaction channels.
“Even so, siloed information and a disconnected CX are top causes of customer concern. Eighty-nine percent of customers report becoming frustrated when they must repeat the issue they already explained in chat or to another agent. To mitigate these risks, leverage technology like AI-powered conversation intelligence to map out and understand the entire customer journey. Customer journey mapping is a critical step toward spanning silos of intelligence and empowering agents with information as customers move between channels.” - Five Critical Trends That Omnichannel Retailers Must Understand,
12. Dynamic pricing. “It’s essential to present competitive prices to maximize revenue without diverting visitors from your retail company.
“Use dynamic pricing to manage offers. It’s a method of adapting prices to the market. Current dynamic pricing relies on historical data, artificial intelligence, and machine learning to determine the best prices for a store. Ride-sharing businesses like Uber and Lyft employ dynamic pricing all the time. Days with unfavorable weather conditions or rush hour affect the service costs to gain additional profits from these conditions.
“eCommerce isn’t an exception. We expect prices to go higher on trendy products, while basic items are usually stable. Below are the most notable retailers with integrated dynamic pricing.” - Alex Husar, How Real-Time eCommerce Analytics Impacts Your Business,
13. Next best offer and next best action. “Personalization is table-stakes for optimal digital experiences. However, many companies implement personalization at the persona or audience level, using information like demographics, website traffic, location data, and other similar attributes. It can work, but it’s not true one-to-one personalization.
“Next-best action is a technique that uses data-driven insights and analytics from marketing, sales, customer service, and other departments to predict the next action brands should take with a consumer. By pulling together data from all interactions across all departments, and analyzing that data using machine learning and AI, a company can more accurately predict the right content, message, or offer a consumer might want or need next.” - Next-Best Action: How To Use AI For Predictive Personalization,
14. Personalized in-store experiences. “According to a 2019 Gartner study, brands risk losing 38 percent of customers because of poor marketing personalization efforts. The beauty of real-time in-store analytics is its ability to easily pinpoint exactly what works and what doesn’t. It takes the guesswork out of how retailers can deliver powerful and meaningful personalization for shoppers by understanding which customers shop at specific zones, the correlation between dwell engagement to dwell conversion and the products actually purchased to create customized shopping experiences. This can help to isolate performance opportunities between products and locations or best practices for floor layouts and in-store designs.” - Judith Subban, Real-Time In-Store Analytics Will Grow Your Business,
Real-time analytics examples in marketing, social media & digital technology
15. Real-time agent guidance. “Conversation analytics and conversation intelligence software, powered by AI, can help you improve the frontline agent experience by providing real-time monitoring and guidance agents can implement to drive positive outcomes from every interaction. Conversation analytics solutions like CallMiner analyze every interaction across multiple channels to give you a better understanding of your customers and what drives their behaviors. With CallMiner, you can create a culture of self-improvement by providing real-time feedback and next-best-action guidance to help them turn negative interactions into great customer experiences.” - Tips & strategies to improve frontline agent experience,
16. Artwork and image selection. “Some say marketing is more art than science. When it comes to the visual imagery that Netflix uses to entice viewers, it’s a marriage of both. Using Artwork Visual Analysis (AVA), ‘a collection of tools and algorithms designed to surface high quality imagery from videos,’ is able to predict which merchandising still will resonate most with individual users based on their age and general preferences.
“Surprisingly complex, AVA uses computer vision to analyze visual data such as composition metadata (heuristic characteristics that make up the images overall aesthetic) and contextual metadata (facial expressions, objects) to drive image selection. In addition to automatically generating thumbnail images for Netflix’s user interface, AVA is also used to select artwork for general marketing and social media campaigns.” - Elizabeth Mixson, Data Science at Netflix: How Advanced Data & Analytics Helps Netflix Generate Billions, AI, Data & Analytics Network;
17. Virtual reality. “According to Facebook, virtual reality (VR) and augmented reality (AR) for their Oculus Quest headsets depend upon ‘positional tracking that [is] precise, accurate, and available in real-time.’ Moreover, the company claims that this positional tracking system must be compact and energy-efficient enough for a standalone headset.
“Meta claims that its ‘Oculus Insight’ (also called its ‘insight stack’) machine learning models leverage the latest computer vision systems and visual-inertial simultaneous localization and mapping, or SLAM. SLAM is used to track the position of the user’s head, while constellation mapping is used to track head movements. Other applications that use SLAM include autonomous driving and mobile AR apps.” - Daniel Faggella, Artificial Intelligence at Meta (Facebook) – Two Current Use-Cases, Emerj Artificial Intelligence Research;
18. Prioritizing social media news feed content. “When picking posts for each person who logs on to Facebook, the News Feed algorithm takes into account hundreds of variables — and can predict whether a given user will Like, click, comment, share, hide, or even mark a post as spam.
“More specifically, the algorithm predicts each of these outcomes with a certain degree of confidence. This prediction is quantified into a single number called a ‘relevancy score’ that's specific both to you and to that post.
“Once every post that could potentially show up in your feed has been assigned a relevancy score, Facebook's sorting algorithm ranks them and puts them in the order they end up appearing in your feed. This means that every time you log in, the post you see at the top of your News Feed was chosen over thousands of others as the one most likely to make you react and engage.” -
Real-time analytics examples in healthcare
19. Benefit verifications and prior authorizations. “A pion, CVS Health has long leveraged RPA, AI and other business process automation tools to optimize its support functions. For example, Using a combination of AI, RPA, machine learning, data analytics and natural language processes (NLP), CVS Health was able to automate its prescription intake, benefits administration and revenue cycle management processes.
“As CEO Karen Lynch explained in a August 2021 earnings call, ‘Our technology-driven programs are leveraging blockchain, driving cloud migration, and intelligent automation, and streamlining processes, to accelerate results and generate greater impact. One example is a specialty pharmacy script automation program that uses artificial intelligence to yield better results more quickly, while eliminating more than 30 manual steps, such as benefit verification and prior authorization.’” - Elizabeth Mixson, CVS Health goes from digital transformation to digital optimization, Intelligent Automation Network;
20. Medication adherence. “Specialty therapies are complex and patients often face challenges including side effects or ineffective treatment, which require active monitoring and personalized engagement to ensure patients stay adherent. If the side effects are significant, or if the patient no longer feels they are getting the benefit of the medication, they may stop taking them. The right monitoring to detect if this is happening, and appropriate interventions are critical to saving payors money.
“Our Intelligent Medication Monitoring solution uses data analytics and our digital infrastructure to identify when patients may no longer be benefiting from their treatment and intervene appropriately. Our proactive surveillance enables us to identify gaps in care, and monitor efficacy, symptoms, pain and exacerbations. Given our high level of digital engagement we can adapt our message to members and reach them through a channel of their preference. When appropriate, we can work with providers to deliver targeted interventions, including stopping treatment or changing to a different therapy.” -
21. Faster, more accurate breast cancer screening and diagnosis. “A new set of real-world data from the 3D mammogram developer iCAD showed that its artificial intelligence-powered screening programs were able to increase breast cancer detection rates and help cut down the number of false positives. … Researchers found that after installing the AI system, the average rate of cancers detected per 1,000 patients screened rose from 3.8 to 6.2, compared to the findings of a team of radiologists. False interpretations of results also dropped, from a rate of 9.6% down to 7.3%.
“At the same time, the researchers said the addition of AI helped nearly double the positive predictive value of screenings where biopsies were performed to confirm the disease, from 29% to 57%.” - Conor Hale, iCAD's 3D mammography AI catches more breast cancers in real-world study,
Other real-time analytics examples
22. Weather forecasting. “As HPC-driven [high-performance computing] analytics fuels progress in weather and climate research, many scientists are looking to artificial intelligence (AI) capabilities to analyze data even more quickly and accurately. Deep learning, a subset of AI, leverages a series of trained algorithms that learn to make predictions based on past insights. Deep learning tools are designed to process massive data sets in order to identify patterns, and because learning can be supervised or unsupervised (using algorithms to reach specific answers or learning without a specific answer in mind), scientists can extract critical insights without exhausting their IT resources.
“According to a research paper on deep hybrid models for weather forecasting, IT architectures based on deep learning demonstrated an improved ability to predict the accumulated daily precipitation for the next day. Using supervised learning, the architecture was able to forecast the daily accumulated rainfall at a specific meteorological station, and outperformed all other analytical approaches.” -
23. Wildlife conservation. “Equipped with cutting edge artificial intelligence technology, Wildlife Insights can automatically identify hundreds of wildlife photos in minutes, a task that traditionally takes researchers weeks or months to complete. Now scientists can access photos and data on any device, whether in an office or out in the field. By sharing data in one place, Wildlife Insight is helping to facilitate collaboration and answer larger conservation questions.
“With access to reliable and timely information on wildlife, scientists, land managers and other stakeholders can better anticipate threats, understand where and why wildlife populations are changing, and take action to protect our most endangered species.” -
24. Public safety. “Another initiative being trialled in Singapore and Thailand involves the use of computer vision at service station forecourts. Computer vision – cameras which can ‘think’ and understand what they are filming – are trained to watch out for the potential hazard of customers lighting cigarettes in the vicinity of pumps and refuelling vehicles.
“Camera data is processed by what is essentially the same technology powering Google’s reverse image search, which allows the content of the picture to be labelled and categorised.
“When an image is detected that matches what the algorithms ‘know’ (through training) is a person lighting a cigarette, alerts can be issued allowing the forecourt staff to close down nearby pumps and reduce the risk of fires or explosions.” -
25. Politics and elections. “AI and machine learning can be used to engage voters in election campaigns and help them be more informed about important political issues happening in the country. Based on statistical techniques, machine learning algorithms can automatically identify patterns in data. By analyzing the online behaviour of voters which includes their data consumption patterns, relationships, and social media patterns, unique psychographic and behavioural user profiles could be created. Targeted advertising campaigns could then be sent to each voter based on their individual psychology. This helps in persuading voters to vote for the party that meets their expectations.
“Apart from using intelligent algorithms, autonomous bots can also be used to spread information on a large scale. Bots are automated programs that can be programmed to run certain tasks over the internet. They can also be employed to detect fake news and misinformation. Whenever fake news is detected, they could issue a warning that the information is incorrect, thereby stopping it from influencing the voter.” -
Deciding what posts users see in their social media feeds, wildlife conservation, breast cancer screening, virtual reality, personalized in-store experiences, and more — these real-time analytics examples merely scratch the surface of what real-time analytics has impacted in modern life and its potential for the future. If you want to put real-time analytics to use for your business, understanding the myriad ways it can be applied is crucial for making strategic investments.
Data Analytics vs Artificial Intelligence – An Overall Analysis
For a long time, Artificial Intelligence and data analytics have been used interchangeably, to the point where people rarely distinguish between the two concepts. However, as data analytics and AI capabilities become more widely available and applied to various company operations such as marketing and supply, it is critical to grasp the differences between them and their role in business operations. The goal of this article is to explain the relationship between the two concepts – Data Analytics vs Artificial Intelligence.
While the dispute over data analytics vs artificial intelligence is causing revolutions throughout industries, there is still a lot of uncertainty surrounding the two phrases. In a fight between data analytics vs artificial intelligence, we can say that both are interconnected, but have different scopes, techniques, and outcomes depending on your sector.
Let us start the comparison between the concepts – Data Analytics vs Artificial Intelligence.
To begin our comparison of Data Analytics vs Artificial Intelligence, let us start by understanding both concepts.
Recommended Reads:
? Data Analyst Skills
? Data Analytics Career
? Data Analyst Interview Questions
? Scope of Data Analytics
? Data Analytics Salary
? Data Analytics For Finance Professionals
? Data Analytics Tools
? Data Analytics Entry-Level Jobs
About Data Analytics & Artificial Intelligence
Fundamentally, data analytics is the science of analyzing big data sets to detect patterns, answer questions, and draw conclusions. It is a complex and varied field that frequently employs sophisticated software, automated processes, and algorithms.
Almost every industry can profit in some manner from data analytics and artificial intelligence. Organizations of all sizes use data analysts to help them make sound decisions about various elements of their business. Typically, past data from events is evaluated, allowing present trends to be identified.
Data analytics comes in many forms, including descriptive analytics, diagnostic analytics, predictive analytics, and prescriptive analytics.
Artificial intelligence (AI) has been discussed for a long time. However, it wasn’t until later that we had the processing power to make it a reality. The ability of computers to simulate human intelligence lies at the heart of artificial intelligence.
By creating machines that can learn, it is feasible to teach computers through experience. These AI systems possess three characteristics: intelligence, adaptability, and intentionality. These traits enable them to make decisions that would normally necessitate human expertise and experience.
Let us go through the concepts of Data Analytics vs Artificial Intelligence.
What is Data Analytics?
Data analytics is a wide word that encompasses the notion and practice (or, perhaps, science and art) of all data-related activities. The primary goal is for data experts, such as data scientists, engineers, and analysts, to make it simple for the rest of the organization to access and comprehend these discoveries.
Data that is left unprocessed has no value. Instead, what you do with that data is what adds value. Data analytics encompasses all the actions you take, both manually and automatically, to identify, analyze, visualize, and tell the story of patterns in your data to drive business strategy and outcomes.
An effective data analytics approach can—and should—provide a more comprehensive strategy for where your company can go. When done correctly, data analytics can assist you in the following ways:
? Discover trends
? Discover new possibilities
? Actions, triggers, or events can be predicted
? Make choices
Read in-depth:
? Data Analytics Jobs
? Data Analyst Qualifications
? Data Analytics For Business
? Data Analytics Trends
? Data Analytics With Python
? Data Analytics Using Power Bi
? Benefits of Data Analytics
Data analytics, like any serious practice, is methodical, with numerous computational and administrative phases. Experts emphasize the word “systematic.” Because data analytics involves many distinct operations and draws on a wide range of data sources, it is critical to be systematic.
Data analytics encompasses many disciplines, including data science, machine learning, and applied statistics. One measurable outcome of a data analytics process is likely to be well-planned reports that employ data visualization to convey the story of the most important points so that the rest of the business—who aren’t data experts—can comprehend, develop, and modify their strategy.
Consider the following examples of how data analytics might reveal areas of opportunity for your company:
Using facts rather than educated guesses to understand how your clients interact may necessitate changes to your sales or marketing procedures. A bakery may utilize its data to discover that demand for bread bowls increases in the winter, implying that you do not need to lower prices when demand is high.
Increased cyberattacks may necessitate proactive precautionary measures.
Data from a range of IoT devices in a certain location, such as your server room, a power station, or a warehouse, could reveal whether you’re providing the necessary safety and reliability at the lowest feasible cost.
What Is Artificial Intelligence (AI)?
Artificial intelligence (AI) is the simulation of human intelligence in machines that are programmed to think and act like humans. The phrase can also refer to any machine that demonstrates human-like characteristics such as learning and problem-solving.
The best aspect of artificial intelligence is its capacity to reason out and carry out actions that have the highest chance of achieving a given objective. A subset of artificial intelligence known as machine learning (ML) refers to the idea that computer programs can automatically learn from and adapt to new data without the help of humans. Deep learning techniques enable autonomous learning by ingesting vast quantities of unstructured data, including text, images, and video.
Explore and learn more about:
? Work From Home Data Analytics Jobs
? Data Analytics Techniques
? Data Analytics For Project Management
? Data Analytics vs Data Science
? Data Analytics and Business Intelligence
? Data Analytics Services
? Importance of Data Analytics
In a nutshell:
? Artificial intelligence is the term used to describe the reproduction or approximate representation of human intelligence in machines (AI)
? The goals of artificial intelligence include computer-enhanced learning, thinking, and perception.
? Some sceptics are concerned that widespread usage of advanced AI would hurt society
The next point of comparison between Data Analytics vs Artificial Intelligence is Skills. We will have a look at the skills required for the role of data analytics vs that required for artificial intelligence.
Skills
Data Analytics Skills
Cleaning and Preparation of Data
According to research, data cleaning and preparation will account for roughly 80% of the labour of most data professionals. As a result, this competence is critical.
A data analyst will frequently be required to retrieve data from multiple sources and prepare it for numerical and categorical analysis. Data cleansing also entails correcting missing and conflicting data, which may have an impact on analysis.
Data cleansing isn’t usually fascinating in data analytics, but it may be fun and challenging when approached as a problem-solving exercise.
Data Exploration and Analysis
It may seem unusual to include “data analysis” in a list of required data analyst abilities, yet analysis is required.
Data analysis is fundamentally concerned with taking a business question or a need and evaluating relevant data to generate a response to that question.
Exploration is another type of data analysis. Data exploration entails searching for intriguing trends or relationships in data that may be useful to a business.
Statistical Understanding
Statistics and probability are crucial data analyst abilities. Understanding statistics will also help you confirm the validity of your analysis and avoid frequent fallacies and logical flaws.
The precise level of statistical knowledge required will vary according to the demands of your specific profession and the data you’re working with.
If your organization relies on probabilistic analysis, for example, you’ll need a far more rigorous understanding of those areas than you would otherwise require.
You should know about:
? Data Analytics Consultant
? Data Analytics Companies in India
? Data Analytics Examples
? Data Analytics vs Data Mining
? Data Analytics vs Machine Learning
? Data Analytics For Beginners
? Data Analytics With R
? Data Analytics Books
Designing Data Visualizations
Data visualizations help to explain data trends and patterns. Humans are visual creatures, which implies that most people will understand a chart or graph faster than a spreadsheet.
This entails producing clean, visually appealing charts that will assist others in comprehending your findings. It also implies avoiding items that are either difficult to read (such as pie charts) or can be misleading (like manipulating axis values).
Visualizations might also be useful in your data research. When you merely look at the numbers, you can miss things that you can see visually in the data.
Creating Dashboards and Reports
As a data analyst, you will be responsible for empowering others to use data to make critical decisions. By creating dashboards and reports, you will remove technical hurdles that prevent others from seeing critical data.
This might be as simple as a chart and a table with date filters, or as complex as a dashboard with hundreds of interactive data points.
Job descriptions and criteria will vary depending on the role, but practically every data analyst job will require you to produce reports on your findings or construct dashboards to display them.
Communication and writing
Another important data analyst skill is the capacity to communicate in numerous formats. Communication skills such as writing, speaking, explaining, and listening will help you excel in any data analytics profession.
When working with co-workers, communication is essential. In a launch meeting with business stakeholders, for example, good listening skills can assist you to comprehend the analyses they demand.
Similarly, you may be required to explain a complex issue to non-technical teammates during your project.
Dig in here and find the advanced courses:
? Data Analytics Courses in India
? Online Data Analytics Courses
? Data Analytics Courses in Delhi
? Data Analytics Courses in Kolkata
? Data Analytics Courses in Bangal0re
? Data Analytics Courses in Pune
? Data Analytics Courses in Hyderabad
Artificial Intelligence Skills
Machine learning and artificial intelligence (ML/AI) are two cutting-edge technologies that have the potential to change the way organizations and people interact. ML/AI is already having an impact on areas such as IT, FinTech, healthcare, education, and transportation—and it’s only going to get worse. Companies are becoming increasingly laser-focused on the value of AI, moving beyond the trial phase, and focusing on expediting its adoption. This means that software engineers who are prepared to work in ML/AI development roles will be more in demand than ever before.
Here are the skills you’ll need to capitalize on the growing chance to create exceptional ML/AI solutions:
Computer Programming Languages
To become an expert in machine learning, you must first gain knowledge in programming languages like Python, C++, JavaScript, Java, C#, Julia, Shell, R, TypeScript, and Scala.
While Python is the most used language in machine learning repositories, Scala is gaining popularity, particularly when interfacing with large data frameworks like Apache Spark.
Data Engineering
Pre-processing and storing raw data generated by your systems is the initial stage in machine learning development. Consider an online company that sells a range of things to people all around the world. This online store will generate a large amount of data relating to specific events. When a consumer clicks on a product description or buys a product, new data is generated, and you’ll need to build Extract, Transform, and Load (ETL) pipelines to process, clean up, and store the data so that it’s easily available for other processes like analytics and predictions.
Analyzing Exploratory Data
Exploratory data analysis on a dataset is a particularly valuable ability since it helps you to discover intriguing patterns in data, identify anomalies, and test theories. It helps to:
? Generate summary statistics for a dataset, such as:
? The total number of rows and columns
? Data types for columns
? Columns that can or cannot be null
? Mean, standard deviation, minimum and maximum values, percentile, and so on for each column
? Produce graphical representations that facilitate data visualization
? Sanitize and prepare data for modeling by doing things like:
Eliminate outliers from your dataset
Discard linked variables
Models
If you want to be a machine learning expert, you must be familiar with machine learning algorithms. But that isn’t all; you must also know when to use them.
For example, if you have a dataset with a series of inputs and their corresponding outputs and want to find a model that describes the relationship between them, you should use supervised learning algorithms, which are further classified as regression (when the output variable is a real value, such as “weight” or “age”) and classification (when the output variable is a category, such as “yes/no”).
Unsupervised learning techniques should be used if you only have a collection of inputs with no outputs and want to identify different patterns in the inputs and cluster them based on similarities. It’s also worth noting that increasingly difficult tasks, such as picture categorization, object detection, face recognition, machine translation, dialogue synthesis, and so on, will necessitate the use of more complex algorithms from the deep learning category, which is based on artificial neural networks.
Find out here the advanced courses for faster learning:
? Data Analytics Courses in Chandigarh
? Data Analytics Courses in Coimbatore
? Data Analytics Courses in Kochi
? Data Analytics Courses in Indore
? Data Analytics Courses in Jaipur
? Data Analytics Courses in Lucknow
? Data Analytics Courses in Kota
Services
Once you’ve determined the best machine learning model for a given problem, you must decide whether to build the model from scratch or use existing services. As an example:
? If you need to integrate nice conversational interfaces (chatbots) into any application that uses voice and text, AWS Lex offers advanced deep learning functionalities such as automatic speech recognition (ASR) for converting speech to text and natural language understanding (NLU) to recognize the intent of the text, allowing you to build applications with highly engaging user experiences and lifelike conversational interactions.
? AWS Comprehend assists you in uncovering insights and correlations in unstructured text data by:
? Recognizing the text’s language
? Identifying key phrases, locations, people, brands, or events
? Conducting sentiment analysis to detect whether a sentence is favorable or negative.
? Automatically categorizing a set of text files
Deploying
When deploying machine learning systems to AWS, essential criteria such as performance, availability, scalability, robustness, and fault tolerance must be considered. To that end, AWS offers solutions and best practices to assist you in the process. You can, for example, enable monitoring of your solutions to verify performance and scale your services up or down as needed. You can also enable autoscaling so that AWS handles everything for you. You can also distribute your solutions over various availability zones to ensure optimal uptime.
Safety
As with any software solution, controlling security for AWS machine learning solutions is critical, particularly because machine learning models require a large amount of data to be trained, and access to that data should be restricted to authorized persons and apps only.
Now let us see the comparison between Data Analytics vs Artificial Intelligence in a nutshell.
Data Analytics vs Artificial Intelligence: in a Nutshell
Data Analytics Artificial Intelligence
Definition Data analytics is concerned with detecting patterns in historical data to forecast future events
AI is concerned with data analysis, making assumptions, and attempting to generate predictions that are beyond human capabilities.
process data Data analytics seeks patterns in provided data AI seeks to automate the process by imbuing machines with human intelligence.
Job Role Sales Analyst, Operations Analyst, Customer Success Analyst, Market Research Analyst, Marketing Analyst, Business Analyst, Financial Analyst, and more. Machine Learning Engineer, Data Scientist, Business Intelligence Developer, Big Data Architect, and Research Scientist.
Skills Mathematical skills, Programming languages- SQL, Oracle, and Python.Ability to analyze, model, and interpret data.
Problem-solving skills. Mathematical and Algorithms skills, Probability and Statistics knowledge, Expertise in Programming – Python, C++, R, JavaWell-versed with Unix Tools, and Awareness of Advanced Signal Processing Techniques.
Let us have a look at the most asked questions about the comparison between Data Analytics vs Artificial Intelligence.
Frequently Asked Questions
Q1. What is the impact of AI on Data analytics?
In the discussion between Data Analytics vs Artificial Intelligence, let us understand the impact of AI on data analytics. AI technology is altering every aspect of life and has numerous applications such as data analysis, decision-making, and information transmission.
Here are a few examples of how AI and machine learning are advancing analytics that will help understand the comparison between Data Analytics vs Artificial Intelligence.
? AI automates report generation and simplifies data interpretation.
? AI speeds up the generation of insights by streamlining processes.
? AI uses machine learning algorithms to examine data to anticipate future outcomes and discover trends and patterns.
? AI removes errors and is more accurate than traditional business intelligence solutions.
Q2. Will AI take over data analytics?
AI can assist data scientists in generating hundreds or thousands of variants of models with varying prediction features and running iterative simulations to select the best variant. The best versions include both AI and data scientists. A dynamic, multifaceted decision process obtained through automation will outperform any single algorithm, regardless of how advanced, by automatically testing, iterating, and monitoring data quality; incorporating new data points as they become available; and allowing for wise real-time response to events.
AI can also help data engineers prepare raw data, cleanse it, and review it for accuracy. This is something that AI cannot handle completely yet. Human judgment is still required to transform raw data into insights that make sense for a complicated company. AI cannot yet properly comprehend what specific data means for an organization, its business, and the industry context. Lower-level stages in data preparation and visualization can be automated with AI, leaving data scientists to walk decision-makers through what the insights truly mean.
Lower-level jobs normally done by data engineers will be the first to be influenced by AI. For example, when computer programming languages advanced in the 1980s, the need for lower-level programmers decreased. However, as businesses adapt to these new languages, demand for developers increases in general. The same thing is happening in analytics right now, with AI performing lower-level activities. This results in the outsourcing of more difficult problem-solving tasks to people.
As a result, the combination of AI and human problem-solving has enhanced rather than threatened data scientists’ professions. Rather than threatening data science professions, AI is more likely to become incredibly clever assistants to data scientists, allowing them to execute more complex data simulations than ever before. Many more traditional occupations may soon require analytical skills. This change is likely to result in the creation of a new class of data scientists, dubbed “citizen data scientists,” who will bridge the gap between commercial functions and exclusively analytical jobs.
Q3. Why Is AI-Driven Analytics Required for Data-Driven Decision-Making?
Traditional analytics solutions have served a role, but they have various flaws that render them ineffective in today’s business climate. They are difficult to scale to meet increasing demand, and they cannot supply the real-time information required to compete with inventive competitors in fast-paced marketplaces.
AI and machine learning are changing the world of analytics by providing unprecedented speed, scalability, and granularity.
Firms are embracing AI technologies to make better decisions and gain a competitive advantage. AI is at the center of the digital revolution in analytics and promises to help enterprises improve their operations and drive new income opportunities.
Conclusion
This is a comparison between Data Analytics vs Artificial Intelligence. AI and data analytics are frequently used in tandem since the former enhances the latter’s capabilities. With AI, analytics technology can conduct more in-depth research, providing the possibility for micro-targeted discoveries that human analysts would struggle to find. AI can perform complex analyses with multiple variables rapidly and efficiently.
AI in data analytics also makes data cleaning easier, which is a critical stage in the analysis process. It is critical to recognize that AI and analytics are not synonymous and should not be treated as such because AI is a component of the analytics ecosystem. Companies must recognize the distinction and be willing to use technology to get an advantage over their competition.
To recap, data analytics and artificial intelligence (AI) are the business of the future. As a result, if you want to help your company, you should think about implementing this cutting-edge technology. Before deploying technologies, whether you are a business owner or a service provider, you must be well-versed in them.
As you can see, artificial intelligence, machine learning, cloud tech, and data visualization are here, among the current and future trends in data analytics.
Disclosure & Legal Disclaimer Statement Some of the Content has been taken from Open Internet Sources just for representation purposes.
Anjoum Sirohhi