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4 Key Themes Emerge As Top Of Mind For Australian Data & Analytics Leaders

We interviewed a number of Australian Data Analytics Leaders from leading industry and research organisations to understand what is top of mind for them when it comes to ‘creating value from data’ and to capture what changes they are seeing in this fast-moving space.

Time to move beyond theoretical ROI

The data driven promise hasn’t delivered for many organisations.  Patience and budget for theoretical ROI has gone.  Impact and real ROI from Data Analytics is essential to the success and survival of data teams and, in some cases, the whole organisation.

  • Clearly define the objectives up front
  • Secure executive level involvement and sponsorship
  • Don’t underestimate the importance of people, culture and transformation in delivering ROI

“The key to creating enterprise value from your data requires greater involvement of analytic leaders in the strategic planning process at the executive level, identifying where analytics can be leveraged to accelerate and enhance organisational strategies.” Peter Inge, General Manager – Data & Analytics, SAI Global

“There is a growing realisation from C-level executives of the value that data analytics can provide to their business. This realisation has arisen because they are increasingly seeing real world applications and tangible benefits to other businesses.” Dr Sanjay Mazumdar, CEO, D2D CRC

“We are shifting from the mindset of ‘what can we do with this data’ to ‘what data do we need to solve this problem’ — and subsequently driving strategic investment into the creation of purpose-led data assets.”
Blair Hudson, Data Analytics Leader

Don’t forget the Transformation in Digital Transformation

Digital transformation, of course, requires human change management.

Whilst shifting to a data-driven culture is recognised as a long term exercise, success of individual data initiatives absolutely hinges on effective change management.  Accounting for this up front is an enabler of success.

  • Identify where challenges to adoption and impact will lie
  • Gain sponsorship and proactively plan for successful adoption
  • Consider structural change if this will unlock realisation of ROI

“Whilst many businesses are standing up insights teams with data analysts and data scientists, organisational barriers remain.  These include building trust in the insights and improving communication, influence and business leadership skills within technical teams.  Establishment of a ‘Business Scientist’ function or dedicated role, one with high soft skills, is an advocate and interface to the business to overcome “buy in” barriers and help translate data science capability to business benefit.
Kari Mastropasqua, Executive General Manager, Data & Analytics – Equifax

“There is a rise of the Chief Data Officer in organisations. This is an important role to explore opportunities where data can be used to enhance the business and champion the cause throughout the company.” Richard Morwood, Head of Innovation, InfoTrack

“Supported decision making, using machine learning and data to augment intelligence rather than automate or replace.  Interpretable machine learning assists acceptance and adoption as users interact with an algorithm in order to gain trust that it’s working as expected.” Ian Hansel Director, Verge Labs

It’s time for Agility – in mindset, technology and execution

Recognising the themes about focused realisation of ROI and keen attention to change management, it is unsurprising to see the Data Analytics Leaders similarly laser-focused on technology that holds up to changing production demands and recognition that the flow of data across your platforms must be carefully managed.  

  • Your solution needs to be low-cost, low-friction, fast-ROI
  • Your technology choice must enable agility, flexibility and scalability
  • DataOps is essential to successful production use of Data Analytics

“A move to a DataOps mindset – Agile, Lean, DevOps style development and deployment of analytic outputs, demonstrating Insights and creating value in far shorter cycles is imperative.” Peter Inge, General Manager – Data & Analytics, SAI Global

“With serverless, even complex architectures can be made robust and faults isolated by taking inspiration from nature and building redundancy into the system: multiple workflows can be set up in the architecture to achieve the same outcome but triggered by different upstream events.” Dr Denis Bauer, Head of Cloud Computing Bioinformatics, CSIRO

“There’s a new breed of tools taking full advantage of the cloud bringing costs down by huge amounts!” Richard Morwood, Head of Innovation, InfoTrack

“Rapid changes in technology means data is exploding.  At the same time, the cost of computing is diving, easing deployment of complex machine learning models.” Kari Mastropasqua, Executive General Manager, Data & Analytics – Equifax

“People are thinking more about what’s required to productionise a machine learning model into something that can be depended upon. Some of the biggest changes are happening in the DataOps space.  More companies are using machine learning to get value from their data and it’s no longer just the large tech companies that are taking machine learning seriously.” Ian Hansel, Director, Verge Labs

You need to be planning for Data Sharing, Ethical AI and Data for Social Good

Ethics in Data Analytics was a backwater topic.  It is now in mainstream media and showing no signs of abating.  Our surveyed Data Analytics leaders observe there is value to be had and that sharing, ethical intent and good governance deliver business value, not just avoidance of reputational risk.

  • There are opportunities in sharing data and knowledge
  • Determine how to share data safely and with the right attribution
  • Ethical AI and using data for ‘social good’ are important to many, including your employees.  Public attention has increased.

“Staying competitive in an environment of ever-increasing speed of technological advancement requires an attitude shift away from proprietary in-house software development towards open access and knowledge sharing.” Dr Denis Bauer, Head of Cloud Computing Bioinformatics, CSIRO

“Data sharing is an important challenge – how to share data and realise value while not breaching privacy.” Dr Sanjay Mazumdar, CEO, D2D CRC

“The next change to come will be with data sharing. This is currently a hard process, for a number of reasons.  However, the potential opportunities that can result will bring focus to overcoming the challenges.” Richard Morwood, Head of Innovation, InfoTrack

“Passionate data scientists are looking for avenues to use their skills for positive social impact, as evidenced by all of the ‘for good’ competitions (such as GovHack), volunteer groups (such as Data for Democracy), thought leaders and individual contributors bringing awareness to the likes of data ethics, consumer privacy, explainable AI, the open data movement, reproducible research and global collaboration (including open source).” Blair Hudson, Data Analytics Leader

Insights On Maximising The Value Of Big Data With Business Intelligence

It’s been a big year for Big Data, with continued advances of interconnected technologies creating immensely large datasets. The challenge that comes with uncovering insights and developing strategies from such a colossal variety of data sources has led to the development of faster and smarter Business Intelligence tools that have changed the way businesses work, interact, collaborate and secure information.

“The new benefits that Big Data Analytics brings to the table are speed and efficiency. A few years ago, a business would have gathered information, run analytics and unearthed information that could be used for future decisions. Today, that business can identify insights for immediate decisions. The ability to work faster – and stay agile – gives organisations a competitive edge they didn’t have before.” – SAS.

Big Data is a corporate asset that’s most valuable when delivered with speed and accuracy, and measured for competitive advantage with the best tools in the market. To increase the opportunities it can bring, an organisation must ensure that there are no shortcomings in the tools used to make sense of information that could magnify their competitive advantage.

“To truly maximise the value from Big Data, your data must reflect the real state of affairs at any given moment in time. Any insights generated by AI apps must be able to adapt rapidly to fluctuations in the dynamic business ecosystem — otherwise, you’re wasting the valuable time of your data scientists and senior leadership.” – Phani Nagarjuna: Founder & CEO of Nuevora Analytics.

 

BI TOOLS IN THE MARKET

A critical driver for making better decisions lies in how data is analysed and how to make sense of the information it uncovers. We’ve compiled an illustrative set of BI tools available in the market for your business to harness the most value from your data in the coming year.

 

Tableau

“Tableau helps people transform data into actionable insights. Explore with limitless visual analytics. Build dashboards and perform ad hoc analyses in just a few clicks. Share your work with anyone and make an impact on your business. From global enterprises to early-stage startups and small businesses, people everywhere use Tableau to see and understand their data.” – Tableau.

 

Qlik

“Search and explore vast amounts of data – all your data. With Qlik, you’re not constrained by preconceived notions of how data should be related, but can finally understand how it truly is related. Analyse, reveal, collaborate and act. Qlik lets you turn data into insights across all aspects of your business.” – Qlik.

 

Microsoft Power BI

“Power BI is a suite of business analytics tools to analyse data and share insights. Power BI dashboards provide a 360-degree view for business users with their most important metrics in one place, updated in real time, and available on all of their devices. With one click, users can explore the data behind their dashboard using intuitive tools that make finding answers easy.” – Microsoft Power BI.

 

Yellowfin BI

“Yellowfin offers the only analytics platform that combines machine learning, visualisation, collaboration and storytelling to provide customers with the quickest time to value.” – Yellowfin.

 

SAS Visual Analytics

“With SAS® Visual Analytics, now everyone can discover, share and collaborate on insights. Visualise data in new ways with features in our new release – including third party customisable graphs, a refined user experience to improve productivity, self-service data preparation, and the power of location analytics to visualise data in new contexts and bring the ‘where’ dimension to the forefront. These enhancements will highlight and provide understanding for key relationships, outliers, clusters and more, revealing vital insights that inspire action.” – Andrei M: CTO at Data Science Central.

 

Sisense

“Sisense’s BI software makes it easy to instantly reveal business insights from complex data – any data source, any size.” – Sisense.

 

Gartner Magic Quadrant For BI & Analytics 2017

There are a lot of players in the BI market, and Gartner Magic Quadrant 2017 highlights which tools are the top performers.

 

BI PREDICTIONS FOR THE COMING YEAR

“We asked users, consultants and software vendors of BI and data management technology to rate their personal view of the importance of twenty trending topics that we presented to them. Data quality/master data management, data discovery/visualisation and self-service BI are the three topics BI practitioners identify as the most important trends in their work.” – BARC’s BI Trend Monitor 2018.

Here in Australia, Contexti’s own experiences, and those reported by our partners, align to these top three trending topics. Whilst visualisation and self-service BI are well recognised, the strength of the current trend toward Data Governance (data quality, MDM, security) has strongly spiked.

 

Data Quality & Master Data Management (MDM)

Master data management is achieved by standardising, matching and consolidating common data elements across Big Data sources such as customer, supplier or product data from disparate applications or silos into a single master view of an organisation’s data.

Data quality plays a big part in this, as post-validation is essential for master data. This includes conducting a baseline assessment to identify any potential data quality issues that must be addressed.

For an organisation to be successful with MDM, a clear strategy must be put in place, including KPIs, data management process, and documentation of data domains.

 

Data Discovery & Visualisation

Data discovery is about mining through the data your business has collected from its many sources by visually navigating through it to detect patterns and outliers. Data visualisation is critical because it facilitates a better understanding among key decision makers in an organisation of what the information represents.

Data discovery tools such as Microsoft Power BI, Qlik Sense and many more have enabled businesses to overcome many business problems through fast access to advanced functions, algorithms and interactive dashboards.

 

Self-Service BI

Self-service BI allows business users to access and work with corporate data even though they do not have a background in data mining or statistical analysis, giving them ability to carry out BI tasks without involving the IT department.

“Functional workers can make faster, better decisions because they no longer have to wait during long reporting backlogs. At the same time, technical teams will be freed from the burden of satisfying end user report requests, so they can focus their efforts on more strategic IT initiatives.” – Information Builders.

 

 

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

 

For more resources, please see below:

Self-Service BI

Big Data Analytics – What It Is & Why It Matters

What’s Big In Big Data: Predictions For 2018

Data Governance, Data Quality & Master Data Management

Data Visualisation Vs. Data Discovery: What’s The Difference?

Key Challenges For Monetising Big Data Powered AI – Analytic Continuity

The Incredible Ways Heineken Uses Big Data, The Internet Of Things and Artificial Intelligence (AI)

Appointing A Chief Data Officer To Increase The Economic Value Of Businesses

“There is a new position, the Chief Data Officer. It’s a good idea, but there has been poor execution. What has been happening is taking a CIO and giving them a new title of CDO. However, it should be the Chief Data Monetisation Officer. The job is to determine how to monetise the data you have available. This should be an economics person rather than IT person.” – Jacob Morgan: Principal of Chess Media Group.

Businesses making it a top priority to bring in a Chief Data Officer are doing so as a means to ensure the quality, governance and performance of their Big Data projects are at their best. The threat of losing opportunities from disruptive innovation and the fear of being unable to manage the exponential growth of data has been a key reason for the large increase in hiring for this role.

“For some organisations today, data has become such an explosive part of business that they have created a Chief Data Officer (CDO) position to reside next to the Chief Information Officer and the Chief Technology Officer. This evolution clearly acknowledges that data in the business setting is separate from the systems running it. Beyond that, it recognises that data has a value that, if fully exploited, can help drive profitable business.” – Wired.

With business acumen, ability to lead change and suitable IT awareness as initial qualifiers, there are many other factors that an executive leader should take into consideration before taking the leap and appointing a Chief Data Officer. Here are a few;

 

#1 – Establishing A Clear Outline Of Roles & Responsibilities

First and foremost, in order ensure the new executive you’re bringing on board is set up on a path to success, it’s important to present the leadership team with a clear definition of roles & responsibilites, and a solid understanding of what the organisation is hoping to achieve. This will help the CDO create a roadmap that is aligned with the organisation’s goals and highlights potential obstacles that need to be addressed, as well as minimise border skirmishes with CIO and CTO peers. The CDO must be suitably empowered and supported to equip them to succeed. The role is unlikely to deliver the required value to the business without authority and support, especially given the CDO’s remit can include challenging existing practices and contributing to digital transformation within the organisation.

“A successful roadmap should divide the implementation into logical phases in order to reduce implementation risk. Phases should be around three months in duration. Taking on all the metrics and goals at the same time or in large chunks is very risky primarily because business users lose interest if they are not engaged on an ongoing basis. Prioritise your roadmap phases in order of importance to your business so that you reap the most benefits from your analytics early in your roadmap and provide justification for additional phases. Strong early success provides the critical mass and positive impression about analytics which leads to stronger business adoption.” – StatSlice.

 

#2 – Building The Right Team

“As well as a financial cost, there’s obviously also a cost in human resources and time. If you have data scientists bumbling their way through hundreds of projects with no clear aim, or decoding terabytes of data you have no clear, immediate use for, they’re likely to be unavailable, or distracted, when something of real value comes along. Having the right people with the right skills in the right place is essential.” – Talend.

Part of the responsibility of a Chief Data Officer is to hire the right team and effectively navigate the success of Big Data projects. In order to put together an A-level team, there needs to be a clear set of qualities, characteristics and expectations of prior experience that the CDO must look out for in the hiring process. A considered approach to recruitment and selection, recognising the change process the business must navigate, will help to select the stand-out candidates that are most suitable for the role.

One example is hiring a Data Scientist. Some of the most important traits include statistical thinking, good communication skills, creativity, curiosity, and of course, the right technical skills.

“A great data scientist has a hacker’s spirit. Technical flexibility is as important as experience, because in this field the gold standards change with an alarming rate. Data scientists work together, love open source, and share our knowledge and experience to make sure that we can move at the speed of demand. If your data scientist is a quick study, you’ve made a sound investment beyond the current trend cycle.” – Datascope Analytics.

 

#3 – Strategic Allocation Of Budget & Resources

“Analytics – the ability to find meaningful patterns in data – can help manage costs, lead to efficiency and better decisions, increase services and make better use of capital.” – Carlos Londono: Global Supply Chain VP at Owens Illinois Inc.

A CDO is responsible for the cost, schedule, delegation of tasks, coaching and technical performance of a Big Data project. In order to be able to implement change, invest in the right technology and systems for processing data, oversee and guide the team and achieve a profitable outcome, effective project management techniques must be adopted to keep track of whether objectives and KPIs are being met.

Among these is also the responsibility to determine which which project management method is most suitable for the project, a popular choice among many organisations being the Agile method.

“By delivering the work in small increments of working – even 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. But the early validation is not the only benefit that Agile brings, it also allows projects to learn from the feedback, take in new or changing requirements and quickly change direction when necessary, without changing the process at all.” – Gino Marckx: Founder & Business Improvement Consultant at Xodiac Inc.

 

 

For more resources, please see below:

The Rise Of The Chief Data Officer

Six Qualities Of A Great Data Scientist

Developing A Business Analytics Roadmap

Where Is Technology Taking The Economy?

Staffing Strategies For The Chief Data Officer

12 Qualities Your Next Chief Data Officer Should Have

Why Businesses That Use “Big Data” Make More Money

Making Data Analytics Work For You – Instead Of The Other Way Around

How To Turn Any Big Data Project Into A Success (And Key Pitfalls To Avoid)

 

To discuss this 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

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

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

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 claims to deliver more value to customers and make your employees more productive by delivering a better 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

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