Big data, analytics and their importance in the era of digital transformation

As organisations begin their digital transformation journey, big data and analytics can play a key role in it being a success

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.

Source: ITproportal

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What you should know about AI

Artificial intelligence seems to be nearly everywhere these days, yet most people have little understanding of AI technology, its capabilities and its limitations.

Despite evocative names like “artificial intelligence,” “machine learning” and “neural networks,” such technologies have little to do with human thought or intelligence. Rather, they are alternative ways of programming computers, using vast amounts of data to train computers to perform a task. The power of these methods is that they are increasingly proving useful for tasks that have been challenging for conventional software development approaches.

The commercial use of AI had a bit of a false start nearly a quarter century ago, when a system developed by IBM called Deep Blue beat chess grand master Garry Kasparov. That generation of AI technology did not prove general enough to solve many real-world problems, and thus did not lead to major changes in how computer systems are programmed.

Since then, there have been substantial technical advances in AI, particularly in the area known as machine learning, which brought AI out of the research lab and into commercial products and services. Vast increases in computing power and the massive amounts of data that are being gathered today compared to 25 years ago also have been vital to the practical applicability of AI technologies.

Today, AI technology has made its way into a host of products, from search engines like Google, to voice assistants like Amazon Alexa, to facial recognition in smartphones and social media, to a range of “smart” consumer devices and home appliances. AI also is increasingly part of automobile safety systems, with fully autonomous cars and trucks on the horizon.

Because of recent improvements in machine learning and neural networks, computing systems can now be trained to solve challenging tasks, usually based on data from humans performing the task. This training generally involves not only large amounts of data but also people with substantial expertise in software development and machine learning. While neural networks were first developed in the 1950s, they have only been of practical utility for the past few years.

But how does machine learning work? Neural networks are motivated by neurons in humans and other animals, but do not function like biological neurons. Rather, neural networks are collections of connected, simple calculators, taking only loose inspiration from true neurons and the connections between them.

The biggest recent progress in machine learning has been in so-called deep learning, where a neural network is arranged into multiple “layers” between an input, such as the pixels in a digital image, and an output, such as the identification of a person’s face in that image. Such a network is trained by exposing it to large numbers of inputs (e.g. images in the case of face recognition) and corresponding outputs (e.g. identification of people in those images).

AI will not replace software, as electricity did not replace steam.

To understand the potential societal and economic impacts of AI, it is instructive to look back at the industrial revolution. Steam power drove industrialization for most of the nineteenth century, until the advent of electric power in the twentieth century, leading to tremendous advances in industrialization. Similarly, we are now entering an age where AI technology will be a major new force in the digital revolution.

AI will not replace software, as electricity did not replace steam. Steam turbines still generate most electricity today, and conventional software is an integral part of AI systems. However, AI will make it easier to solve more complex tasks, which have proven challenging to address solely with conventional software techniques.

While both conventional software development and AI methods require a precise definition of the task to be solved, conventional software development requires that the solution be explicitly expressed in computer code by software developers. In contrast, solutions with AI technology can be found automatically, or semi-automatically, greatly expanding the range and difficulty of tasks that can be addressed.

Despite the massive potential of AI systems, they are still far from solving many kinds of tasks that people are good at, like tasks involving hand-eye coordination or manual dexterity; most skilled trades, crafts and artisanship remain well beyond the capabilities of AI systems. The same is true for tasks that are not well-defined, and that require creativity, innovation, inventiveness, compassion or empathy. However, repetitive tasks involving mental labor stand to be automated, much as repetitive tasks involving manual labor have been for generations.

The relationship between new technologies and jobs is complex, with new technologies enabling better-quality products and services at more affordable prices, but also increasing efficiency, which can lead to reduction in jobs. New technologies are arguably good for society overall because they can broadly raise living standards; however, when they lead to job loss, they can threaten not only individual livelihood but also sense of identity.

An interesting example is the introduction of ATMs in the 1970s, which transformed banking from an industry with highly limited customer access to one that operated 24/7. At the same time, levels of teller employment in the U.S. remained stable for decades. The effects on employment of automation because of AI are likely to be particularly complex, because AI holds the potential of automating roles that are themselves more complex than with previous technologies.

We are in the early days of a major technology revolution and have yet to see the great possibilities of AI, as well as the need to address possible disruptive effects on employment and sense of identity for workers in certain jobs.

Source: Techcrunch

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