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 | firstname.lastname@example.org
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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 | email@example.com
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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“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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“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.
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“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.
“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.
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“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.
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