Posts

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 & Augmented Intelligence

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

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

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

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

 

#1 – CONTEXTUAL DISCOVERY

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

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

 

#2 – ENABLING OPPORTUNITIES ACROSS THE ORGANISATION

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

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

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

 

#3 – FASTER & MORE ACCURATE INSIGHTS

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

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

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

 

#4 – SECURITY ANALYSIS

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

 

 

 

For more resources, please see below:

The Hype – And Hope – Of Artificial Intelligence

Augmented Intelligence: What You Should Know

How Augmented Intelligence Helps Businesses Grow

The Shift to Augmented Intelligence and How It Helps Your Organisation

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

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

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

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

 

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

Big Data Analytics – Enabling The Agile Workforce

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

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

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

 

#1 – DECISIONS & DELIVERY IN NEAR-REAL TIME

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

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

 

#2 – TEAM EFFICIENCY & COMMUNICATION

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

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

 

#3 – IMPLEMENTING THE RIGHT TECHNOLOGY

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

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

 

#1 – JIRA

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

 

#2 – PLANBOX

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

 

#3 – ASANA

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

 

For more resources, please see:

Powering The Agile Workplace

Big Data Analytics – What It Is And Why It Matters

How To Choose The Right Technology For Agile Transformation

 

Agile project management tools:

Asana

Planbox

Jira – Atlassian

 

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

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

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

Preparing Your Business For Digital Transformation With Data Science & Cloud Computing

“Modern enterprise technologies generate vast amounts of data, which can be challenging and time-consuming to analyse. By building data science models that are accessible, meaningful, and actionable, however, you can spot new opportunities quickly and speed up decision-making.” – The Infor Blog.

When it comes to speed of execution and reliability of insights, data science is a key component for success. Analysing data patterns allow businesses to build models that create forecasts of what can happen in different scenarios, create solutions and generate actionable insights.

 

THE KEY DRIVERS OF DIGITAL TRANSFORMATION

“Digital transformation can be defined as the acceleration of business activities, processes, competencies and models to fully leverage the changes and opportunities of digital technologies and their impact in a strategic and prioritised way.” – Mark Edmead: IT Transformation Consultant & Trainer at MTE Advisors.

Organisations are faced with the constant challenge of adapting to a changing business landscape in order to remain competitive. This includes keeping up with demand, changes in customer behavior and adopting new and innovative technologies into the business model. The key drivers behind digital transformation include profitability, scalability, and added value propositions to product and service offerings.

“Enterprises should be able to deliver custom applications at the speed of ideas. That’s the way to stay ahead in competition in today’s world. Lowering operational costs and enhancing customer experience is the core of digital transformation.” – Forbes.

When it comes to implementing digital transformation effectively, it’s not just about technology, it’s about building the right team. Organisational culture, the right mindset, good communication skills, and a clear understanding of the digitisation strategy are key players in the speed and effectiveness of digital transformation. Employees that are empowered by IT leaders and have received the right training feel more encouraged and enabled to embrace technology and data-driven decision making.

This is why it’s important to address any skill gaps before digital transformation takes place, and to also have a criteria of skill sets required for new employees, based on the goals your organisation is trying to achieve.

“The people you need to hire are the flexible, innovative and entrepreneurial ones. They’re not afraid to fail. They can pick up new techniques very quickly. They’re curious.” – Talent Sonar.

 

DATA SCIENCE IN THE CLOUD

“Data science is enabling the next generation of enterprise software, resulting in solutions that tell users what is going to happen and what they should do about it today.” – Ben Rossi: Contributor at Information Age.

Exploding data volumes are increasing the complexity of analysis, and with most data scientists running tools like AWS Machine Learning, Azure, Python and R in the cloud, it’s safe to say that data science and cloud computing go hand-in-hand.

Many organisations are investing a lot of time and resources on Big Data and making sure it stays in the cloud, in order to experience benefits like flexibility, ability to collaborate, reduced IT costs, and easy access to data.

“Businesses need to be continuously embracing new online marketing channels, bringing new digitally-evolved products to market, refreshing the value propositions of their offerings, and utilising cloud technologies to enable scaling and globalisation at pace.” – Conservit.

 

DATA STORAGE & ACCESSIBILITY

“Processing data and shifting it to Cloud organisations avails two benefits, including tackling large sets of data for decision making and reducing the overall cost of infrastructure.” – Edureka!

Data storage is a challenge for many businesses, but Cloud computing has made storing and analysing data much easier by simplifying IT management, data maintenance and infrastructure updates.

“Value of data is dependent on frequency and speed of access needed to deliver business requirements. Digital enterprises operate with radically different datanomics than conventional physical businesses. Here, digital information is the business.Yes, that means that there is exponentially more data to store and manage. But it also means a fundamental difference in how that data needs to be stored and managed.” – Ash Ashutosh: Contributor at InfoWorld,

Cloud computing, built around a series of hardware and software that can be accessed remotely through any web browser, can greatly assist businesses looking to improve their service offerings through accessible, machine readable data.

“Cloud computing offers your business many benefits. It allows you to set up what is essentially a virtual office to give you the flexibility of connecting to your business anywhere, any time. With the growing number of web-enabled devices used in today’s business environment (e.g. smartphones, tablets), access to your data is even easier.” – Business Queensland.

 

 

For more resources, please see below:

Benefits Of Cloud Computing

Why Your Business Needs Digital Transformation

5 Effective Steps To Hire For Digital Transformation

Why Digital Transformation Is Not Just About Technology

The Importance Of Data Science With Cloud Computing

Running Scalable Data Science on Cloud With R & Python

How Digital Transformation Disrupted The Storage Industry

How To Digitally Transform Your Business With Data Science

Digital Transformation: Why It’s Important To Your Organisation

Digital Transformation and Innovation In Today’s Business World

How Data Science Is The Driving Force Behind Successful Digital Transformation

Blockchain Technology Securing The Internet Of Things

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

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

 

SECURITY

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

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

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

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

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

 

IMPLEMENTING BLOCKCHAIN TECHNOLOGY INTO YOUR BUSINESS

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

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

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

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

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

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

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

 

 

For more resources, please see below:

 

The Business Blockchain

Bitcoin – The Internet Of Money

A Strategist’s Guide To Blockchain

4 Critical Security Challenges Facing IoT

What Blockchain Is And What It Can Do

3 Ways To Use Blockchain In Your Business This Year

How Blockchain Could Revolutionise The Internet Of Think

Could Blockchain Technology Help You Find Better Employees?

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

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

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

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

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