October Insights From Five Companies Winning With Big Data Analytics

Harnessing the power of Big Data, and finding the right set of tools that will enable your business to efficiently generate value from it comes with its challenges. Successfully utilising the power of technology starts with a shift in culture, adopting a data-driven mindset and clearly identifying the business challenges you are looking to address with data analytics.

“The biggest challenge of making the evolution from a knowing culture to a learning culture—from a culture that largely depends on heuristics in decision making to a culture that is much more objective and data driven and embraces the power of data and technology—is really not the cost. Initially, it largely ends up being imagination and inertia.” – Murli Buluswar: Chief Science Officer at AIG

Businesses can use information derived from data to increase their efficiency and success in many ways, like automating processes and gaining in-depth knowledge of target markets. This month, we’ve gained insights from five businesses who are front-runners in the data analytics game.

 

#1 – AMAZON

“The next time you contact the Amazon help desk with a query, don’t be surprised when the employee on the other end already has most of the pertinent information about you on hand. This allows for a faster, more efficient customer service experience that doesn’t include having to spell out your name three times.” Eleanor O’Neill: Writer at ICAS.

Amazon, the online retail giant, has mastered the art of ecommerce. By embracing cutting edge technology to analyse and make use of the massive amount of customer data they have access to, they have become the pros of supply chain optimisation, price optimisation and fraud detection. With sophisticated advertising algorithms, and leveraging their
Amazon Elastic MapReduce platform for machine learning, the company has built an empire by providing goods to their customers faster and cheaper than their competitors, as well as exceptional customer service.

“Amazon.com Inc is a leader in collecting, storing, processing and analysing personal information from you and every other customer as a means of determining how customers are spending their money. The company uses predictive analytics for targeted marketing to increase customer satisfaction and build company loyalty.” – Jennifer Wills: Owner of JDW Writing.

 

#2 – GOOGLE

“Google is of course an expert in Big Data. They have developed many open source tools and technologies that are widely used in the big data ecosystem. Using many different Big Data techniques, it is capable of sifting through millions of websites and petabytes of data and to give you the right answer within milliseconds. How do they do that?” – Datafloq.

Aside from their impressive search engine, google’s strategy of mining data and placing targeted ads in front of customers who have used free google products before has been a key factor in their success, allowing them to track customers based on their behavior and interests. Google’s service offering to businesses looking to get their ads in front of the right customers has been a huge revenue builder for the organisation.

“Google has not only significantly influenced the way we can now analyse Big Data (think MapReduce, BigQuery, etc.) – but they probably are more responsible than anyone else for making it part of our everyday lives. I believe that many of the innovative things Google is doing today, most companies will do in years to come. Although these days Google’s Big Data innovation goes well beyond basic search, it’s still their core business.” – Bernard Marr: Founder & CEO of Bernard Marr & Co.

 

#3 – NETFLIX

With a user base of approximately 99 million, data scientists at Netflix collect and analyse a colossal amount of behavioral data to reveal insights for decision-making in a way that differentiates them from competitors like Stan and Amazon Prime Video.

“From predicting the kind of content that would garner high viewership to recommending content to specific users, Netflix uses data everywhere. In fact, since its days of being a DVD-by-mail service, Netflix placed prime importance on collecting user data and building a recommendation system. Cinematch was the first algorithm behind their recommendation system. After launching their streaming media service in 2007, it took them 6 years to collect enough data to predict the sure-shot success of their first original production ‘House of Cards’. Data accumulated from numerous sources influence decisions regarding shows. Not only user data, Netflix also observe data generated by piracy sites. “Prison Break” is a hit show on that front.” – Toai Chowdhury: Author at upX Academy.

 

#4 – AMERICAN EXPRESS

“The AMEX team now comprises 800 data scientists globally. American Express claims the lowest fraud loss rate on their records, and among the lowest in the industry. The company states that benefits from fraud improvement alone have paid for their investments in Big Data.” – Randy Bean: CEO & Founder of NewVantage Partners LLC.

AMEX has improved their identification of customer attrition using IBM’s SPSS predictive analytics modelling software. The model delivers a list of prospective customers at highest risk, which allows the organisation to communicate with methods such as direct marketing and follow-up calls.

“American Express increasingly is moving away from focusing on its traditional function of providing credit for consumers and providing merchant services for processing transactions, and toward actually making the connection between consumers and the businesses that want to reach them. The company is using its vast data flows to develop apps that can connect a cardholder with products or services. One app looks at past purchase data and then recommends restaurants in the area that the user is likely to enjoy.” – Bernard Marr: Founder & CEO of Bernard Marr & Co.

 

#5 – APPLE

“With the help of Big Data Analytics and Hadoop cloud, Apple has positioned itself as not just one of the best tech companies around, but one of the best companies period. That reign will likely continue into the future as Apple utilises Big Data in new and exciting ways.” – Jonathan Buckley: Founder & Principal of The Artesian Network LLC.

Apple’s partnership with enterprise experts like Cisco, Deloitte, IBM and SAP has impacted their success as a powerful presence in the mobile market, with millions of loyal customers around the world. The wide range of apps they have released for banking, insurance, travel and entertainment; and the launch of wearable devices like the iWatch, Apple is collecting more customer data than ever before.

“As well as positioning itself as an ‘enabler’ of Big Data in other people’s lives, it has also been put to use in its own internal systems. Apple has often been secretive about the processes behind its traditionally greatest strength – product design. However it is known that Big Data also plays a part here. Data is collected about how, when and where its products – Smart phones, tablets, computers and now watches – are used, to determine what new features should be added, or how the way they are operated can be tweaked to provide the most comfortable and logical user experience.” – Bernard Marr: Founder & CEO of Bernard Marr & Co.

 

 

For more resources, please see below:

10 Companies That Are Using Big Data

How Companies Are Using Big Data & Analytics

6 Ways To Win In Business With Big Data Analytics

16 Case Studies of Companies Proving ROI of Big Data

 

Google

Wow! Big Data At Google

How Google Applies Big Data To Know You

What Would Google Do? Leveraging Data Analytics To Grow Your Organisation

 

Apple

How Apple Is Using Big Data

How Apple Uses Big Data To Drive Business Success

 

Amazon

Amazon EMR

How Amazon Is Leveraging Big Data

7 Ways Amazon Uses Big Data To Stalk You

How Amazon Became The World’s Largest Online Retailer

 

American Express

Inside American Express’ Big Data Journey

American Express Charges Into The World of Big Data

How Predictive Analytics Is Tackling Customer Attrition At American Express

 

Netflix

Big Data: How Netflix Uses It To Drive Business Success

How Netflix Uses Big Data Analytics To Ensure Success

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

If you’re looking for a seamless, fast-scaling machine learning platform, IBM PowerAI might be the deep-learning solution you’re after. Offering a collection of the most popular open source frameworks for deep learning in one installable package, PowerAI simplifies 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

Key Players In Automation & Artificial Intelligence

“Innovations in digitisation, analytics, artificial intelligence, and automation are creating performance and productivity opportunities for business and the economy.” – McKinsey & Company.

With the rise of artificial intelligence and automation, we’ve seen a huge shift in how many jobs are being done in industries like agriculture, logistics, manufacturing and much more. As technology continues to advance at a rapid place, the number of machines performing data analysis and cognitive tasks are multiplying.

We’ve rounded up a few of the most popular automation and artificial intelligence platforms today.

 

#1 – DeepMind Technologies

Created to push boundaries, the founders behind DeepMind, a world leader in AI research, believe that this will be one of the most beneficial scientific advances ever made. Acquired by Google in 2014 and backed by investors like Elon Musk, Peter Thiel and Li Ka-shing, the company’s mission is to ‘solve intelligence.’

“I think we’re going to need artificial assistance to make the breakthroughs that society wants,” Hassabis says. “Climate, economics, disease — they’re just tremendously complicated interacting systems. It’s just hard for humans to analyse all that data and make sense of it. And we might have to confront the possibility that there’s a limit to what human experts might understand. AI-assisted science will help the discovery process.” – Demis Hassabis: Founder & CEO of DeepMind.

 

#2 – IBM Automation With Watson

With Watson, companies are able to get actionable insights through the combination of automation and analytics. It promises to deliver more value to customers and make your employees more productive by delivering the ideal balance between cost and performance.

“IBM Automation With Watson has the capability to understand natural language, think, learn and get smarter over time. This level of automation involves more than just replacing redundant tasks with software, It’s capabilities that are enabled by analytics, cloud, mobile and cognitive computing.” – IBM.

 

#3 – Amazon Echo

This artificially intelligent bluetooth speaker can make your house a whole lot smarter. Now available for purchase to the public, this voice- controlled assistant is being called ‘the future of home automation.’

“Amazon Echo is a hands-free speaker controlled with your voice. It features a personal assistant called Alexa, who will perform various tasks for you and control various systems. There are seven microphones within Echo, all of which feature enhanced noise cancellation and far field voice recognition, meaning you can ask Alexa a question from any direction, even when playing music, and she should still hear you.” – Britta O’Boyle: Features Editor at Pocket-lint.

Got any questions about AI & Machine Learning? Check out Context’s partnership with Amazon Web Services.

 

#4 – Google Home

Google Home, powered by Google Assistant, launched in Australia earlier this year as Amazon Echo’s rival in the home automation game; But which voice assistant you prefer is based on your priorities, what services you’re already subscribed to and whether or not they would be compatible with the device.

“While Amazon may have a head start, Google’s been doing AI and voice commands for years, so both devices are pretty powerful already. Of course, Amazon has already proven that it will add new updates to the Echo regularly, but we’ll have to wait and see if Google will keep up that same pace.” – Eric Ravenscraft: Writer at Lifehacker Australia.

 

 

 

For more resources, please see below:

Google Home

DeepMind: Inside Google’s Super-Brain

IBM Shaping The Future Of Cognitive Automation

What’s Now And Next In Analytics, AI & Automation

The Age Of Analytics: Competing In A Data-Driven World

IBM Watson takes on IT Services With New Automation Platform

Amazon Echo Is The First Artificial Intelligence You’ll Want At Home

Smart Home Assistant Showdown: Amazon Echo Vs. Google Home

Amazon Echo: What Can Alexa Do & What Services Are Compatible?

Amazon Echo Vs. Google Home: Which Voice Controlled Speaker Is Best For You?

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

Implementing this service could mean a drastic increase in the productivity of your data science team by allowing them to create, deploy and manage high quality self-learning behavioral 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

This data science platform could be a great asset to your business, with the ability to work with both R and Python. Anaconda is a leading 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

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

 

Data Empowering Artificial Intelligence & Machine Learning

#1 – FASTER & SMARTER DECISIONS

Digital transformation through Artificial Intelligence has led to more agile, productive and smarter businesses. Automation and machine learning are helping companies save time and money, personalise customer service and detect fraud while also improving work processes and expanding top-line growth.

“Artificial Intelligence or AI, has become pervasive in business in every industry where decision making is being fundamentally transformed by Thinking Machines. The need for faster and smarter decisions and the management of Big Data that can make the difference is what is driving this trend.” – James Canton: CEO & Chairman of The Institute of Global Futures.

 

#2 – DATA-DRIVEN AI & MACHINE LEARNING

“Machine Learning is a current application of AI based around the idea that we should really just be able to give machines access to data and let them learn for themselves.” – Bernard Marr: Founder & CEO of Bernard Marr & Co.

With data science reaching new capabilities for industry disruption, the correlation of data and Artificial Intelligence has powerful potential; and with advancements in machine learning becoming more accessible, it can now be applied to resolve actual business problems.

“The ability to access large volumes of data with agility and ready access is leading to a rapid evolution in the application of AI and machine-learning applications.” – Randy Bean: CEO of NewVantage Partners.

 

#3- ARTIFICIAL INTELLIGENCE VS. HUMAN INTELLIGENCE

“There have been multiple reports recently which claim that a major part of the human workforce will be replaced by automatons and machines in the years to come. With excessive research and development being conducted in the field of artificial intelligence, many fear that a major job crisis will unfold since multiple jobs are more accurately and efficiently performed with the utilisation of machines.” – Brent Morgan: Founder of Transcendent Designs LLC.

With all the benefits of Artificial Intelligence comes the growing fear of job crises. Will AI help or hinder our career opportunities? All though it’s hard to argue the fact that intelligent machines are in fact reliable when it comes to logical decision-making, there are still aspects of human intelligence that machines cannot mimic, like our emotional intelligence. Some argue that the combination of Human Intelligence and Artificial Intelligence will create more opportunities, not less.

“Machine Intelligence can help augment people to do their jobs by making them smarter in a situation, make better decisions, and offer greater engagement with customers.” – Charles Babcock: Editor at Information Week.

“HI is what defines us as humans and our relationship with everything on earth. Now, through the combination of HI and AI, we are at the brink of intelligence enhancement, which could be the most consequential technological development of our time, and in history.” – Bryan Johnson: Contributor at Techcrunch.

 

#4 – KEY TECHNOLOGIES

“The market for Artificial Intelligence (AI) technologies is flourishing. Beyond the hype and the heightened media attention, the numerous startups and the internet giants racing to acquire them, there is a significant increase in investment and adoption by enterprises.” – Gil Press: Managing Partner at gPress.

Australian startups such as Aipoly, creators of an app that combines image- recognition algorithms with smartphones to give instant feedback on surroundings for the visually impaired, have made a huge impact using Artificial Intelligence.

“People have told us that they’ve just started crying when they used it. They’ll say, ‘I have 200 apps on my phone and none of them have made the difference in my life that Aipoly has. It’s an amazing impact you can have on the life of someone that can’t see.” – Marita Cheng: Co-Founder of Aipoly.

There are a variety of AI tools and technologies taking the world by storm, among the most popular being deep learning platforms that provide algorithms such as FluidAI & MathWorks, biometrics for image and touch recognition like Affectiva and 3VR, and natural language processing tools used for fraud detection like Coveo and Sinequa.

 

For more resources, please see below:

The Business of Artificial Intelligence

Top 10 Hot Artificial Intelligence (AI) Technologies

These Emerging Technologies Will Play Critical Roles

Artificial Intelligence: Can It Replace Human Intelligence?

Data To Analytics To AI: From Descriptive To Predictive Analytics

How Big Data Is Empowering AI & Machine Learning At Scale

8 Ways Machine Learning Is Improving Companies’ Work Processes

Big Data & IoT Benefit From Machine Learning, AI Apocalypse Not Imminent

Meet The Australian Startup Using Artificial Intelligence To Help Blind People See

The Combination Of Human & Artificial Intelligence Will Define Humanity’s Future

Meet The Startups That Bring Artificial Intelligence To Log Management & Analysis

How Big Data Is Changing The Customer Experience & Improving SEO

The most powerful driver of success is a great customer experience, and almost every organisation is placing this at the core of of their strategy. But in order to provide excellence at every touchpoint of the customer journey, businesses must utilise data in the best way possible to understand their customers.

Digital innovation is accelerating our ability to do this at a great scale. Personalisation, optimisation and better-targeted campaigns are among the many benefits of insights derived from Big Data Analytics. But where do you start?

 

#1 – CROSS-CHANNEL ANALYTICS

“While volume, variety and velocity are good, value is always better.” – Experian.

The primary goal of digital marketers today is getting an ROI on their marketing efforts. Analysing the activity of customers across different channels, seeing which channels are performing better and how they work together can help you make the most out of your marketing budget. Finding golden pieces of information out of the mass volume of data your organisation has collected isn’t always easy. But digital marketers are finding tons of valuable data through the multi-channel funnel of google analytics, providing a single view of all the information needed to understand customer behaviour and measure performance.

“Cross-channel analytics seeks to provide insight into the path that the customer takes to conversion. This can take multiple forms; including understanding which channels combine to drive conversion, what popular paths are across and within channels, and finally detailed analysis of specific visitor paths.” – Matt Lawson: Managing Director of Ads Marketing at Google.

 

#2 – CUSTOMER DATA MANAGEMENT

“Businesses grapple with huge quantities and varieties of data on one hand, and ever-faster expectations for analysis on the other. The vendor community is responding by providing highly distributed architectures and new levels of memory and processing power.” – Doug Henschen: VP & Principal Analyst at Constellation research, Inc.

Data analytics tools help you collect, recover and categorise customer data according to profile, geolocation, purchase history and preferences in order to keep track of customer behaviour and target people more effectively.

“The social attributes of your customers is important if you want to deliver them highly satisfying experience for your business. Big Data can give valuable information regarding the social interactions of your customers, what their shopping preferences are and the kind of products they regularly need.” – Jason Bowden: Portfolio Manager of Enterprise Data Platforms at Cox Automotive Inc.

 

#3 – IMPROVING THE CUSTOMER JOURNEY

Are you frustrated by online shoppers who don’t make it past the first page of the checkout? You’re not the only one. There are many things you can do improve the online customer experience like good web design, helpful site search and making sure that navigating through your website is made as easy as possible.

Big Data Analytics helps us explore the best pathways to success/conversion. For example, analysing how your payment gateway optimises your customer experience.

“Customer psychology is enough of a minefield all by itself – you really don’t want to give them any additional reasons to abandon the cart.” – Shopify.

 

#4 – SEARCH ENGINE OPTIMISATION

“If you can’t measure it, you can’t improve it.” – Kissmetrics.

Using data science to get valuable insights into your website’s performance can answer a lot of your SEO questions so you can make better informed decisions and and be well on your way to higher keyword rankings and brand awareness. But the key with search engine optimisation is patience, as these results may not appear overnight. However, tracking our progress has become much easier with tools such as Tableau, BigQuery, Google Search Console and Google Analytics.

“The more information you possess, the better results you’ll see from your SEO strategies.” – Liza Perstneva: Corporate Speaker Coordinator at SEMrush.

 

#5 – DATA ACCURACY

“Improving the customer experience is the end game, but getting there requires more than data. It requires the right data, from multiple channels, integrated to give a holistic picture of the customer journey.” – Harvard Business Review.

In order to make decisions that are customer-centric, you need to ensure that the data you’re analysing is a true reflection of what’s really happening. Fortunately, popular cleansing tools like DataWrangler and OpenRefine can help your business generate more trustworthy insights, and even speed up information processing.

Going the extra mile when analysing your data pays off, and certain platforms like Apache Spark and Apache Hadoop can provide you with the efficient and accurate processing of data you’re looking for.

 

For more resources, please see below:

 

Cross-Channel Analytics:

Harnessing Big Data For Cross-Channel Success

Benefits Of Cross-Channel Analytics For Search Marketers

 

Improving The Customer Journey:

Marketing Analytics Can Improve The Customer Experience

 

Customer Data Management:

Journey To Customer Insight

Digital Marketing Optimisation

7 Ways To Effectively Manage Your Customer Data

5 Ways To Optimise Customer Experiences With Big Data

 

Search Engine Optimisation:

How Data Science Can Impact SEO

How Your Payment Gateway Impacts Your Customer Experience

Deliver an Excellent Customer Experience Using Big Data

4 Checkout Conversion Killers That May Drive Your Buyers Away

 

Data Accuracy:

How To Clean Your Data Quickly In Five Steps