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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

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

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

 

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

July Insights On Big Data & Business Transformation

It’s been a busy month in Big Data, and we’ve been deep in learning about what factors are driving the industry. We’ve translated our experience, research and insight into five key lessons.

Here’s what we’ve learnt this month.

 

#1 – PLAN & TEST FIRST, INTEGRATE LATER

“Big data has proven to be a valuable business asset, but using it to gain competitive advantage requires the right combination of strategy, technology and execution.” – Mahmood Majeed: Managing Partner at ZS Associates.

In all the excitement of implementing a Big Data strategy, It’s easy to get caught up in the hype of new technologies promising to do magical things with your data, and make hasty decisions that once integrated, can be disappointing.

To avoid this, team leaders of Big Data projects must ensure that expectations and output are aligned.

“Start by developing a strategy across the entire enterprise that includes a clear understanding of what you hope to accomplish and how success will be measured.” – Harvard Business Review.

 

#2 – SECURITY IS NOT AN AFTERTHOUGHT, IT’S A NECESSITY

“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.

With the volume of worldwide data reaching unprecedented levels, new cyber security 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.”

 

#3 – IT’S ABOUT QUALITY, NOT VOLUME

“Because Big Data presents new features, its data quality also faces many challenges.” – Li Cai & Yangyong Zhu: Fudan University.

As data evolves, new challenges emerge which is why it’s important for businesses to develop data quality standards. With the rise of insight-driven business models, the quality of the data used is key to making the right decisions. Leaders of Big Data projects must ensure that the data is accurate for the intended use.

“Data quality depends not only on its own features, but also on the business environment using the data, including business processes and business users.” – Data Science Journal.

 

#4 – BIG DATA + AGILE = SUCCESS

“The longer you take to find the data, the less valuable it becomes.” – Wired.

With recent advancements in technology, more emphasis is being placed on data agility and the importance of data-driven insights in real-time. “How fast can you extract value from your mountains of data and how quickly can you translate that information into action?” – tdwi.org.

Ian Abramson, former Director at EPAM Systems, describes the alignment of Big Data and Agile as “the infrastructure and framework to be successful.” He then goes on to talk about how this alignment enables focus and a clear picture of how to get from A to B. “ What is the question, what is the success factor, how will we get there, and who will be involved?”

At the 2016 International Conference on Ambient Systems, Networks and Technologies (ANT), experts in the field of Big Data were asked, “ What do you think is most important in the management of Big Data projects?” Interestingly, respondents rated three out of the Four Values of The Agile Manifesto as the highest in their answers, which were: cooperation with customers, people & communication, and working with software over comprehensive documentation.

 

#5 – SKILL-UP OR MISS OUT

“Is the world going to become a place in which automation is everywhere yet employment is scarce?” – Michael Grothaus: The Hanbury Literary Agency.

According to KPMG’s 2016 CIO Survey, data analytics is the most in-demand technology skill for the second year running, but nearly 40% of IT leaders say they suffer from shortfalls in skills in this critical area. “Big Data training is beneficial in meeting the demands of Data Management, faster decision making, better understanding of customers and tapping into the right demographic.” – Vikrant Singh: Senior Manager at Xebia Group International.

In a recent article , Sophia Bernazzani, Marketing Manager at HubSpot says, “The fact remains that some jobs will be replaced by machines – it’s the essence of any industrial or technological revolution. The good news is; some jobs won’t be strictly replaced – they just might be adjusted to account for new technologies.”

So how do we skill-up and adopt these new technologies into our businesses? “By training those already with the company, businesses get to keep valuable team members that already have experience with the enterprise while giving them some much-needed skills.” – Rick Delgado: Enterprise Tech Commentator & Writer.

“Training employees can never be a liability. It is in fact an asset, an investment to the company.” – Big Data Trunk.

 

For more resources, please see below:

 

Data Integration

How To Integrate Data And Analytics Into Every Part of Your Organisation

Charting An Effective Big Data Strategy

Big Data: The Management Revolution

 

Data Quality

Beyond Volume, Variety & Velocity Is The Issue of Big Data Veracity

The Challenges of Data Quality Assessment In The Big Data Era

Big & Fast Data: The Rise of Insight-Driven Businesses

 

Cyber Security

How Big Data Is Improving Cyber Security

Big Data Security Analytics: A Weapon Against Cyber Security Attacks

 

Data Skills

The Importance of Employee Training

Businesses And The Big Data Skills Shortage

Big Data Jobs Are Out There: Are you Ready?

The Importance of Big Data Training to Your Data Analysis Growth

10 Jobs Artificial Intelligence Will Replace (and 10 That Are Safe)

 

Data Agility

Big Data and Agile: The Perfect Marriage

Agile Project Management And Its Use in Big Data Management

Hottest Trends Driving Big Data In 2017

How businesses are using data is evolving, and the rise of new technology is changing data-driven projects for the better.

Here’s some of the hottest trends we’ve observed this year.

 

#1 – THE RISE OF CLOUD

“Enterprise IT had been rapidly changing, and the Cloud is playing an ever-larger factor. Cloud technologies offer unprecedented resource and flexibility for Big Data & Data Science.” – Zmags.

With the growing popularity of cloud computing, there’s been a lot of debate about the pros and cons of moving data from on-premise to cloud environments.

“Contrary to popular belief, on-premise systems were shown to be more expensive to operate annually than their cloud-based counterparts, with the overall cost 60% higher on an annual basis – even after the first year!” – Andrew Heriot: Head of Services EMEA at Maximizer Software.

With many businesses moving to cloud solutions as a way of cutting down costs, other key reasons why they’re deciding to make the switch are simplicity, flexibility, accessibility and experimentation.

“The cloud enables new kinds of possibilities, including inexpensive experimentation that allows businesses to configure ‘best fit’ solutions that satisfy their needs.” – Vasant Dhar: Professor at the Stern School of Business & the Centre for Data Science at New York University.

 

#2 – CYBER SECURITY

“The world will create 180 zettabytes of data (or 180 trillion gigabytes) in 2025, up from less than 10 zettabytes in 2015, according to IDC.” – Forbes.

With worldwide data reaching unprecedented levels, cyber attacks have also become more prevalent. As a result, cyber security through Big Data Analytics has become a major area of investment, and businesses who treat it as an afterthought rather than a major area of concern are leaving themselves vulnerable.

“An ASX survey of the cyber risk facing Australia’s top 100 publicly listed companies found that nearly two-thirds of Australian companies see cyber breaches as an “IT issue” rather than a major business risk, and only 45 per cent of ASX 100 companies are confident in their organisation’s ability to detect and manage a cyber hacking event.” – Alice Uribe: Australian Financial Review.

 

#3 – DATA AGILITY

“It’s not about how much data you can store and process. It’s about data agility. How fast can you extract value from your mountains of data and how quickly can you translate that information into action?” – tdwi.org

Businesses have shifted their focus from capturing and managing data to actively using it for business impact. To achieve this, new tools to discover and explore data are being developed for greater flexibility and speed. A great example of this would be Cloudera Altus, which our partner released earlier this year, taking the deployment of data platforms and data pipelines in the cloud to the next level.

“Our customers wanted to understand how to leverage the agility, scale, and ease-of-use offered by the cloud to efficiently and cost-effectively gain insights from their ever-growing business data.” Jennifer Wu: Director of Product Management at Cloudera.

 

#4 – DATA VISUALISATION

“Having the data is not enough, I have to show it in ways people both enjoy and understand.” – Hans Rosling (1948-2017): Professor of International Health at Karolinska Institutet & Co-Founder of the Gapminder Foundation.

With technology advancing at a rapid pace, data visualisation is constantly evolving. A recent Business2community survey of data professionals found that the data science skill with the highest correlation to project success was data mining and visualisation tools such as Tableau and JavaScript.

“Data visualisation is the best way of engaging decision makers with a visual narrative that leads them to the insight. It also shows the quality of the data, where data is missing, and whether it’s valid with a quick, preliminary visualisation.” – Alex Lane: International Events Coordinator at Innovation Enterprise.

 

#5 – FROM IOT TO IOE

Forget the Internet of Things (IoT). The Internet of Everything (IoE) is re-inventing how we do business by bringing together people, process, data and things to create new capabilities, experiences and opportunities for businesses by connecting them to more valuable networks.

“The Internet of Everything builds on the foundation of the Internet of Things by adding network intelligence that allows convergence, orchestration and visibility across previously disparate systems.” – Cisco.

With digital technology constantly improving products and services, customer expectations are raised and businesses must rise up to meet the challenge. Now that digital is embedded in everything we do, IoT is not enough.

“The Internet of Everything will re-invent industries at three levels: business process, business model, and business moment.” – Hung Le Hong: Research Vice President and Gartner Fellow.

 

#6 – THE STARTUP BOOM

“Big Data has become a crucial growth enabler by empowering companies with deep insights on the internal business processes along with the competitors and market. This exponential demand for data has led to the mushrooming of startups focusing on acquiring, analysing and building innovative products on top of Big Data.”- Jacob Koshy: Content & Social Media Marketer at PromptCloud.

Big Data can achieve big outcomes, but is useless without the skills to analyse it. Recognising the challenge, several startups have emerged with their own solutions to making sense of this data and turning it into actionable insights for businesses.

“When a new startup comes up with technological advancement in Big Data and machine learning, none of the big guys want their competitors getting hold of it. This along with the advantage of having the best tools to handle data makes acquiring such startups a lucrative thing to do for the bigger companies.” – PromptCloud.

Companies like Apple, Microsoft and SAP are acquiring small start-ups for competitive advantage when entering into new markets, creating new business models, and making their enterprises more customer-centric.

 

For more resources, please read the following links:

 

Big Data Trends

Top 10 Big Data Trends 2017

5 Trends Driving Big Data in 2017

Big Data Trends To Watch Out for In 2017

15 Data and Analytics Trends That Will Dominate 2017

6 Predictions For The $203 Billion Big Data Analytics Market

 

Cloud

Five Ways to Move Your Big Data Projects Into the Cloud

The Cloud or Not to Cloud: Where Does Your Data Warehouse Belong?

Industry Experts Discuss Advantages & Risks of Shifting Data Analytics to The Cloud

 

Data Agility

Ready, Set, Go – How Fast Is Your Data?

Why Data Agility is a Key Driver of Big Data Technology Development

 

Cyber Security

How Big Data is Improving Cyber Security

Commonwealth Bank of Australia Years Ahead of Rivals on Cyber Security

 

The Internet of Everything

Internet of Everything FAQ

The Internet of Everything (IoE)

 

Big Data Startups

Why Large Enterprises are Acquiring Big Data Startups

Big Data at Work: Key Lessons from Startups and Large Firms