Organizations generate a plethora of data on a daily basis. For any organization to have a successful business model, collecting insightful information is imperative. This information can be retrieved from the existing data that the organizations have. Often, it is observed that while segregating data, instead of retrieving the insightful information, organizations retrieve reliable information, which gives them the idea of what they are looking for, but doesn’t help them to understand the importance of the retained information. But, for any organizations to grow and evolve, it becomes imperative that information which gets retrieved is insightful. That’s where the process of Web Data mining comes into the picture.

Just like the manual process of mining which is conducted for extracting minerals, web data mining is the process of extracting information, that determines the patterns, trends, and ideas for accelerating business intelligence, observing business growth, and promulgating strategies that can be deployed for business excellence. Business intelligence is also governed by analysing the inputs installed by the competitors’ company, observing its market share,  and deriving patterns that can accelerate, and enhance business. Hence, integrating business models with web data mining would be crucial for the success of the organizations.

Understanding Web Data Mining

The Web data mining is the process of automatically identifying and extracting information from the available documents and services available online, in order to identify patterns and behaviour of customers towards a product, observe the pattern in the sale of that particular product and for improving the capability of the Search Engine Optimization for identifying useful web pages.

A Successful business model does not only involve scanning through the data available on the web, but also picking out information that is of paramount importance. According to experts, successful web data mining would involve the following applications:

• Scanning through the news articles of the rival companies, for identify the strategic plan of that company.
• Looking through the 10-k filing, so that insights about the product development, sales, and purchases can be derived, and comparing it with the company’s sales, purchase and product models, so that difference can be observed.
• Automatically finding and analyzing the government rules and regulations
• Identifying and tracking conferences with locations and organizations.

Web Data Mining using Web Content

Web content mining, extracts information from the web documents, by analyzing the text, videos and images, using machine learning and natural language processing. Web content mining would help in identifying that information that is driving the content of the rival company to be successful. By analyzing the blogs put out by the rival company, or accessing the videos and images, the organizations will have an idea about the quality and speciality of the products offered by the competitor. This will drive them to chalk out strategic processes that will upscale their products.

Web Structure Mining

Web structure mining extracts the information from the structure of the data available online. By analyzing the data available online, organizations will be able to track down the nodes, hyperlinks, and web graphs, which will be helpful in determining the link between different commercial products. By analyzing the hyperlinks in the content of the rival company, necessary information like the market performance can also be retained.

Web Usage Mining

With the help of the machine learning model, web usage mining extracts information from the large sets of data, to understand customer behaviour.

Hence for any successful business model, it becomes paramount that insightful information is readily available, so that data-driven decisions can be made.

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Roughly a year ago, Google open-sourced MediaPipe, a framework for building cross-platform AI pipelines consisting of fast inference and media processing (like video decoding). Basically, it’s a quick and dirty way to perform object detection, face detection, hand tracking, multi-hand tracking, hair segmentation, and other such tasks in a modular fashion, with popular machine learning frameworks like Google’s own TensorFlow and TensorFlow Lite.

MediaPipe could previously be deployed to desktop, mobile devices running Android and iOS, and edge devices like Google’s Coral hardware family, but it’s increasingly making its way to the web courtesy WebAssembly, a portable binary code format for executable programs, and XNNPack ML Inference Library, an optimized collection of floating-point AI inference operators. On the graphics and rendering side, MediaPipe now automatically taps directly into WebGL, a JavaScript API for rendering interactive 2D and 3D graphics within any compatible web browser, such that the browser creates a virtual machine at runtime that executes instructions very quickly.

An API facilitates communications between JavaScript and C++, allowing users to change and interact with MediaPipe graphs directly using JavaScript. And all the requisite demo assets, including AI models and auxiliary text and data files, are packaged as individual binary data packages to be loaded at runtime.

“Since everything runs directly in the browser, video never leaves the user’s computer and each iteration can be immediately tested on a live webcam stream (and soon, arbitrary video),” explained MediaPipe team members Michael Hays and Tyler Mullen in a blog post.

Google leveraged the above-listed components to integrate preview functionality into a web-based visualizer — a sort of workspace for iterating over MediaPipe flow designs. The visualizer, which is hosted at viz.mediapipe.dev, enables developers to inspect MediaPipe graphs (frameworks for building machine learning pipelines) by pasting a graph code into the editor tab or uploading a file to the visualizer. Users can pan around and zoom into the graphical representation using a mouse and scroll wheel, and the visualization reacts to changes made within the editor in real time.

Hays and Mullen note that currently, web-based MediaPipe support is limited to the demo graphs supplied by Google. Developers must edit one of the template graphs — they can’t provide their own from scratch or add or alter assets. TensorFlow Lite inference isn’t supported, and the graph’s computations must be run on a single processor thread.

A lack of compute shaders — routines compiled for high-throughput accelerators — available for the web is to blame for this last limitation, which Hays, Mullen, and team attempted to work around by using graphic cards for image operations where possible and the lightest-weight possible versions of all AI models. They plan to “continue to build upon this new platform” and to provide developers with “much more control” over time.

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Gentlemen, start your web engines. The National Association for Stock Car Auto Racing, better known as NASCAR, has inked a deal establishing Amazon Web Services as its preferred partner for machine learning, artificial intelligence and cloud computing, according to a recent statement.

In the works is a new AWS-powered video series on NASCAR.com called This Moment in NASCAR History that will showcase the greatest historical moments in NASCAR racing with viewers. The league also plans to migrate its 18-petabyte video archive to AWS, leveraging Amazon Rekognition for metadata tagging capabilities to cut down on thousands of hours of manual search time annually.

NASCAR also reveals plans to use Amazon SageMaker to train deep learning models using historical footage as well as Amazon Transcribe for automatic speech recognition to caption and time stamp archived videos. The partnership is yet another sign of Amazon’s dominance in the SaaS in cloud computingmarket.

“Speed and efficiency are key in racing and business which is why we chose AWS—the cloud with unmatched performance, the most comprehensive set of services, and the fastest pace of innovation—to accelerate our migration to the cloud.”

“Speed and efficiency are key in racing and business which is why we chose AWS—the cloud with unmatched performance, the most comprehensive set of services, and the fastest pace of innovation—to accelerate our migration to the cloud,” said Craig Neeb, Executive Vice President of Innovation and Development, NASCAR.

“Amazon’s 20 years of machine learning experience, along with our broad analytics and machine learning capabilities, make us the best choice for organizations who want to use machine learning to gain insights into their data and establish new levels of engagement with their customers,” said Mike Clayville, Vice President, Worldwide Commercial Sales at AWS. “NASCAR is utilizing cloud technology to enhance the way people experience the sport and deliver even more impactful content to fans.”

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Big data and analytics are topics firmly embedded in our business dialogue. The amount of data we’re now generating is astonishing. Cisco predicts that annual global IP traffic will reach 3.3 ZB per year by 2021 and that the number of devices connected to IP networks will be more than three times the global population by 2021, while  Gartner predicts $2.5M per minute in IoT spending and 1M new IoT devices will be sold every hour by 2021. It’s testament to the speed with which digital connectivity is changing the lives of people all over the world.

Data has also evolved dramatically in recent years, in type, volume, and velocity – with its rapid evolution attributed to the widespread digitisation of business processes globally. Data has become the new business currency and its further rapid increase will be key to the transformation and growth of enterprises globally, and the advancement of employees, ‘the digital natives’.

The Cisco Global Cloud Index points to the Cloud as the top driver as exponential data centre growth with cloud centre traffic quadrupling in the next five years. Data generated by IoT applications (such as connected homes, smart cities and healthcare) will be 600ZB (zettabytes) per year by 2020, 39 times higher than current data centre traffic which is 15.3 ZB.

Big Data therefore has a far-reaching impact and meaning. But how do we understand it and its benefits, along with analytics on the journey to Digital Transformation? Understanding the value of Data is key to the successful implementation of operational strategies that facilitate agile and effective business growth.

Big data means better business 

Data is an enabler of future strategies and immediate change, thanks to the power of predictive analytics and advanced data science. Correctly harnessing data can help to achieve better, fact-based decision-making and improve the overall customer experience. By using new Big Data technologies, organisations can answer questions in seconds rather than days, and in days rather than months. This acceleration allows businesses to enable the type of quick reactions to key business questions and challenges that can build competitive advantage and improve performance, and provide answers for complex problems or questions that have resisted analysis.

Big Data and analytics are becoming closely intertwined and need to work together to deliver the promised results of Big Data. Traditionally, Data management and analytics have resided in different parts of the organisation. Breaking down organisational boundaries and creating better integration between the IT and business departments is a critical step on the road to successful transformation.

There is also a widespread realisation of the need for better Business Analytics (BI).Business Analytics are the skills, technologies, practices for continuous iterative exploration and investigation of past business performance to gain insight and drive business planning. The key is integrating Big Data with traditional Business Analytics to create a data ecosystem that allows the business to generate new insights while executing on what it already knows.

Keep learning. Skills are everything.

Proficiency with data mining and visualisation tools ranks as one of the most important skills in determining project success.

All organisations need to consistently develop new data mining skills to fully realise the business potential. A key trend in big data is machine learning. Big data experts who can harness machine learning technology to build and train predictive analytic apps such as classification, recommendation, and personalisation systems are in high demand. Statistical and Quantitative Analysis, which aims to understand or predict behaviour or events through the use of mathematical measurements and calculations, statistical modelling and research, is also imperative to accomplishment. Other key data mining techniques that are employed industry wide include:

• Association –  one of the best-known data mining techniques. With association, a pattern is discovered based on a relationship between items in the same transaction.
• Classification is a classic data mining technique based on machine learning.
• Clustering is a data mining technique that makes a meaningful or useful cluster of objects which have similar characteristics using the automatic technique.
• Prediction is one of a data mining techniques that discovers the relationship between independent variables and relationship between dependent and independent variables.
• Sequential patterns analysis seeks to discover or identify similar patterns, regular events or trends in transaction data over a business period.
• Decision tree technique; the root of the decision tree is a simple question or condition that has multiple answers.

Educate your stakeholders 

All stakeholders need to be educated and made aware of Data’s value and understand that it’s essential to business continuity and growth. But they may feel overwhelmed (and under informed) to the power and complexity of the data if it is not properly communicated and presented. Regular meetings, ideally face to face will enforce the importance of the issue and the need for their buy-in.

Deliver Digital Ready networks – it makes financial sense 

All today’s businesses must, via Network Function Virtualisation (decreasing the amount of proprietary hardware needed to launch and operate network services), and Software Defined Networking (that allows updates to be made in real time or as the business demands, in just a few clicks) deliver Digital Ready networks to gain competitive advantage.

The increased simplicity and reduced costs associated with deploying and maintaining a more digital-ready network are core benefits and therefore should be employed as a necessity to improve and enhance business efficiency.

Automation is a high priority 

Automation is a high priority in accelerating Digital Transformation, allowing organisations to optimise their existing processes. Automation technology is IT system and process agnostic, allowing businesses to build on their systems within the existing IT environment.

In order to create a transformative environment and improve speed and quality of delivery, organisations need to integrate automation into their existing processes to increase the ability to frequently release high-quality products –  and to enable revenue and profit growth.

Automation also improves operational efficiency and allows employees to focus on more rewarding tasks. With automation, cost-effective solutions are enabled for repetitive, rules-based tasks. In addition, the prospect of human error is eliminated, delivering outcomes that are 100% accurate. By automating tasks, companies can significantly reduce the overall process cycle.

The road towards digital transformation is a business critical one. Organisations embarking on this journey need to consider how each aspect of their business can be optimised to fulfil new digital objectives and new growth potential.  Big data and analytics play a pivotal role in digital transformation, enabling organisations to optimise their existing processes and stay ahead of the competition.

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What exactly does artificial intelligence (AI) mean in practice? To most, there is a fear associated with it. It spells catastrophe for the human race. First, it will take over our jobs and before we know it, we will have no place in this world. However, AI has the ability to improve how we operate as well as our experiences.

In business, it has the potential to improve efficiency, how we make decisions, and drastically cut costs. In fact, it could help create high quality jobs.

A report by Deloitte suggested that even though technology has potentially contributed to the loss of around 800,000 lower-skilled jobs, it had helped create nearly 3.5 million new higher-skilled ones. This shows how the UK economy is benefiting from technology and the potential emerging tech has.

Recruitment  

Recruitment is one area where AI has the potential to have a significant impact. It has the ability to speed up the hiring process and to take over laborious tasks, allowing people to concentrate on other tasks at hand, ultimately increasing productivity.

AI will also mean businesses will be able to assess candidates more efficiently and quickly determine whether they have the suitable skillset and experience necessary for the role.

In addition, it has the ability to overcome challenges that we find harder to deal with, such as accounting for bias in the hiring process.

Training and development

The introduction of AI to the training process has caused much excitement to a myriad of firms. The potential of adding machine learning capabilities to a variety of human resources systems has arrived and offers an infinite amount of potential for anticipating business goals and developing new and effective people management strategies. In addition, AI can monitor various employee requests on benefits and policies of the firm as well as recommend more adequate staff training programs.

The gig economy is rapidly growing with no signs of decline – it is only a matter of time before a freelance corporate training market emerges. When it does, AI training evaluation tools will be used extensively. Trainers will be hired based on the results that their training schemes have generated. Results ranging from employee engagement, revenues and time management will all be measurable at rapid speed.

AI can measure the immeasurable when it comes to learning and development by providing insight and analysis. In sales for instance, training results are easy to measure, but it is extremely difficult to measure corporate culture. AI evaluation tools could analyse over 100 factors simultaneously and this could be extremely useful to the training industry.

Additionally, when inducting new employees, one of the ongoing challenges in any firm is the ability to rapidly and efficiently instill knowledge and understanding of the firm’s procedures and internal policies. Comprehension of a company operates, will allow employees to perform optimally in the workplace.

Job retention 

The pace of AI development is astonishing – Google estimates robots will reach levels of human intelligence by 2029. To most people, this means that many of their jobs could be replaced by robots and smart machines. However, it is predicted that AI will primarily introduce new jobs and find different functions for existing jobs rather than replace humans. It is crucial for humans to adapt to the idea that AI will inevitably play a pivotal role in most people’s lives in the coming years.

However, though AI will introduce new jobs and functions, some industries will be affected more than others with its growing influence.  In the healthcare industry, many large pharmaceutical and medical companies are extensively using AI and achieving great results. Manufacturing was one of the first industries to harness AI by using robots to assemble and package products for shipment and there is no sign of slowing down. Every day, new robotic developments will take things to the next level by having the ability to assemble more complicated items such as cars and electronics at a much faster pace than humans. It is important to note however, that, many AI driven production lines will still need human support and supervision.

The customer service industry seems to be almost completely taken over by automation. In what used to be a human-run industry, automated processes have now become a quick and easy solution for companies and to some extent for consumers. Before the rise in automated customer service, hold times for a live agent were incredibly lengthy. However, the customer service industry in particular has experienced some of the backlash as consumers can become increasingly frustrated with the lack of human contact, especially when queries and issues are not simple to resolve and require more attention and detailed options to resolve. This highlights the importance of a need to have a combination of the use of technology and human interaction.

Humans and technology working together

The human element remains to be integral to all businesses. Technologies such as AI should be seen as an enhancer, not a replacement to the human mind. Intelligent technologies can advance human activity and people need to understand the possibilities and benefits AI can bring to businesses and everyday life first.

Businesses should be ready to embrace the technology and make part of their goals and strategies as AI becomes more integrated into various aspects of our lives. If you are prepared, you can prosper as new innovations come along.

The introduction of AI has undoubtedly changed how businesses operate, but it also has the ability to create and improve processes as well as increase higher level jobs in every industry.  It is normal to fear the unfamiliar, and with the reputation AI has you can see why there is a high level of scepticism. However, AI could be pivotal for businesses. Ultimately, we need to embrace it, as those who don’t will be left behind. AI is an emerging technology that is growing at a rapid speed and the quicker we learn to work with it instead of fear it, the more beneficial it will be to all.

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