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 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.
“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 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’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 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 | firstname.lastname@example.org
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Though many businesses understand the importance of Big Data Analytics and its potential to impact business growth in the areas of marketing, finance and operations, not every organisation knows how to achieve these benefits. One key to unlocking value is harnessing the power of Advanced Analytics.
“Advanced Analytics is the autonomous or semi-autonomous examination of data or content using sophisticated techniques and tools, typically beyond those of traditional business intelligence (BI), to discover deeper insights, make predictions, or generate recommendations.” – Gartner.
Data mining, machine learning, predictive & prescriptive analytics, pattern matching, neural networks and location intelligence are just some of the categories that make up Advanced Analytics. Whilst the applications of Advanced Analytics are many, here are five ways it may help your business.
#1 – RISK MINIMISATION
All businesses have some level of risk, including possibilities of fraud, intellectual property theft and ransomware. Fortunately with advanced analytics, these risks can be measured, identified and acted upon.
“Advanced analytics capabilities enable clearer visibility into the challenges associated with managing the many types of risk in such key areas as operations, regulatory compliance, supply chain, finance, ecommerce and credit. By using analytics to measure, quantify and predict risk, leaders can rely less on intuition and create a consistent methodology steeped in data-driven insights.” – Deloitte.
#2 – INCREASING CUSTOMER LOYALTY
“With improved customer experience and service, and more efficient operations leading to increased customer acquisition and retention, companies are realising what advanced analytics can do for their operations. And as these data-driven strategies take hold, they will become an increasingly important point of competitive differentiation.” – Tribridge Connections.
Advanced analytics is changing the way we engage with customers. We are now able to use data to predict consumer buying behaviours that help with micro-targeting, up-selling and churn management.
#3 – EFFECTIVE PROMOTIONAL STRATEGIES
Ensuring that marketing efforts are effective requires an organisation to invest in promotional strategies that are based on data rather than theory. Today’s business environment requires data to support effectiveness claims and seeks marketing results that are not usually achieved without the sophistication that advanced analytics enables.
Predictive analytics based on machine learning technologies can help in this regard, as the various predictive models can be used for customer segmentation, analysing customer engagement, collaborative filtering, up-selling and prioritising leads.
“Predictive analytics appears to have the potential to double marketing success measures in customer engagement and targeted sales across B2B and B2C industries.” – Daniel Faggella: CEO & Founder of TechEmergence.
#4 – BETTER DECISION MAKING
Data-driven decision making derived from Advanced Analytics enables businesses to decrease costs, increase revenue and achieve regulatory compliance.
“Companies that make better decisions, make them faster and execute them more effectively than rivals nearly always turn in better financial performance. Not surprisingly, companies that employ advanced analytics to improve decision making and execution have the results to show for it.” – Bain & Company.
#5 – IMPROVING EFFICIENCY
“A Big Data and Analytics Implementation can help companies uncover ways to make operations more efficient and effective by improving asset efficiency and streamlining global operations.” – IBM.
Predictive analytics tools such as Optimotive, Infer and SAP Predictive Analytics allow businesses to optimise operations and be better prepared to respond to changes in the marketplace.
Companies that are actively analysing and using data are experiencing the rewarding benefits of staff and operational efficiency. For example, companies can now build forecasting models that accurately predict sales volumes, optimise preventative maintenance or perform optimal resource scheduling. These models are swiftly trained, self-optimise and can accommodate highly complex input considerations, or computations at great scale. This allows businesses to consider the use of data types they’d not previously have been able to harness, such as detailed data from customer website use or use of assets such as machines and vehicles.
To discuss Advanced Analytics and other topics, please contact the team at Contexti – +61 2 8294 2161 | email@example.com
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“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.
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To discuss Augmented Intelligence and other topics, please contact the team at Contexti – + 61 28294 2161 | firstname.lastname@example.org
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.
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Agile project management tools:
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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.
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“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.
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“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.
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.
“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.
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Business Intelligence Tools
Businesses Leveraging Cloud Computing
“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.
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“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.
“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 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 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 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.
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.
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.
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