October Insights From Five Companies Winning With Big Data Analytics

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

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

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

 

#1 – AMAZON

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

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

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

 

#2 – GOOGLE

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

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

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

 

#3 – NETFLIX

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

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

 

#4 – AMERICAN EXPRESS

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

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

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

 

#5 – APPLE

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

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

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

 

 

For more resources, please see below:

10 Companies That Are Using Big Data

How Companies Are Using Big Data & Analytics

6 Ways To Win In Business With Big Data Analytics

16 Case Studies of Companies Proving ROI of Big Data

 

Google

Wow! Big Data At Google

How Google Applies Big Data To Know You

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

 

Apple

How Apple Is Using Big Data

How Apple Uses Big Data To Drive Business Success

 

Amazon

Amazon EMR

How Amazon Is Leveraging Big Data

7 Ways Amazon Uses Big Data To Stalk You

How Amazon Became The World’s Largest Online Retailer

 

American Express

Inside American Express’ Big Data Journey

American Express Charges Into The World of Big Data

How Predictive Analytics Is Tackling Customer Attrition At American Express

 

Netflix

Big Data: How Netflix Uses It To Drive Business Success

How Netflix Uses Big Data Analytics To Ensure Success

Key Players In Automation & Artificial Intelligence

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

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

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

 

#1 – DeepMind Technologies

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

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

 

#2 – IBM Automation With Watson

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

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

 

#3 – Amazon Echo

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

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

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

 

#4 – Google Home

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

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

 

 

 

For more resources, please see below:

Google Home

DeepMind: Inside Google’s Super-Brain

IBM Shaping The Future Of Cognitive Automation

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

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

IBM Watson takes on IT Services With New Automation Platform

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

Smart Home Assistant Showdown: Amazon Echo Vs. Google Home

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

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

Faster & Smarter Insights With Predictive Analytics & Machine Learning

“While new software and hardware is created on a weekly basis, many companies continue to use systems they have used for years. Their hesitation is based on costs, and the possibility things can go horribly wrong. Some established companies have offered Data Analytics-as-a-Service, in part to maximise profits on Data Science programs they had developed for themselves. Additionally, startups with a focus on offering a broad range of Data Analytics services are becoming a reality.” – Keith D. Foote.

With rapid digital growth from innovative and disruptive tools and technologies, businesses are able to achieve results faster and smarter than ever before, using data science techniques to turn Big Data into Smart Data. The data strategy of your business must be able to harness and scale innovation at the pace that it’s happening.

“The old ways of analysing data don’t cut it anymore in the business world. Every day I talk to more enterprises blending their corporate data with sentiment, location and sensor data for more precise insights to grow revenue, gain a 360 degree view of their customers, mitigate risk and operate more efficiently.” – Quentin Gallivan: CEO of Blue Jeans Network.

 

PREDICTIVE ANALYTICS

“From drug discovery to price optimisation, across virtually every industry, more companies are using predictive analytics to increase revenue, reduce costs, and modernise the way they do business.” – Lisa Morgan: Freelance Writer at InformationWeek.

Businesses who utilise data to prepare for what’s ahead are at a competitive advantage. By extracting information from existing datasets to find patterns, trends and insights, businesses are able to forecast the result of future activities, and we’ve discovered some tools that do this really well.

 

RapidMiner

RapidMiner Inc. is a data science platform used to perform predictive analytics, machine learning, data mining, text analytics, business analytics and visualisation, with little or no coding required.

“RapidMiner is a centralised solution that features a very powerful and robust graphical user interface that enables users to create, deliver, and maintain predictive analytics. With RapidMiner, the whole process of modelling to implementation is unhindered.” – financesonline.com.

 

SalesPRISM

SalesPRISM is a customer pattern-recognition tool from Lattice, used for predictive lead scoring and delivering predictive marketing and sales cloud applications to B2B companies. It allows sales teams to better prioritise their leads based on a customer’s likelihood to buy.

“SalesPRISM looks at many factor such as CRM data, site traffic and sales history along with external data that analyses LinkedIn activity and even LexisNexis reports. This Big Data Analytics generates leads for the sales, along with specific guidance on how to approach customers based on past success.” – IDG.

 

GraphLab Create

GraphLab is an open source, parallel framework for machine learning, designed considering the scale, variety and complexity of real world data. It has been successfully used for a broad range of data mining tasks.

“What makes it amazing? It’s the presence of neat libraries for data transformation, manipulation and model visualisation. In addition, it comprises of scalable machine learning toolkits which has everything (almost) required to improve machine learning models. The toolkit includes implementation for deep learning, factor machines, topic modeling, clustering, nearest neighbours and more.” – Analytics Vidhya.

 

MACHINE-LEARNING

Most organisations use machine learning software to develop predictive models that are used in multiple applications such as churn analysis and prevention, fraud analysis and detection and real-time recommendation.

“For those of us who are practicing and developing machine learning technology, it’s no longer sufficient to provide the ability to achieve the most accurate, fast, and scalable predictive insights. Ultimately, for machine learning to impact the world around us in a truly meaningful way, we have to deliver machine learning in a smarter, more usable form.” – Wired.

Machine learning is the modern science of detecting patterns, making predictions, data mining, and advanced/predictive analytics. High performance machine learning can analyse a whole dataset, not just a sample of it. It’s useful for gaining insights from data across multiple channels such as CRM, social media and transactional. The scalability of it allows predictive solutions based on sophisticated algorithms to be more accurate, and also drives the importance of software’s speed of interpretation.

“Analytic solutions based on machine learning are best suited for fast changing data, large variety of unstructured data and the sheer scaling issues associated with Big Data.” – Martin Hack: Executive Chairman of zPREDICTA.

The Machine Learning software that you chose will depend on what your business requirements are, and one of the key things to look at is ensuring that the machine-learning based technology you’re implementing can be integrated with the software environment of the enterprise.We’ve rounded up some of the most popular for you to consider.

 

Amazon Machine Learning (AML)

AML is a largely automated platform that applies machine learning algorithms to data stored in the popular Amazon Web Services Platform, and includes an automatic data transformation tool.

“Amazon Machine Learning (AML) offers companies an easy, highly-scalable on-ramp for interpreting data. Under the umbrella of Amazon Web Services (AWS), launched in 2006, AML offers visual aids and easy-to-access analytics to make machine learning accessible to developers without a data science background, using the same technology fueling Amazon’s internal algorithms.” – Hope Reese: Writer for TechRepublic.

 

IBM Watson Machine Learning Service

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

“IBM Watson Machine Learning is built on IBM’s proven analytics platform, making it easy for developers and data scientists to make smarter decisions, solve tough problems, and improve user outcomes.” – IBM.

 

Anaconda

This data science platform could be a great asset to your business, with the ability to work with both R and Python. Anaconda is a leading package and environment manager for data science, built with different versions of R, Python and their associated packages, allowing for easy management.

“With more than 13 million downloads to date, Anaconda is blossoming into a real phenomenon in a crowded data science field. What made the collection of mostly python-based tools so popular to data hackers – a dedication to openness, interoperability, and innovation – is also also the strategy behind Continuum Analytics’ business expansion, and possibly even an IPO.” – Datanami.

 

Google Cloud Machine Learning

Google Cloud Machine Learning provides users with access to high-level algorithms used by Google Analytics, making it possible to get insights from real-time metrics that will give you a competitive advantage. Users can also build their own models, or use pretrained models that support video analysis, image analysis, speech recognition, text analysis and translation.

“Google Cloud Machine Learning Engine makes it easy for you to build sophisticated, large scale machine learning models that cover a broad set of scenarios from building sophisticated regression models to image classification. It is portable, fully managed, and integrated with other Google Cloud Data platform products such as Google Cloud Storage, Google Cloud Dataflow and Google Cloud Datalab so you can easily train your models.” – Google Cloud Platform.

 

 

For more resources, please see below:

 

Predictive Analytics

6 Practical Predictive Analytics Tools

10 Ways Predictive Analytics Improves Innovation

Real Stories Of Challenges That Slow Digital Transformation

The Future Of Big Data: Smart Data Innovations & Challenges

 

Machine Learning

IBM Machine Learning

Anaconda – Predictive Analytics

Google Cloud Platform

A Tour Of Machine Learning Algorithms

Anaconda Data Science Platform For R, Python Or Both

Should Amazon Be Your AI And Machine Learning Platform?

Amazon Machine Learning – Predictive Analytics With AWS

Use Data To Tell The Future: Understanding Machine Learning

Getting Started With GraphLab For Machine Learning In Python

Why Anaconda’s Data Science Tent Is So Big – And Getting Bigger

Machine Learning Platforms Comparison: Amazon, Azure, Google, IBM

Blockchain Technology Securing The Internet Of Things

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

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

 

SECURITY

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

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

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

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

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

 

IMPLEMENTING BLOCKCHAIN TECHNOLOGY INTO YOUR BUSINESS

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

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

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

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

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

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

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

 

 

For more resources, please see below:

 

The Business Blockchain

Bitcoin – The Internet Of Money

A Strategist’s Guide To Blockchain

4 Critical Security Challenges Facing IoT

What Blockchain Is And What It Can Do

3 Ways To Use Blockchain In Your Business This Year

How Blockchain Could Revolutionise The Internet Of Think

Could Blockchain Technology Help You Find Better Employees?

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

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

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

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

September Insights On Big Data For Marketing, Sales & E-Commerce

#1 – TARGETING THE OMNI-CHANNEL CUSTOMER

“The use of Big Data has become a critical force in growing revenues. Big Data Analytics is helping retailers stay in front of a new breed of consumer, the omni-channel shopper.” – Durjoy Patranabish: Former Senior Vice President of Analytics at Blueocean Market Intelligence.

Over the last decade, the field of marketing has undergone rapid changes, moving from mass-marketing to a more personalised, individual communication approach. Analytics tools allow us to segment customers based on preferences, and track the progress of our marketing campaigns.

“Consumers can now engage with a company in a physical store, on an online website or mobile app, through a catalog, or through social media. They can access products and services by calling a company on the phone, by using an app on their mobile smartphone, or with a tablet, a laptop, or a desktop computer.” – Mike Stocker: Vice President of Business Development at Vidyard.

With multiple channels available to purchase from, marketers are faced with the challenge of providing consistency in the customer experience at every potential touchpoint of their purchasing journey. From monitoring web traffic on Google Analytics to launch promotions at optimal times, to investing in SEO services to boost keyword rankings, to building customer journey maps, marketers need to be in the know-how about what motivates their customers in order to deliver what they’re looking for.

 

#2 – WHAT GETS MEASURED, GETS MANAGED

“The most successful companies are digging deep into the data driven research available to them, giving them a leg up on customer retention and bolstering the bottom line.” – Jennifer Havice: Website Copywriter & Online Marketing Strategist at Make Mention Media & Communications.

Big or small, every business can reap the benefits of data analytics tools that give you the insights you need to increase your marketing ROI. We’ve rounded up some of the most popular tools in the industry.

 

Mixpanel

A platform for following the digital footprint of each of your users across both mobile and web devices. This tool allows for for flexibility and customisation, no matter what your role within the business, so you can get the precise knowledge you’re after about your product or service.

 

Kissmetrics

A popular customer intelligence web analytics platform to help track the customer journey, aimed at businesses looking to optimise their digital marketing and boost conversion rates.

 

Google Analytics

A seamless, all-inclusive picture of your business performance. Google Analytics shows you how your campaigns are doing, which customer channels have the highest conversion rate, and allows to set goals and targets, so you you can track your progress over time.

 

Kapost

Helping businesses “turn content into customers,” this platform is used to drive content operation and realise your b2b marketing strategy. It can be integrated with tools like WordPress, Hootsuite and Marketo.

 

#3 – IDENTIFYING OPPORTUNITIES

“The biggest challenge for most eCommerce businesses is to collect, store and organise data from multiple data sources. There’s certainly a lot of data waiting to be analysed and it is a daunting task for some E-commerce businesses to make sense of it all.” – Jerry Jao: CEO & Founder of Retention Science.

Not only does data analytics increase revenue potential with your current customers, it can also be used to identify and attract new markets to tap into.

“Large online vendors can scale their offerings with Big Data and meet specific customer needs. But Big Data also allows to predict customer needs and enable a future optimisation of the product portfolio. So with Big Data, it is possible to optimise the stock costs.” – Big Data Made Simple.

Online retailers can now make better informed decisions while also forecasting for the future. Wouldn’t you love to know what you’re customers would like to buy in advance, and how much they’d be willing to spend? with predictive analytics, you can.

Predictive analytics involves extracting information from your existing data to determine patterns and predict future outcomes and trends. Platforms like RapidMiner and Lattice help identify potential anomalies, service opportunities, reduce the uncertainty of outcomes and score better sales leads.

 

 

 

For more resources, please see below:

 

The Omni-Channel Customer

What Is Omnichannel?

Targeting Omni-Channel Shoppers

The Definition of Omni-Channel Marketing – Plus 7 Tips

Ten Ways Big Data Is Revolutionising Marketing & Sales

 

Marketing Tools

Kapost

Mixpanel

Kissmetrics

8 Big Data Solutions For Small Businesses

Big Data Trends: Top Eight Analytics Lessons For Business

4 Marketing Analytics Tools That Are Shaping The Industry

 

Identifying Opportunities

Lattice Engines

RapidMiner: Data Science Platform

Why Big Data Is A Must In E-Commerce

How Predictive Analytics Is Transforming eCommerce & Conversion Rate Optimisation

 

 

 

3 Strategies For Getting The Most Value From Your Data Lake

“Big Data’ and ‘data lake’ only have meaning to an organisation’s vision when they solve business problems by enabling data democratisation, re-use, exploration, and analytics.” – Carlos Maroto: Technical Manager at Search Technologies.

A data lake is a storage repository that acts as the central source of all your organisation’s current and historical data, both structured and unstructured. This data is transformed as it moves through the pipeline for things such as analysis, creating quarterly and annual reports, machine learning and data visualisation. The information contained in a data lake can be highly valuable asset, however, without the right structure, your data lake could turn into a data swamp.

Here’s three strategies for getting the most value from your data lake.

 

#1 – BUSINESS STRATEGY & TECHNOLOGY ALIGNMENT

“It’s important to align goals for your data lake with the business strategy of the organisation you’re working to support.” – Bizcubed.

What are the business goals you’re trying to achieve with your data lake? Operational efficiency? Better understanding of your customers? Will your current infrastructure help you achieve this while also maximising your profits? Aligning your goals with the technology you’re planning to implement will not only help you articulate what problem you’re trying to solve, but also improve your chances of gaining executive buy-in and winning the support of your team. The better the plan, the easier it is to identify possible roadblocks and the higher the chance of success.

“As technology teams continue to be influenced by the hype and disruption of Big Data, most fail to step back and understand where and how it can be of maximum business value. Such radically disruptive new business processes can’t be implemented without knowledge gathering and understanding how Big Data technology can become a catalyst for organisation and cultural change.” – Thierry Roullier: Director of Product Management at Infogix, Inc.

 

#2 – INTEGRATION & ARCHITECTURE

“You need to be able to integrate your data lake with external tools that are part of your enterprise-wide data view. Only then will you be able to build a data lake that is open, extensible, and easy to integrate into your other business-critical platforms.” – O’Reilly.

Technology is moving at a rapid place.The tools you use in your business may not cooperate well with your data lake, and may not support the data architectures of tomorrow. During the implementation process, one of the first things to look at is how adaptable your long-term technology investments are.

Big Data architectures are constantly evolving, and it’s important to select flexible data processing engines and tools that can handle changes to security, governance and structure without being too costly to the organisation. Before implementing anything, you need to have a clear vision of what you want the end technical platform to look like, and what components you will need to make that happen.

“Modern data onboarding is more than connecting and loading. The key is to enable and establish repeatable processes that simplify the process of getting data into the data lake, regardless of data type, data source or complexity – while maintaining an appropriate level of governance.” – Bizcubed.

 

#3 – DATA VIRTUALISATION & DEMOCRATISATION

“ Data virtualisation involves abstracting, transforming, federating and delivering data from disparate sources. The main goal of data virtualisation technology is to provide a single point of access to the data by aggregating it from a wide range of data sources.” – TechTarget.

Data lakes and data virtualisation tools work well together to solve different problems and provide a layer of intelligence that results in more agility and adaptability to change.

“ As an example, a virtual layer can be used to combine data from the data lake (where heavy processing of large datasets is pushed down) with golden records from the MDM that are more sensitive to stale copies. The advance optimisers of modern data virtualisation tools like Denodo make sure that processing is done where it is more convenient, leveraging existing hardware and processing power in a transparent way for the end user. Security and governance in the virtual layer also add significant value to the combined solution.” – datavirtualizationblog.com.

Data democratisation is the ability for information in a digital format to be accessible to the average end user. The goal of data democratisation is to allow non-specialists to be able to gather and analyse data without requiring outside help.

“Data must be freed from its silos. Today, it resides in a variety of independent business functions, such as HR, manufacturing, supply chain logistics, sales order management and marketing. To get a unified view of this data, businesses are engaging in a variety of ad-hoc, highly labor-intensive processes.” – Computer Weekly.

 

For more resources, please see below:

Best Practices For Data Lakes

How To Build A Successful Data Lake

Five Keys To Creating A Killer Data Lake

Avoiding The Swamp: Data Virtualisation & Data Lakes

Democratising Enterprise Data Access: A Data Lake Pattern

How To Successfully Implement A Big Data/ Data Lake Project

Top Five Differences Between Data Lakes & Data Warehouses

 

2018 Big Data Predictions

“There are only two certainties in Big Data today: It won’t look like yesterday’s data infrastructure, and it’ll be very, very fast.” – Matt Asay: Head of Developer Ecosystem at Adobe.

Technology and the power of data science have created huge leaps of growth for businesses who utilise it, and it’s no surprise that the mass increase of worldwide data will mean that Big Data will encounter some big changes in the year ahead.

 

#1 – COGNITIVE TECHNOLOGIES

Cognitive technologies are constantly evolving, and becoming more and more capable of performing tasks that require human intelligence.

“It is now possible to automate tasks that require human perceptual skills, such as recognising handwriting or identifying faces, and those that require cognitive skills, such as planning, reasoning from partial or uncertain information, and learning.” – Deloitte University Press.

Cognitive systems like IBM Watson are improving business products, processes and insights by allowing systems to interact with humans more naturally, and understand complex questions posed in natural language.

“Computing systems of the past can capture, move and store unstructured data, but they cannot understand it. Cognitive systems can. The application of this breakthrough is ideally suited to address business challenges like scaling human expertise and augmenting human intelligence.” – IBM.

 

#2 – PRESCRIPTIVE ANALYTICS

“If analytics does not lead to more informed decisions and more effective actions, then why do it at all?” – Mike Gualtieri: Vice President & Principal Analyst at Forrester Research.

Informed decisions lead to better results. Prescriptive analytics incorporates both predictive and descriptive analytics, and is used to determine the best course of action to take in a given situation. It involves a combination of mathematics, analytics and experimentation that help businesses make
better decisions based on logic. When used correctly, it can help businesses optimise production and enhance the customer experience.

“Prescriptive analytics predicts not only what will happen, but also why it will happen providing recommendations regarding actions that will take advantage of the predictions.” – halobi.com

 

#3 – FAST DATA IS THE NEW BIG DATA

“The argument is that big isn’t necessarily better when it comes to data, and that businesses don’t use a fraction of the data they have access to. Instead, the idea suggests companies should focus on asking the right questions and making use of the data they have — big or otherwise.” – Forbes.

Fast data applies Big Data Analytics to smaller datasets in near-real or real time to mine both structured and unstructured data and quickly gain insight on what action to take. With streaming systems like Apache Storm and Apache Kafka, the value of fast data is being unlocked.

“As organisations have become more familiar with the capabilities of Big Data Analytics solutions, they have begun demanding faster and faster access to insights. For these enterprises, streaming analytics with the ability to analyze data as it is being created, is something of a holy grail.” – Dana Sandu: Marketing Evangelist at SQLstream.

 

#4 – MACHINE LEARNING & AUTOMATION

“It’s possible to quickly and automatically produce models that can analyse bigger, more complex data and deliver faster, more accurate results – even on a very large scale. The result? High-value predictions that can guide better decisions and smart actions in real time without human intervention.” – sas.

The learning capabilities of machines are growing at a large scale, and connecting people, processes and products in new and exciting ways.

“Your digital business needs to move towards automation now while ML technology is developing rapidly. Machine learning algorithms learn from huge amounts of structured and unstructured data, e.g. text, images, video, voice, body language, and facial expressions. By that it opens a new dimension for machines with limitless applications from healthcare systems to video games and self-driving cars.” – Ronald Van Loon: Director at Advertisement.

Today, machine learning is transforming online businesses and being used by organisations for a myriad of things like fraud detection, real-time ads, pattern recognition, speech analysis and spam-filtering. But in 2018, machine learning is said to become faster and smarter than ever before, while also making better predictions for the future.

“Now machine learning seems to offer a solution for demand forecasting. With the inherent capability to learn from current data, machine learning can help to overcome challenges facing businesses in their demand variations.” – Dataversity.

 

#5 – AI ENHANCING CYBER SECURITY

“Artificial Intelligence is looking quite interesting for 2018 and the near future with the attempts to apply reinforcement learning to problems, which enables machines to model human psychology in order to make better predictions; or contesting neural networks with generative adversarial networks algorithms which requires less human supervision and enables computers to learn from unlabeled data; making them more intelligent.” – Exastax.

With capabilities of problem-solving and modeling human psychology, enhancements in AI are also said to be a defence mechanism for safeguarding data in the near future.

“Ironically, our best hope to defend against AI-enabled hacking is by using AI. AI can be used to defend and to attack cyber infrastructure, as well as to increase the attack surface that hackers can target, that is, the number of ways for hackers to get into a system. Business leaders are advised to familiarise themselves with the cutting edge of AI safety and security research.” – Harvard Business Review.

 

For more resources, please see below:

 

2018 Big Data Predictions

Big Data Changes Coming In 2018

Why Big Data Is Important To Your Business

Five Key Predictions For Data & Analytics Through 2020

17 Predictions About The Future Of Big Data Everyone Should Read

 

Cognitive Technologies

How To Get Started With Cognitive Technology

Cognitive Technologies: The Real Opportunities For Business

KPMG Invests In Game-Changing Cognitive Technologies For Professional Services

 

Prescriptive Analytics

What Exactly The Heck Are Prescriptive Analytics?

Descriptive, Predictive And Prescriptive Analytics Explained

 

Fast Data

Fast Data: The Next Step After Big Data

The Future Of Fast And Big Data Technologies

 

AI & Cyber Security

Cyber Intelligence: What Exactly Is It?

Top 10 Security Predictions Through 2020

Five Trends In Cyber Security For 2017 And 2018

The Future Of Artificial Intelligence: Prediction For 2018

AI Is The Future Of Cyber Security For Better And For Worse

18 Artificial Intelligence Researchers Reveal The Profound Changes Coming To Our Lives

Cyber Threats Are Growing More Serious, And Artificial Intelligence Could Be The Key To Security

 

Machine Learning & Automation

Machine Learning & Automation – What It Is & Why It Matters

The Future Of Machine Learning: Trends, Observations & Forecasts

 

Data Empowering Artificial Intelligence & Machine Learning

#1 – FASTER & SMARTER DECISIONS

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

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

 

#2 – DATA-DRIVEN AI & MACHINE LEARNING

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

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

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

 

#3- ARTIFICIAL INTELLIGENCE VS. HUMAN INTELLIGENCE

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

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

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

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

 

#4 – KEY TECHNOLOGIES

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

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

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

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

 

For more resources, please see below:

The Business of Artificial Intelligence

Top 10 Hot Artificial Intelligence (AI) Technologies

These Emerging Technologies Will Play Critical Roles

Artificial Intelligence: Can It Replace Human Intelligence?

Data To Analytics To AI: From Descriptive To Predictive Analytics

How Big Data Is Empowering AI & Machine Learning At Scale

8 Ways Machine Learning Is Improving Companies’ Work Processes

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

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

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

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

Cyber Security Strengthened By Big Data Analytics & Machine Learning

Information is the most valuable asset, which is why everyone is recognising the importance of data in business and the economy. But our heavy reliance on information to make decisions requires an understanding of how to protect it.

With increasing data causing new cyber threats to surface daily, data practitioners who are utilising preventative technologies to bridge the security gap are at a competitive advantage when it comes to gaining the trust of their clients. Digital innovation enabled by data and analytics has taken the world by storm and is present in our everyday lives, even on our wrists. With wearable technology and mobile devices collecting a vast amount of information about us, it’s no surprise that security and privacy have become primary concerns.

“The sophistication, ferocity, and scope of attacks have also increased. We’ve moved beyond merely defending against criminals. We’re now fighting back against nation states, organised crime, and a troubling new trend: criminal organisations hacking on behalf of rogue nations.” – TechRepublic

To combat this threat, the use of analytics and machine learning are really adding value to businesses looking to build up their defences.

“Big Data and analytics is showing promise with improving cyber security. 90% of respondents from MeriTalk’s new U.S. government survey said they’ve seen a decline in security breaches.” – SentinelOne.

 

DETECTING & PREVENTING CYBER THREATS

“It’s data that’s getting stolen, but it’s also data that can come to the rescue. You just have to know how to use it in the right way.” – Susan O’Brien: Vice President of Marketing at Datameer.

According to the 2016 Big Data Cybersecurity Analytics Research Report, 72 percent of respondents said that Big Data Analytics played an important role in detecting advanced cyber threats.

Here’s some examples of how businesses can use Big Data Analytics to detect and prevent cyber attacks.

 

#1 – USING HISTORICAL DATA

With worldwide data reaching unprecedented levels, new cyber threats are emerging daily. To combat this, an article in CSO discusses the benefits of using historical data to identify potential cyber attacks while also predicting future events.

“Using this historical data, you can create statistical baselines to identify what is ‘normal’. You will then be able to determine when the data deviates from the norm. This historical data can also create new possibilities for predictive models, statistical models, and machine learning.”

 

#2 – MONITORING EMPLOYEE ACTIVITY

“Employing a system monitoring program where the HR person or compliance officer can replay the behavior of an insider is invaluable.” – Kevin Prince: CEO of StratoZen.

Frequent news headlines about “inside jobs” involving data hacks and leaking of information make it hard to ignore the fact that employee-related breaches are on the rise.

By ensuring that access to sensitive information is limited only to the relevant employees, and appropriate policies and procedures are put in place to protect and monitor the use of information, organisations can prevent security breaches by staff.

“Unauthorised access is when staffers use applications to view files or change data they should not be able to touch. This usually requires another employee, such as a system administrator, to be lax with system access controls. Data theft or destruction can follow.” – Justin Kapahi: Vice President of Solutions & Security at External IT.

 

#3 – EDUCATING YOUR TEAM

Although it’s crucial to take the right security measures, educating your team on how to recognise potential threats is just as important. Cyber criminals are targeting employees in many ways including text, email, phone calls, fake websites and dangerous links that could give hackers possession of an organisation’s most confidential information.

“Hackers routinely target workers who are dangerously oblivious to proper cybersecurity practices. Managers who care about protecting their clients, their firms and themselves must prioritize educating employees of all levels on how breaches occur.” – Tech Center.

 

#4 – DEPLOYING AN INTRUSION DETECTION SYSTEM

Data encryption, multi-factor authentication and firewalls are all common security measures, but another important precaution to take is deploying an Intrusion Detection System (IDS).

“IDS provides an umbrella to the network by monitoring all traffic on specific segments that may contain malicious traffic or have mal-intent. The sole function of a network-based IDS is to monitor the traffic of that network.” – TechTarget.

When deploying an Intrusion Detection System, It’s important to understand the requirements of your business in order to select the one most suitable one for the company’s infrastructure.

“Intrusion detection and prevention should be used for all mission-critical systems and systems that are accessible via the Internet, such as Web servers, e-mail systems, servers that house customer or employee data, active directory server, or other systems that are deemed mission critical.” – IT Business Edge.

 

For more resources, please see below:

8 Ways To Prevent Data Breaches

How Big Data Is Improving Cyber Security

Your Biggest Cyber Security Threat? Your Employees

Hacker Hunting: Combatting Cybercrooks With Big Data

Intrusion Detection System Deployment Recommendations

Challenges to Cyber Security & How Big Data Analytics Can Help

Big Data & Machine Learning: A Perfect Pair For Cyber Security?

Healthcare, Cybersecurity & Innovation In The Wearable Technology Market

Big Data Analytics Strengthen Cybersecurity Postures, Reveals Ponemon Institute Report