Posts

Big Data & Augmented Intelligence

“Each year computers are getting faster, but at the same time we as humans are getting better at using them.” – Daniel Gutierrez: Data Scientist at AMULET Analytics.

Augmented intelligence is the use of information technology to elevate human intelligence. It focuses on the assistive role of AI, highlighting the fact that it is designed to enhance human intelligence rather than replace it, by helping employees understand and keep up-to-date with the increasingly digital world that we live in.

Sean Gourley, CEO of Stealth Machine Intelligence Company, describes Augmented Intelligence as “humans and machines learning together to solve very, very difficult problems that neither one can solve by themselves.On the one side, you’ve got a very complex world, and we use mathematics to simplify it, and the other side, we’ve got the human version 1.0, where we use visualisation to enhance our natural cognitive ability, and It’s being used to solve some of the most difficult problems in the world.”

Companies like Quid, CognitiveScale, Eolian and Virtualitics are using Augmented Intelligence to improve their business outcomes, with many other emerging start-ups following the lead. Here are some examples of how the interface between humans and machines can help you increase the success of your organisation.

 

#1 – CONTEXTUAL DISCOVERY

Contextual discovery refers to the early phases of research where new knowledge or different ways of thinking about a subject are introduced. With the combined power of machine learning and human curation, contextual awareness across the entire data and analytics workflow is impacted.

“Augmented Intelligence enables contextual discovery so users can find what they’re looking for based on natural language queries and context of their work.” – Vice President of Brand Experience at Brainspace.

 

#2 – ENABLING OPPORTUNITIES ACROSS THE ORGANISATION

“Augmented Intelligence is the perfect marriage of machine learning and human intuition, enabling any user to become a data scientist; R&D can use it to research patents, HR can use it for workforce analytics, or corporate investigators can use it to explore cases.” – Brainspace.

Augmented Intelligence serves multiple purposes in an organisation across sales, finance, HR, operations and marketing departments, helping with the streamlining and classification of data, increasing savings, enabling better employee management and resource allocation whilst also reducing human error.

“Using augmented intelligence to make suggestions to staff, and to record their response, means that humans and the machines can work together to come up with the best solutions. There is less need to worry that people are going to miss important actions or take wrong decisions because of the visibility of what’s going on.” – Dataconomy.

 

#3 – FASTER & MORE ACCURATE INSIGHTS

“Quantitatively, the speed with which users can extract key insights with machine learning can save your organisation significant time and allocated costs. Augmenting worker intelligence allows your organisation to extract valuable data hidden in all corners of the company and connect it in ways that make sense to human users.” – Brainspace.

Time management is of high importance in every organisation. With augmented intelligence, the ability to extract meaning from unstructured data and present it logically enables the organisation to derive higher quality insights more quickly.

“AI developers are looking for opportunities to augment our abilities, and to develop tools to do tasks that we cannot do. Consider healthcare professionals, who only have seconds or minutes to make huge decisions.” – Ronald Van Loon: Director of Advertisement.

 

#4 – SECURITY ANALYSIS

Using AI, a business can analyse reams of structured and unstructured data and identify errors and security breaches faster than any human alone. Augmented Intelligence tools, like IBM Watson, are proving to be effective at this.
“Dozens of organisations are already working with this technology and helping discover new ways Watson can be used in the fight against cybercrime. In the future, bots will seek out network vulnerabilities, diagnose them, and recommend ways to patch them — all while working seamlessly with cybersecurity experts, who will be even more valuable in the fight against cybercrime because they have been trained in the use of augmented intelligence.” – Sandy Bird: Chief Technology Officer at IBM Security.

 

 

 

For more resources, please see below:

The Hype – And Hope – Of Artificial Intelligence

Augmented Intelligence: What You Should Know

How Augmented Intelligence Helps Businesses Grow

The Shift to Augmented Intelligence and How It Helps Your Organisation

Just Buying Into Modern BI And Analytics? Get Ready For Augmented Analytics

Big Data And The Rise of Augmented Intelligence: Sean Gourley at TEDx Auckland

Augmented Intelligence: How To Combine Human & Artificial Intelligence To Change Behaviour

Big Data & Analytics, Virtual & Augmented Reality, Artificial Intelligence & Cloud Are Driving Universities To Innovate

 

To discuss Augmented Intelligence and other topics, please contact the team at Contexti – + 61 28294 2161 | connect@contexti.com

Big Data Analytics – Enabling The Agile Workforce

In a fast-paced, constantly evolving digital era, time waits for no one. Agility, fast recovery from failure and adaptability to change continue to grow in importance for businesses who want to remain competitive. The capability to be this sort of business requires the right team and right technology.

Agile refers to a type of project management used often for software development. Tasks are divided into short phases of work and plans are frequently reassessed and adapted.

Here’s three ways an agile approach to Big Data Analytics can improve the success of an organisation.

 

#1 – DECISIONS & DELIVERY IN NEAR-REAL TIME

Data science allows us to make sense of the information we collect, and identify the valuable insights hidden in terabytes of structured and unstructured data. That being said, Big Data projects can be uncertain in nature if the right delivery methods are not used. This is where agile comes in.

“The principles and practices that are collected under the Agile umbrella all focus on validating assumptions as early as possible in the delivery lifecycle, significantly reducing the risk exposure as the project continues. By delivering the work in small increments, even with production-ready software, those assumptions are all validated early on. All code, design, architecture and requirements are validated every time a new increment is delivered. Even the plan is validated as teams get real and accurate data around the progress of the project.” – Gino Marck: Head of Agile Competency Center, EPAM Canada.

 

#2 – TEAM EFFICIENCY & COMMUNICATION

For a team to truly embrace agile, an interactive, adaptable and feedback-driven culture must be fostered in the organisation. The ability of a team to communicate progress and change direction when needed is crucial to the success of any Big Data project.

“For example, Ruben Perez, who runs a digital project management team at Scholastic Corporation, has his managers hold a daily scrum. When work is moving fast, you have to ensure that everyone is moving in the same direction, he says. “The scrum manager holds a 15-minute check-in every day to ensure that the tasks that have been slotted for a particular sprint are on track and that nothing is blocking forward progress. Anything that is standing in the way is assigned to someone to resolve—separately.” – The Economist.

 

#3 – IMPLEMENTING THE RIGHT TECHNOLOGY

“Shorter product cycles, compressed delivery times and pressures from a global economy require employees to thrive on change and be empowered to make decisions in near–real time. To power this sort of agility, companies must have the right technology—tools that allow for instant communication, collaboration and centralised platforms. And they’ve also got to establish and nurture an adaptive culture. Changing directions in a large organisation with long-established processes isn’t easy.” – The Economist.

Once the right culture is set in place, working with the right tools is what makes agile possible. An organisation must select the technology that will best cater to the transformation they’re after. Luckily, there’s no shortage of options. We’ve rounded up a few that you may find useful.

 

#1 – JIRA

With key features like issue tracking, bug tracking, kanban boards, workflows and the ability to customise the dashboard to meet the needs of your business, this software is the among the most popular project management tools available.

 

#2 – PLANBOX

Built as a four-level platform supporting Scrum Methodology, Planbox allows members across the business to collaborate, plan, and deliver projects, as well as enabling agile software development. Its features include release management, iterations, stories, backlog, prioritisation, scrum roles, sprints, estimated hours and story points.

 

#3 – ASANA

Asana is the ultimate progress tracker that helps you visualise your team’s work and follow up on individual tasks on a kanban board, calendar or list. It’s a flexible tool that adapts easily to an organisation’s scrum practices. With work efforts and communication in one place, team members can ensure that they have full clarity on sprint plans, milestones, launch dates and backlog.

 

For more resources, please see:

Powering The Agile Workplace

Big Data Analytics – What It Is And Why It Matters

How To Choose The Right Technology For Agile Transformation

 

Agile project management tools:

Asana

Planbox

Jira – Atlassian

 

To discuss this and other topics, please contact the team at Contexti – +61 2 8294 2161
| connect@contexti.com

Deep Learning Technologies Enabling Innovation

“Deep Learning has had a huge impact on computer science, making it possible to explore new frontiers of research and to develop amazingly useful products that millions of people use every day.” – Rajat Monga, Engineering Director at TensorFlow & Jeff Dean, Senior Fellow at Google.

With innovation driving business success, the demand for community-based, open-source software that incorporates AI & deep learning is taking over start-ups and enterprises alike. We’ve rounded up a few successful deep learning technologies that are making a big impact.

 

#1 – TensorFlow

TensorFlow is an open source software library that uses data flow graphs for numerical computation. Nodes in the graph represent mathematical operations, while the graph edges represent the multidimensional data arrays communicated between them. With extensive built-in support for deep learning, TensorFlow can compute any algorithm that can be expressed in a computational flow graph.

“TensorFlow was built from the ground up to be fast, portable, and ready for production service. You can move your idea seamlessly from training on your desktop GPU to running on your mobile phone. And you can get started quickly with powerful machine learning tech by using our state-of-the-art example model architectures.” – Google Research Blog.

 

#2 – IBM PowerAI

Offering a collection of open-source frameworks for deep learning in one installable package, IBM Power AI claims to simplify  the installation and system optimisation required to bring up a deep learning infrastructure.

“PowerAI makes deep learning, machine learning, and AI more accessible and more performant. By combining this software platform for deep learning with IBM® Power Systems™, enterprises can rapidly deploy a fully optimised and supported platform for machine learning with blazing performance. The PowerAI platform includes the most popular machine learning frameworks and their dependencies, and it is built for easy and rapid deployment. PowerAI requires installation on IBM Power Systems S822LC for HPC server infrastructure.” – IBM

 

#3 – Intel Nervana

Nervana Systems, acquired by Intel last year, is now known as Intel Nervana and referred to as ‘the next big shift inside corporate data centers.’

“Nervana has built an extensive machine learning system, which runs the gamut from an open-sourced software platform all the way down to an upcoming customised computer chip. The platform is used for everything from analysing seismic data to find promising places to drill for oil to looking at plant genomes in search of new hybrids.” – Aaron Pressman: Senior Writer at Fortune.

This state-of-the-art deep learning system is made up of curated, enterprise-grade collections of the world’s most advanced deep learning models and is updated on a regular basis.

“The Intel® Nervana™ Deep Learning Studio, a suite of tools with an easy-to-use interface, dramatically simplifies the deep learning process and accelerates time-to-solution. After you import your data, you can extend one of our state-of-the-art models or build your own. Then, you can kick off training with single click and track progress on the dashboard. All the capabilities of the platform are also accessible via a powerful command line interface.” – Intel Nervana.

 

#4 – NVIDIA Deep Learning SDK

‘The NVIDIA Deep Learning SDK provides high-performance tools and libraries to power innovative GPU-accelerated machine learning applications in the cloud, data centers, workstations, and embedded platforms.’ – NVIDIA.

Offering a comprehensive development environment for building new GPU-accelerated deep learning algorithms, and the inclusion of libraries for deep learning primitives, inference, video analytics, linear algebra, sparse matrices, and multi-GPU communications, your business could dramatically increase the performance of existing applications.

“With the updated Deep Learning SDK optimised for Volta, developers have access to the libraries and tools that ensure seamless development and deployment of deep neural networks on all NVIDIA platforms, from the cloud or data center to the desktop to embedded edge devices. Deep learning frameworks using the latest updates deliver up to 2.5x faster training of CNNs, 3x faster training of RNNs and 3.5x faster inference on Volta GPUs compared to Pascal GPUs.” – NVIDIA.

 

 

For more resources, please see below:

IBM Power AI

Intel Nervana Platform

Why Deep Learning Is Suddenly Changing Your Life

Nividia Accelerated Computing – Deep Learning Software

Why Intel Bought Artificial Intelligence Startup Nervana Systems

TensorFlow – Google’s Latest Machine Learning System, Open Sourced For Everyone

Intel Is Paying More Than $400 Million To Buy Deep-Learning Startup Nervana Systems

PowerAI: The World’s Fastest Deep Learning Solution Among Leading Enterprise Servers

Data’s Growing Potential To Transform Business

“Big Data does not only refer to online activity but also to behaviour offline, including use of credit cards or even smartphones, which send GPS locations and records behaviour. The existence of large volumes of data that can be used for different applications provides those willing to data mine and analyse with several opportunities.” – Daniel Abela: Owner & Managing Director at Redorange.

In the past few years, Big Data analytics has become a game-changer for many businesses worldwide, with profitable outcomes achieved in successful startups like Treasure Data and MapD, and large enterprises like Amazon and Apple. With new and innovative technologies continuing to launch at a rapid pace, the potential for growth won’t be slowing down anytime soon.

“The integrated use of analytics, Big Data, the cloud, the Internet of Things (“IoT”), mobile, and application development—is driving change at unprecedented rates. Our digital economy is subject to Moore’s law and digital transformation has become the new normal.” – Forbes.

Here’s some examples of how you can use data analytics to grow your business.

 

#1 – Business Intelligence For Better Decision-Making

“No matter what BI application is used, the reality is that organisations are continuously searching for ways to get more value out of their data. BI provides one of the best ways to transform data sources into interactive information that can lead to better decision making and planning.” – Lyndsay Wise: Solution Director at Information Builders.

The aim of business intelligence is to generate value, insight and support better decision-making. With a myriad of BI tools in the market delivering real-time insights on user-friendly dashboards, businesses have more power than ever when it comes to leveraging information to their advantage. We’ve rounded up a few successful ones to help you decide which tool is right for your business.

 

Qlik Sense

With the ability to easily combine your data sources and get detailed reports in an instant, Qlik has been deemed as an effective and user-friendly analytics tool by its users.

“With the Associative engine at its core, Qlik Sense lets you discover insights that query-based BI tools simply miss. Freely search and explore across all your data, instantly pivoting your analysis when new ideas surface. You’re not restricted to linear exploration within partial views of data. And you get total flexibility with a cloud-ready data analytics platform that supports the full spectrum of BI use cases – ideal for any analyst, team or global enterprise.” – Qlik.

 

Sisense

“Designed to be used by people who need to consume and analyse large amounts of data but have little or no prior experience in data crunching.” – Forbes.

An industry leader in business intelligence tools, this agile tool lets you analyse and visualise both big and disparate datasets and adapts to the needs of your business.

“Our Single-Stack™ architecture takes you from data integration to visualisation with a single BI software solution, eliminating the need to use additional tools.” – Sisense.

 

Microsoft Power BI

“It is the exact visually-appealing, dynamic, and user-friendly tool every developing company needs, and has thus brought a number of critical benefits.” – financesonline.com.

Power BI is a set of business analytics tools designed to analyse data, share insights, provide a 360-degree view of important metrics available on all devices, receive real-time updates and provide hundreds of connections to popular business apps.

“Power BI can unify all of your organisation’s data, whether in the cloud or on-premises. Using the Power BI gateways, you can connect SQL Server databases, Analysis Services models, and many other data sources to your same dashboards in Power BI. If you already have reporting portals or applications, embed Power BI reports and dashboards for a unified experience.” – Microsoft Power BI.

 

#2 – Digitisation Of Business Processes For Operational Efficiency & Customer Retention

“Spoiled by user experiences on Google and Amazon, people are increasingly demanding enhanced digital access to their records, as well as instantaneous access to the services they’re buying. This increases the pressure on traditional companies and leaves them vulnerable to disruption.” – Sharon Fisher: Content Strategist at The Economist Group.

Digitisation of people and processes is the future of business. The end-to-end customer experience design of your business can make or break your competitive edge. As demands and expectations grow, automation and optimisation become key to customer retention and organisational productivity.

“Intuitive interfaces, around-the-clock availability, real-time fulfillment, personalised treatment, global consistency, and zero errors—this is the world to which customers have become increasingly accustomed. It’s more than a superior user experience, however; when companies get it right, they can also offer more competitive prices because of lower costs, better operational controls, and less risk.” – McKinsey & Company.

Using Big Data analytics to implement automated operational strategies into your business model can be both a cost and time effective strategy, as well as an enabler for revenue growth.

“Automation gives fast growing companies the tools to keep up, but the how-to-get-there can seem like a daunting task. Any successful owner, founder, or CEO knows you have to plan for growth. That plan should include finding the right technology that can scale with your business — and automation must be integral to that plan.” – Salesforce.

 

 

#3 – Innovation & Growth Using Big Data Analytics Powered By Cloud Computing

“Whether making the decision to move to the cloud is instigated by economics or the ever-increasing speed of business, organisations need to get data-driven faster, and turning to the Cloud sooner rather than later may just be the answer.” – Dataversity.

Companies who maximise their use of analytics have a faster rate of growth and are in a stronger position to innovate than those who don’t. Using the cloud as a platform for speed, scale, customer engagement and innovation has increased the performance of the companies below.

 

Atlassian – “Aussie startups are thriving thanks to cloud technology services. Atlassian, a company that sells $100m worth of software to 130 different countries per year is an Australian startup success story. Atlassian has grown from a tech startup making clever use of cloud technologies, to an internationally renowned, billion-dollar company.” – Amazon Web Services.

Founded in 2002, Atlassian is a software company with various collaboration tools used by enterprises and startups worldwide.

“Atlassian uses AWS to scale its issue-tracking software applications faster than before, provide improved services to tens of thousands of global customers, and enhance its disaster recovery and availability. The Australia-based organisation provides software that helps developers, project managers, and content managers collaborate better. Atlassian uses Amazon EFS to support customers deploying JIRA Data Center on AWS, and also runs an internal issue-tracking application platform on AWS.” – Amazon Web Services.

 

Pearson – Founded in 1998, Pearson is a global online education provider that offers learning resources to a wide range of people, from preK-12 education and higher education to industry professionals.

“Pearson is using the cloud to transform the way it delivers education worldwide. The cloud is enabling Pearson to establish a more flexible global hybrid infrastructure with common systems and processes, which frees up resources to invest in new, more web-oriented educational products that deliver measurable outcomes for learners. This is part of an enterprise-wide business transformation that will help accelerate the company’s shift towards fast-growing markets — like South Africa and China — and educational products that are increasingly digital in nature.” – Forbes.

 

Judo Capital – “Working with cloud based services and capabilities, provided by Itoc, has enabled us to remain focused on our true mission, while achieving our vision of an IT-less future.” – Graham Dickens: Chief Technology Officer at Judo Capital.

Judo Capital, built by a small group of highly experienced bankers, is a specialist financier designed to address the financial needs of Australian SMEs. Using Itoc, a provider of a range of cloud and DevOps services, they have been able to leverage growth through better decision-making.

“Designed and built from the ground up in just 6 months, the Judo team and their technology partners have created a new breed of platform, a true ecosystem in the cloud that supports real time effective distribution of information, transparent communication and decision making. The result of which empowers Judo bankers and brokers to deliver an unrivalled service and provide customers with the opportunity to gain insight and transparency into the renowned ‘dark art’ that is today’s customer experience of SME lending.” – Richard Steven: CEO of Itoc.

 

 

For more resources, please see below:

Big Data, Huge Opportunities

Big Data & Advanced Analytics

How To Digitise Your Business In Simple Steps

Accelerating The Digitisation Of Business Processes

Why Automation Is Essential To Your Business Growth

Four Ways To Innovate Using Big Data And Analytics

Time To Digitise Business Processes, McKinsey Says

Business Transformation: How Big Data Analytics Helps

8 Ways You Can Grow Your Business Using Data Science

Four Reasons Why Big Data Analytics In The Cloud Makes Sense Now

Business Intelligence, Data Transformation And Better Decision Making

Using Rapid Process Digitisation To Transform The Customer Experience

The Importance Of Big Data and Analytics In The Era Of Digital Transformation

How Digital Disrupts Operations, Business Processes And Customer Experience

Seven Business Process Automation Benefits That Make Your Company More Money

 

Business Intelligence Tools

Sisense

Qlik Sense

Microsoft Power BI

15 Business Intelligence Tools For Small And Big Businesses

 

Businesses Leveraging Cloud Computing

Itoc

Pearson

Atlassian

Judo Capital

Amazon Web Services

Case Study: Unleashing The Potential Of Australian Businesses

The Advantages Of Cloud Computing For Startups

Three Companies That Transformed Their Businesses Using Cloud Computing

Faster & Smarter Insights With Predictive Analytics & Machine Learning

“While new software and hardware is created on a weekly basis, many companies continue to use systems they have used for years. Their hesitation is based on costs, and the possibility things can go horribly wrong. Some established companies have offered Data Analytics-as-a-Service, in part to maximise profits on Data Science programs they had developed for themselves. Additionally, startups with a focus on offering a broad range of Data Analytics services are becoming a reality.” – Keith D. Foote.

With rapid digital growth from innovative and disruptive tools and technologies, businesses are able to achieve results faster and smarter than ever before, using data science techniques to turn Big Data into Smart Data. The data strategy of your business must be able to harness and scale innovation at the pace that it’s happening.

“The old ways of analysing data don’t cut it anymore in the business world. Every day I talk to more enterprises blending their corporate data with sentiment, location and sensor data for more precise insights to grow revenue, gain a 360 degree view of their customers, mitigate risk and operate more efficiently.” – Quentin Gallivan: CEO of Blue Jeans Network.

 

PREDICTIVE ANALYTICS

“From drug discovery to price optimisation, across virtually every industry, more companies are using predictive analytics to increase revenue, reduce costs, and modernise the way they do business.” – Lisa Morgan: Freelance Writer at InformationWeek.

Businesses who utilise data to prepare for what’s ahead are at a competitive advantage. By extracting information from existing datasets to find patterns, trends and insights, businesses are able to forecast the result of future activities, and we’ve discovered some tools that do this really well.

 

RapidMiner

RapidMiner Inc. is a data science platform used to perform predictive analytics, machine learning, data mining, text analytics, business analytics and visualisation, with little or no coding required.

“RapidMiner is a centralised solution that features a very powerful and robust graphical user interface that enables users to create, deliver, and maintain predictive analytics. With RapidMiner, the whole process of modelling to implementation is unhindered.” – financesonline.com.

 

SalesPRISM

SalesPRISM is a customer pattern-recognition tool from Lattice, used for predictive lead scoring and delivering predictive marketing and sales cloud applications to B2B companies. It allows sales teams to better prioritise their leads based on a customer’s likelihood to buy.

“SalesPRISM looks at many factor such as CRM data, site traffic and sales history along with external data that analyses LinkedIn activity and even LexisNexis reports. This Big Data Analytics generates leads for the sales, along with specific guidance on how to approach customers based on past success.” – IDG.

 

GraphLab Create

GraphLab is an open source, parallel framework for machine learning, designed considering the scale, variety and complexity of real world data. It has been successfully used for a broad range of data mining tasks.

“What makes it amazing? It’s the presence of neat libraries for data transformation, manipulation and model visualisation. In addition, it comprises of scalable machine learning toolkits which has everything (almost) required to improve machine learning models. The toolkit includes implementation for deep learning, factor machines, topic modeling, clustering, nearest neighbours and more.” – Analytics Vidhya.

 

MACHINE-LEARNING

Most organisations use machine learning software to develop predictive models that are used in multiple applications such as churn analysis and prevention, fraud analysis and detection and real-time recommendation.

“For those of us who are practicing and developing machine learning technology, it’s no longer sufficient to provide the ability to achieve the most accurate, fast, and scalable predictive insights. Ultimately, for machine learning to impact the world around us in a truly meaningful way, we have to deliver machine learning in a smarter, more usable form.” – Wired.

Machine learning is the modern science of detecting patterns, making predictions, data mining, and advanced/predictive analytics. High performance machine learning can analyse a whole dataset, not just a sample of it. It’s useful for gaining insights from data across multiple channels such as CRM, social media and transactional. The scalability of it allows predictive solutions based on sophisticated algorithms to be more accurate, and also drives the importance of software’s speed of interpretation.

“Analytic solutions based on machine learning are best suited for fast changing data, large variety of unstructured data and the sheer scaling issues associated with Big Data.” – Martin Hack: Executive Chairman of zPREDICTA.

The Machine Learning software that you chose will depend on what your business requirements are, and one of the key things to look at is ensuring that the machine-learning based technology you’re implementing can be integrated with the software environment of the enterprise.We’ve rounded up some of the most popular for you to consider.

 

Amazon Machine Learning (AML)

AML is a largely automated platform that applies machine learning algorithms to data stored in the popular Amazon Web Services Platform, and includes an automatic data transformation tool.

“Amazon Machine Learning (AML) offers companies an easy, highly-scalable on-ramp for interpreting data. Under the umbrella of Amazon Web Services (AWS), launched in 2006, AML offers visual aids and easy-to-access analytics to make machine learning accessible to developers without a data science background, using the same technology fueling Amazon’s internal algorithms.” – Hope Reese: Writer for TechRepublic.

 

IBM Watson Machine Learning Service

The implementation of this service claims to increase productivity of your data science team by allowing them to create, deploy and manage high quality self-learning behavioural models securely and in real-time.

“IBM Watson Machine Learning is built on IBM’s proven analytics platform, making it easy for developers and data scientists to make smarter decisions, solve tough problems, and improve user outcomes.” – IBM.

 

Anaconda

As a technology that Contexti has been exploring, this data science platform can enable your business to work with both R and Python. Anaconda is a package and environment manager for data science, built with different versions of R, Python and their associated packages, allowing for easy management.

“With more than 13 million downloads to date, Anaconda is blossoming into a real phenomenon in a crowded data science field. What made the collection of mostly python-based tools so popular to data hackers – a dedication to openness, interoperability, and innovation – is also also the strategy behind Continuum Analytics’ business expansion, and possibly even an IPO.” – Datanami.

 

Google Cloud Machine Learning

Google Cloud Machine Learning provides users with access to high-level algorithms used by Google Analytics, making it possible to get insights from real-time metrics that will give you a competitive advantage. Users can also build their own models, or use pretrained models that support video analysis, image analysis, speech recognition, text analysis and translation.

“Google Cloud Machine Learning Engine makes it easy for you to build sophisticated, large scale machine learning models that cover a broad set of scenarios from building sophisticated regression models to image classification. It is portable, fully managed, and integrated with other Google Cloud Data platform products such as Google Cloud Storage, Google Cloud Dataflow and Google Cloud Datalab so you can easily train your models.” – Google Cloud Platform.

 

 

For more resources, please see below:

 

Predictive Analytics

6 Practical Predictive Analytics Tools

10 Ways Predictive Analytics Improves Innovation

Real Stories Of Challenges That Slow Digital Transformation

The Future Of Big Data: Smart Data Innovations & Challenges

 

Machine Learning

IBM Machine Learning

Anaconda – Predictive Analytics

Google Cloud Platform

A Tour Of Machine Learning Algorithms

Anaconda Data Science Platform For R, Python Or Both

Should Amazon Be Your AI And Machine Learning Platform?

Amazon Machine Learning – Predictive Analytics With AWS

Use Data To Tell The Future: Understanding Machine Learning

Getting Started With GraphLab For Machine Learning In Python

Why Anaconda’s Data Science Tent Is So Big – And Getting Bigger

Machine Learning Platforms Comparison: Amazon, Azure, Google, IBM

Blockchain Technology Securing The Internet Of Things

“If we are most likely to understand the Internet of Things (IoT) by describing how it relates to objects we use every day, such as a toaster or bicycles, we miss the big picture: a futuristic global network of connected devices, transforming industrial and business processes in a way that we cannot yet comprehend.” – Rhian Lewis: Co-Founder of Count My Crypto.

This revolutionary concept of IoT is already well under way, and the multitude of smart devices connected to it could transform businesses, homes and cities. But without the right security strategy, the more devices we connect, the more problems we encounter.

 

SECURITY

“The intersection between IoT and Blockchain is a fascinating area. Gartner has predicted there will be 20.4 bln connected devices by 2020 with smaller, more efficient sensors and microprocessors offering the potential for use cases of which we can barely dream today. Decentralised architectures mitigate against single points of failure while providing standard protocols for devices to discover each other and communicate.” – The Cointelegraph.

With security concerns on the rise, blockchain technology is not only a safety measure, it’s a cost-saving and error-reducing opportunity.

“Blockchain’s potential to transform the way we think about IoT security is actually a side effect of an even greater opportunity: to rethink problems with online identity that have been festering for decades.” – The Wall Street Journal.

Blockchain, as a way to structure data, is allowing entities to share a digital ledger across a network of computers without the need for a central authority. No single party has the ability to tamper with records. Encryption enables the entities to share a common infrastructure for database retention. The blockchain database is not stored in one location, meaning that records are truly public and can be verified easily. With no centralised version of this information, a hacker can’t corrupt it.

“Although IoT devices are miracles of engineering, they are still underpowered compared to the hardware powering successful blockchains.” – Forbes.

 

IMPLEMENTING BLOCKCHAIN TECHNOLOGY INTO YOUR BUSINESS

“The distributed ledger technology that started with bitcoin is rapidly becoming a crowdsourced system for all types of verification. Could it replace notary publics, manual vote recounts, and the way banks manage transactions?” – strategy-business.com.

Blockchain is being used by businesses for a myriad of things like accepting payments, hiring and even paying wages with Bitwage.

“The clearest and easiest way to start using blockchain tech is to start accepting Bitcoin – or another cryptocurrency – for payments. These payments are fine for peer-to-peer transactions, but they work even better for small businesses.” – Capterra.

Bitcoin is a worldwide cryptocurrency and digital payment system. Units of currency are regulated using encryption techniques to verify the transfer of funds, operating independently of a central bank. Bitcoin is received, stored, and sent using software known as a Bitcoin Wallet. Getting started with bitcoin is relatively easy. By simply downloading the Bitcoin Wallet of your choice, you can send and receive bitcoin.

“This distributed ledger — the first blockchain ledger ever created was for bitcoin, and it set the pattern for others — represents the most innovative and potentially influential aspect of the technology. Participants interact with one another using pseudonyms, and their real identities are encrypted. The ledger uses public-key encryption, which is virtually impossible to break, because a message can be unlocked only when a public and a private element (the latter held only by the recipient) are linked.” – strategy-business.com.

Although controversial, businesses around the world are seeing the potential that this technology has to not only how we handle payments, but transform how we do business in every way, from how stock exchanges operate, to re-shaping capital markets, to smart contracts. But the global blockchain revolution won’t happen overnight.

“Today, you have to assemble a lot of pieces by hand if you want to develop a blockchain platform, just like how, in 1995 or 1996, if you wanted to publish a website, you had to work with html. You had a page editor where you would hand write the html page almost line by line. No one does that anymore. Now you can create a web page without touching a line of code with Squarespace, WordPress, Tumblr, etc. That’s where we need to go.” – Laura Shin: Senior Editor at Forbes.

 

 

For more resources, please see below:

 

The Business Blockchain

Bitcoin – The Internet Of Money

A Strategist’s Guide To Blockchain

4 Critical Security Challenges Facing IoT

What Blockchain Is And What It Can Do

3 Ways To Use Blockchain In Your Business This Year

How Blockchain Could Revolutionise The Internet Of Think

Could Blockchain Technology Help You Find Better Employees?

Looking To Integrate Blockchain Into Your Business? Here’s How

What Is Blockchain Technology? A Step-By-Step Guide For Beginners

Are We Creating Insecure Internet Of Things (IoT)? Security Challenges & Concerns

Blockchain: Wall Street’s Most Game-Changing Technology Advance Since The Internet

September Insights On Big Data For Marketing, Sales & E-Commerce

#1 – TARGETING THE OMNI-CHANNEL CUSTOMER

“The use of Big Data has become a critical force in growing revenues. Big Data Analytics is helping retailers stay in front of a new breed of consumer, the omni-channel shopper.” – Durjoy Patranabish: Former Senior Vice President of Analytics at Blueocean Market Intelligence.

Over the last decade, the field of marketing has undergone rapid changes, moving from mass-marketing to a more personalised, individual communication approach. Analytics tools allow us to segment customers based on preferences, and track the progress of our marketing campaigns.

“Consumers can now engage with a company in a physical store, on an online website or mobile app, through a catalog, or through social media. They can access products and services by calling a company on the phone, by using an app on their mobile smartphone, or with a tablet, a laptop, or a desktop computer.” – Mike Stocker: Vice President of Business Development at Vidyard.

With multiple channels available to purchase from, marketers are faced with the challenge of providing consistency in the customer experience at every potential touchpoint of their purchasing journey. From monitoring web traffic on Google Analytics to launch promotions at optimal times, to investing in SEO services to boost keyword rankings, to building customer journey maps, marketers need to be in the know-how about what motivates their customers in order to deliver what they’re looking for.

 

#2 – WHAT GETS MEASURED, GETS MANAGED

“The most successful companies are digging deep into the data driven research available to them, giving them a leg up on customer retention and bolstering the bottom line.” – Jennifer Havice: Website Copywriter & Online Marketing Strategist at Make Mention Media & Communications.

Big or small, every business can reap the benefits of data analytics tools that give you the insights you need to increase your marketing ROI. We’ve rounded up some of the most popular tools in the industry.

 

Mixpanel

A platform for following the digital footprint of each of your users across both mobile and web devices. This tool allows for for flexibility and customisation, no matter what your role within the business, so you can get the precise knowledge you’re after about your product or service.

 

Kissmetrics

A popular customer intelligence web analytics platform to help track the customer journey, aimed at businesses looking to optimise their digital marketing and boost conversion rates.

 

Google Analytics

A seamless, all-inclusive picture of your business performance. Google Analytics shows you how your campaigns are doing, which customer channels have the highest conversion rate, and allows to set goals and targets, so you you can track your progress over time.

 

Kapost

Helping businesses “turn content into customers,” this platform is used to drive content operation and realise your b2b marketing strategy. It can be integrated with tools like WordPress, Hootsuite and Marketo.

 

#3 – IDENTIFYING OPPORTUNITIES

“The biggest challenge for most eCommerce businesses is to collect, store and organise data from multiple data sources. There’s certainly a lot of data waiting to be analysed and it is a daunting task for some E-commerce businesses to make sense of it all.” – Jerry Jao: CEO & Founder of Retention Science.

Not only does data analytics increase revenue potential with your current customers, it can also be used to identify and attract new markets to tap into.

“Large online vendors can scale their offerings with Big Data and meet specific customer needs. But Big Data also allows to predict customer needs and enable a future optimisation of the product portfolio. So with Big Data, it is possible to optimise the stock costs.” – Big Data Made Simple.

Online retailers can now make better informed decisions while also forecasting for the future. Wouldn’t you love to know what you’re customers would like to buy in advance, and how much they’d be willing to spend? with predictive analytics, you can.

Predictive analytics involves extracting information from your existing data to determine patterns and predict future outcomes and trends. Platforms like RapidMiner and Lattice help identify potential anomalies, service opportunities, reduce the uncertainty of outcomes and score better sales leads.

 

 

 

For more resources, please see below:

 

The Omni-Channel Customer

What Is Omnichannel?

Targeting Omni-Channel Shoppers

The Definition of Omni-Channel Marketing – Plus 7 Tips

Ten Ways Big Data Is Revolutionising Marketing & Sales

 

Marketing Tools

Kapost

Mixpanel

Kissmetrics

8 Big Data Solutions For Small Businesses

Big Data Trends: Top Eight Analytics Lessons For Business

4 Marketing Analytics Tools That Are Shaping The Industry

 

Identifying Opportunities

Lattice Engines

RapidMiner: Data Science Platform

Why Big Data Is A Must In E-Commerce

How Predictive Analytics Is Transforming eCommerce & Conversion Rate Optimisation

 

 

 

3 Strategies For Getting The Most Value From Your Data Lake

“Big Data’ and ‘data lake’ only have meaning to an organisation’s vision when they solve business problems by enabling data democratisation, re-use, exploration, and analytics.” – Carlos Maroto: Technical Manager at Search Technologies.

A data lake is a storage repository that acts as the central source of all your organisation’s current and historical data, both structured and unstructured. This data is transformed as it moves through the pipeline for things such as analysis, creating quarterly and annual reports, machine learning and data visualisation. The information contained in a data lake can be highly valuable asset, however, without the right structure, your data lake could turn into a data swamp.

Here’s three strategies for getting the most value from your data lake.

 

#1 – BUSINESS STRATEGY & TECHNOLOGY ALIGNMENT

“It’s important to align goals for your data lake with the business strategy of the organisation you’re working to support.” – Bizcubed.

What are the business goals you’re trying to achieve with your data lake? Operational efficiency? Better understanding of your customers? Will your current infrastructure help you achieve this while also maximising your profits? Aligning your goals with the technology you’re planning to implement will not only help you articulate what problem you’re trying to solve, but also improve your chances of gaining executive buy-in and winning the support of your team. The better the plan, the easier it is to identify possible roadblocks and the higher the chance of success.

“As technology teams continue to be influenced by the hype and disruption of Big Data, most fail to step back and understand where and how it can be of maximum business value. Such radically disruptive new business processes can’t be implemented without knowledge gathering and understanding how Big Data technology can become a catalyst for organisation and cultural change.” – Thierry Roullier: Director of Product Management at Infogix, Inc.

 

#2 – INTEGRATION & ARCHITECTURE

“You need to be able to integrate your data lake with external tools that are part of your enterprise-wide data view. Only then will you be able to build a data lake that is open, extensible, and easy to integrate into your other business-critical platforms.” – O’Reilly.

Technology is moving at a rapid place.The tools you use in your business may not cooperate well with your data lake, and may not support the data architectures of tomorrow. During the implementation process, one of the first things to look at is how adaptable your long-term technology investments are.

Big Data architectures are constantly evolving, and it’s important to select flexible data processing engines and tools that can handle changes to security, governance and structure without being too costly to the organisation. Before implementing anything, you need to have a clear vision of what you want the end technical platform to look like, and what components you will need to make that happen.

“Modern data onboarding is more than connecting and loading. The key is to enable and establish repeatable processes that simplify the process of getting data into the data lake, regardless of data type, data source or complexity – while maintaining an appropriate level of governance.” – Bizcubed.

 

#3 – DATA VIRTUALISATION & DEMOCRATISATION

“ Data virtualisation involves abstracting, transforming, federating and delivering data from disparate sources. The main goal of data virtualisation technology is to provide a single point of access to the data by aggregating it from a wide range of data sources.” – TechTarget.

Data lakes and data virtualisation tools work well together to solve different problems and provide a layer of intelligence that results in more agility and adaptability to change.

“ As an example, a virtual layer can be used to combine data from the data lake (where heavy processing of large datasets is pushed down) with golden records from the MDM that are more sensitive to stale copies. The advance optimisers of modern data virtualisation tools like Denodo make sure that processing is done where it is more convenient, leveraging existing hardware and processing power in a transparent way for the end user. Security and governance in the virtual layer also add significant value to the combined solution.” – datavirtualizationblog.com.

Data democratisation is the ability for information in a digital format to be accessible to the average end user. The goal of data democratisation is to allow non-specialists to be able to gather and analyse data without requiring outside help.

“Data must be freed from its silos. Today, it resides in a variety of independent business functions, such as HR, manufacturing, supply chain logistics, sales order management and marketing. To get a unified view of this data, businesses are engaging in a variety of ad-hoc, highly labor-intensive processes.” – Computer Weekly.

 

For more resources, please see below:

Best Practices For Data Lakes

How To Build A Successful Data Lake

Five Keys To Creating A Killer Data Lake

Avoiding The Swamp: Data Virtualisation & Data Lakes

Democratising Enterprise Data Access: A Data Lake Pattern

How To Successfully Implement A Big Data/ Data Lake Project

Top Five Differences Between Data Lakes & Data Warehouses

 

2018 Big Data Predictions

“There are only two certainties in Big Data today: It won’t look like yesterday’s data infrastructure, and it’ll be very, very fast.” – Matt Asay: Head of Developer Ecosystem at Adobe.

Technology and the power of data science have created huge leaps of growth for businesses who utilise it, and it’s no surprise that the mass increase of worldwide data will mean that Big Data will encounter some big changes in the year ahead.

 

#1 – COGNITIVE TECHNOLOGIES

Cognitive technologies are constantly evolving, and becoming more and more capable of performing tasks that require human intelligence.

“It is now possible to automate tasks that require human perceptual skills, such as recognising handwriting or identifying faces, and those that require cognitive skills, such as planning, reasoning from partial or uncertain information, and learning.” – Deloitte University Press.

Cognitive systems like IBM Watson are improving business products, processes and insights by allowing systems to interact with humans more naturally, and understand complex questions posed in natural language.

“Computing systems of the past can capture, move and store unstructured data, but they cannot understand it. Cognitive systems can. The application of this breakthrough is ideally suited to address business challenges like scaling human expertise and augmenting human intelligence.” – IBM.

 

#2 – PRESCRIPTIVE ANALYTICS

“If analytics does not lead to more informed decisions and more effective actions, then why do it at all?” – Mike Gualtieri: Vice President & Principal Analyst at Forrester Research.

Informed decisions lead to better results. Prescriptive analytics incorporates both predictive and descriptive analytics, and is used to determine the best course of action to take in a given situation. It involves a combination of mathematics, analytics and experimentation that help businesses make
better decisions based on logic. When used correctly, it can help businesses optimise production and enhance the customer experience.

“Prescriptive analytics predicts not only what will happen, but also why it will happen providing recommendations regarding actions that will take advantage of the predictions.” – halobi.com

 

#3 – FAST DATA IS THE NEW BIG DATA

“The argument is that big isn’t necessarily better when it comes to data, and that businesses don’t use a fraction of the data they have access to. Instead, the idea suggests companies should focus on asking the right questions and making use of the data they have — big or otherwise.” – Forbes.

Fast data applies Big Data Analytics to smaller datasets in near-real or real time to mine both structured and unstructured data and quickly gain insight on what action to take. With streaming systems like Apache Storm and Apache Kafka, the value of fast data is being unlocked.

“As organisations have become more familiar with the capabilities of Big Data Analytics solutions, they have begun demanding faster and faster access to insights. For these enterprises, streaming analytics with the ability to analyze data as it is being created, is something of a holy grail.” – Dana Sandu: Marketing Evangelist at SQLstream.

 

#4 – MACHINE LEARNING & AUTOMATION

“It’s possible to quickly and automatically produce models that can analyse bigger, more complex data and deliver faster, more accurate results – even on a very large scale. The result? High-value predictions that can guide better decisions and smart actions in real time without human intervention.” – sas.

The learning capabilities of machines are growing at a large scale, and connecting people, processes and products in new and exciting ways.

“Your digital business needs to move towards automation now while ML technology is developing rapidly. Machine learning algorithms learn from huge amounts of structured and unstructured data, e.g. text, images, video, voice, body language, and facial expressions. By that it opens a new dimension for machines with limitless applications from healthcare systems to video games and self-driving cars.” – Ronald Van Loon: Director at Advertisement.

Today, machine learning is transforming online businesses and being used by organisations for a myriad of things like fraud detection, real-time ads, pattern recognition, speech analysis and spam-filtering. But in 2018, machine learning is said to become faster and smarter than ever before, while also making better predictions for the future.

“Now machine learning seems to offer a solution for demand forecasting. With the inherent capability to learn from current data, machine learning can help to overcome challenges facing businesses in their demand variations.” – Dataversity.

 

#5 – AI ENHANCING CYBER SECURITY

“Artificial Intelligence is looking quite interesting for 2018 and the near future with the attempts to apply reinforcement learning to problems, which enables machines to model human psychology in order to make better predictions; or contesting neural networks with generative adversarial networks algorithms which requires less human supervision and enables computers to learn from unlabeled data; making them more intelligent.” – Exastax.

With capabilities of problem-solving and modeling human psychology, enhancements in AI are also said to be a defence mechanism for safeguarding data in the near future.

“Ironically, our best hope to defend against AI-enabled hacking is by using AI. AI can be used to defend and to attack cyber infrastructure, as well as to increase the attack surface that hackers can target, that is, the number of ways for hackers to get into a system. Business leaders are advised to familiarise themselves with the cutting edge of AI safety and security research.” – Harvard Business Review.

 

For more resources, please see below:

 

2018 Big Data Predictions

Big Data Changes Coming In 2018

Why Big Data Is Important To Your Business

Five Key Predictions For Data & Analytics Through 2020

17 Predictions About The Future Of Big Data Everyone Should Read

 

Cognitive Technologies

How To Get Started With Cognitive Technology

Cognitive Technologies: The Real Opportunities For Business

KPMG Invests In Game-Changing Cognitive Technologies For Professional Services

 

Prescriptive Analytics

What Exactly The Heck Are Prescriptive Analytics?

Descriptive, Predictive And Prescriptive Analytics Explained

 

Fast Data

Fast Data: The Next Step After Big Data

The Future Of Fast And Big Data Technologies

 

AI & Cyber Security

Cyber Intelligence: What Exactly Is It?

Top 10 Security Predictions Through 2020

Five Trends In Cyber Security For 2017 And 2018

The Future Of Artificial Intelligence: Prediction For 2018

AI Is The Future Of Cyber Security For Better And For Worse

18 Artificial Intelligence Researchers Reveal The Profound Changes Coming To Our Lives

Cyber Threats Are Growing More Serious, And Artificial Intelligence Could Be The Key To Security

 

Machine Learning & Automation

Machine Learning & Automation – What It Is & Why It Matters

The Future Of Machine Learning: Trends, Observations & Forecasts

 

Cloudera Acquires Fast Forward Labs To Enhance Machine Learning & AI Research

Given the speed of innovation in the digital realm, it’s exciting to see our partner Cloudera continue to stay ahead of the game with their recent acquisition of Fast Forward Labs (now known as Cloudera Fast Forward Labs), a top-tier applied research and advisory services company.

Cloudera Fast Forward Labs, will concentrate on practical research into new approaches to data science and applying research to business problems that are broadly applicable to a variety of industries and applications.

For the full story, check out Cloudera’s Recent Business & Financial Highlights.

For more resources, please see below:

Data Empowering Artificial Intelligence & Machine Learning

How Big Data Is Changing The Customer Experience & Improving SEO

Cyber Security Strengthened By Big Data Analytics & Machine Learning