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

 

Cloudera Acquires Fast Forward Labs To Enhance Machine Learning & AI Research

Given the speed of innovation in the digital realm, it’s exciting to see our partner Cloudera continue to stay ahead of the game with their recent acquisition of Fast Forward Labs (now known as Cloudera Fast Forward Labs), a top-tier applied research and advisory services company.

Cloudera Fast Forward Labs, will concentrate on practical research into new approaches to data science and applying research to business problems that are broadly applicable to a variety of industries and applications.

For the full story, check out Cloudera’s Recent Business & Financial Highlights.

For more resources, please see below:

Data Empowering Artificial Intelligence & Machine Learning

How Big Data Is Changing The Customer Experience & Improving SEO

Cyber Security Strengthened By Big Data Analytics & Machine Learning

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

August Insights On The Future of Big Data & Business Intelligence

Our reliance on data is growing, and the right alignment of IT and business objectives can help us find the answers we’re looking for. With the need to stay competitive by facilitating convenience and exceptional service to our customers, the importance of combining Big Data with business intelligence is a no-brainer.

 

BUSINESS INTELLIGENCE INCREASING ORGANISATIONAL SUCCESS

“BI’s primary objective is achieving better information visibility.” – Brad Cowdrey: Founder & CEO of Clear Peak LLC.

With the vast number of BI tools available, it’s easy to say that Business Intelligence makes our lives a whole lot easier, with the benefits of optimisation, simplified tasks, efficient workload management and even an increase in organisational success.

Businesses are achieving this by either embedding BI software in-house, buying embedding software or outsourcing their software development.

 

THE ALIGNMENT OF BIG DATA & BUSINESS INTELLIGENCE

“BI software has become almost a staple in any data-centric company regardless of size. Big or small, startup or well-rooted enterprise, the idea that data is power has finally begun to stick, and having quality tools to make this happen has become a business priority.” – Inside Big Data.

By collecting data from multiple, disparate systems, analysing and transforming it into meaningful insights and displaying it in an easy to understand format, businesses can utilise data to drive growth more rapidly than ever before.

“Australian organisations can now tap on a rather new concept to help drive business growth and competitive advantage. A combination of business intelligence software and collaboration tools has resulted in what many are calling collaborative business intelligence. Applied to company-wide analytics and reporting, collaborative business intelligence allows for better sharing of data and information and a more team approach to decision-making.” – Dr. Lawrence Ampofo: Founder & Director of Semantica Research.

 

HOW INNOVATION AND COLLABORATION WILL BE IMPACTED

“Every so often, we see a shift in technology that will be the catalyst for innovation. Today, SaaS, cloud computing, social computing and mobile computing are examples of popular new computing models that present an opportunity for innovation in business intelligence.” – Bob Zurek: Senior Vice President & Chief Technology Officer at HealthcareSource.

Cloud computing and data science enable discovery; and discovery leads to new products, services, applications and business models that change the way we do things. Although this presents a challenge to businesses who constantly need to evolve, the technologies we tap into are immensely useful in the process. They also act as an enabler between people and processes, streamlining the communication process and allowing for better collaboration. With devices simplifying the distribution of data flow, business leaders can make the right decisions in real-time.

“Data-driven businesses of the future will be able to tap on technologies and tools to allow peers to analyse data, and change information via the web. Collaboration business intelligence tools of the near future will allow teams to brainstorm ideas conveniently, remotely and seamlessly interact with each other using features like those on social networking websites.” – Wigins.

 

IMPLEMENTING BI AND BIG DATA INTO YOUR ORGANISATION

Business Intelligence is helping us seize opportunities and manage competitive threats, but getting it right is about much more than just building a data warehouse.

“You’ve got to have the right technology and a clear data architecture roadmap, but you’ve also got to have an organisation that can consume information and develop insight. That’s absolutely the number one tip: if you don’t view your BI project as a pervasive cultural approach to business, you might as well not bother starting.” – Martin Draper: Technology Director at Liberty.

Fostering the right culture can happen by educating your team, planning a long-term roadmap and having strategic clarity on what the desired outcome is.

“You need to define what success looks like, such as increasing customer value, increasing market penetration, or generating cost efficiencies. During this step, Big Data project owners should look to secure management buy-in. This will help ensure a strong data-driven culture pervades the organisation, giving projects legitimacy and value.” – David Sharp: CEO of RoZetta Technology.

 

For more resources, please see below:

The Future of Business Intelligence

The Difference Between Big Data & Business Intelligence

5 Ways The Internet of Things Will Change Global Security

How Building A Data Ecosystem Adds Value To Your Business

The Future of Business Intelligence Is Embedded and Conversational

CIO Strategies: Five Best Tips For Implementing Business Intelligence

Big Data And Cloud Computing: Innovation Opportunities and Challenges

Discovering New Innovations In Predictive Analytics And Business Intelligence

How To Make Big Data Work For Your Business In 6 Steps

“If you use bad quality data to make decisions, the insights will be meaningless.” – Florence La Carbona: Enterprise Data Manager at TAL.

Good data is good for business. You can have a vast amount it, and the technical experts to analyse it, but that still doesn’t mean you’ll get the answers you need.

Here’s how to make Big Data work for your business in 6 steps.


#1 – DEFINE YOUR PURPOSE

“Big Data can be a lot like spring cleaning. You can come across a lot of ‘stuff’ you don’t really need, but you still have to dig into it. So where do you start?” – TechRepublic.

In a world saturated with ever-increasing data and information, it’s important to recognise what’s relevant and what isn’t. The best way to do this is to make sure the data you’re using is aligned with the use case at hand.

“Good data quality always depends on the context in which it is used.” – bi-survey.com

Picking the right use case involves clearly defining your business outcomes. What are you trying to achieve with Big Data Analytics?

“The Business outcome will help the organisation stay focused on finding the right match for the business challenge. From there, they can clean and link only the most pertinent data.”- Yoni Malchi: Author at World Wide Technology.

 

#2 – IMPLEMENT THE RIGHT TOOLS

“Are ‘data-rich’ organisations really leveraging their data to support continuous improvement? To succeed at this they must provide user-friendly tools that turn what is often an overwhelming amount of data into actionable insights.” – Menno Veeneklaas & Tibor Schwartz: Partners In Performance.

Gone are the days where businesses struggled extract meaning from mass volumes of raw data and wait several days, even weeks, for results. New software tools have taken the pain out of the process involved in collecting and analysing data. Products like Hadoop, Pig, Hive and Spark allow you to create your own Big Data stack and build your own solution platform.

“New architectural concepts such as data lakes or technologies like Spark and Hadoop require enterprises to rethink their data pipelines, starting at the source where data is produced, to how it is transported and eventually stored and prepared for analysis.” – Kumar Srivastava: Contributor at TechCrunch.

 

#3 – BECOME A DATA DETECTIVE

“The more familiar you are with the data, the easier it is to spot something that seems strange. A good place to start is by looking at the raw data to see what jumps out.” – Matthew Peters: Research Scientist at Allen Institute of Artificial Intelligence.

Although it may seem like a dull task, taking the time to make sure you’re collecting the best data possible will give you a significant advantage when it comes to increasing your profitability and achieving your business goals.

“Working to make sure that your organisation has the most accurate data on its clients possible can seem quite tedious. However, software tools from providers make the process of collecting accurate data simple.” – Experian.

 

#4- KEEP TRACK OF YOUR PERFORMANCE

“Each key performance indicator should be defined to measure the quality, enhancement over time and ways in which to improve a specific set of data.” – Forbes.

The main purpose for implementing a Big Data Analytics strategy in any organisation is to see an improvement in performance by turning insights into solutions that drive competitive advantage.
It’s important to measure the performance of your data analytics project by measuring it against set objectives from its inception through to completion.

“Finding business intelligence in Big Data depends on identifying strong key performance indicators that deliver high value to the business.” – Mary Shacklett: Contributor at TechRepublic.

 

#5 – UNIFY YOUR DATA

“Demand is growing for analytics tools that seamlessly connect to and combine a wide variety of cloud-hosted data sources. Such tools enable businesses to explore and visualise any type of data stored anywhere, helping them discover hidden opportunity in their IoT investment.” – Tableau.

Data can tell you what you need to know, but only if you can see it clearly. By building a single source, 360-degree view of integrated data, your team can access and drive value from a cohesive analytic environment.

“A unified data architecture is a more comprehensive view of the overall enterprise architecture; a collection of services, platforms, applications, and tools that make the best use of available technologies to unleash the optimal value of data.” – tdwi.org.

 

#6 – START WITH PEOPLE, NOT TECHNOLOGY

“How do you harness the power of software-defined solutions, and how do you get yourself ready for the next phase of your business’ IT strategy?” – Logicalis.

Implementing Data Analytics doesn’t start with technology, it starts with people.

“How can the CIO and his team introduce big data into their workflow, and how can they translate what appears to be hieroglyphics to top-level executives in plain language?” – Aberdeen Essentials.

It all starts with knowledge. This involves de-mystifying the common buzz words around Big Data and Analytics, so that your team can communicate effectively about what you are trying to achieve. The second step is to approach Data Analytics in a way that’s relevant to your team.

“Ask questions about the pain points that people feel in their everyday jobs. This presents data analysis as the solution you know it can be, rather than the burden someone else may see it as. Approach your intent to get your office on board with data analysis as a way to make the team even stronger, and a way to empower each individual to do his or her job better, and to make better informed decisions.” – Kelli Simpson: Former Marketing Manager at DataHero.

 

For more resources, please see below:

Data Quality Importance

Unified Data Architecture

Top Ten Big Data Trends For 2017

From Big Data To Real-Time KPIs

Setting a KPI Course For Big Data

Big Data Project: Objectives First, Plan Second

How To Maintain A High-Quality Big Data Company

Nine Tips To Improve Data Quality & Improve Decisions

The Importance of Data Quality: Good, Bad, or Ugly

Getting Your Organisation To Embrace Big Data Analytics

Merging Key Performance Indicators With Big Data Analytics

For Analytics To Be The Answer, You Need The Right Use Cases

How To Measure The Success of Your Big Data & Analytics Strategy

Data Quality & Master Data Management: How To Improve Your Data Quality

“Big Data”, Business Intelligence (BI) and Key Performance Indicators (KPIs)

Five Tips For Data Efficiency

At Contexti, we’re always looking for new ways to make it easier to work with data.

When it comes to Big Data projects, it’s all about efficiency. We’ve rounded up the five best tips on how to make it happen.

 

#1 – DATA COMPRESSION

This can be a great way to reduce repetitive information, have shorter transition times and free up some storage space. The process of encoding data more efficiently to achieve a reduction in file size can happen in two ways: lossless and lossy compression.

“Lossless compression algorithms use statistic modeling techniques to reduce repetitive information in a file. Some of the methods may include removal of spacing characters, representing a string of repeated characters with a single character or replacing recurring characters with smaller bit sequences.” – Conrad Chung: Customer Service & Support Specialist at 2BrightSparks.

The great thing about lossless compression is that no data is lost during the compression process. With lossy compression, data such as multimedia files for images and music can be discarded. Lossy compression on the other hand, works very differently.

“These programs simply eliminate ‘unnecessary’ bits of information, tailoring the file so that it is smaller. This type of compression is used a lot for reducing the file size of bitmap pictures, which tend to be fairly bulky.” – Tom Harris: Contributing writer at HowStuffWorks.

 

#2 – CLOUD OPTIMISATION

“If your organisation wants to extract the highest level of application performance out of the computing platforms that it purchases, you should ensure that workloads are optimised for the hardware they run on.”- Joe Clabby: Contributor at TechTarget.

Choosing the right cloud services to achieve this requires consideration of efficiency, performance and cost advantage. A great tool for workload optimisation is the Cloudera Navigator Optimizer for Hadoop-based platforms.

“Cloudera Navigator Optimizer gives you the insights and risk-assessments you need to build out a comprehensive strategy for Hadoop success.” – Cloudera Inc.

Not only does it reduce risk and provide usage visibility, it’s also flexible and keeps up with changes in demand. “Simply upload your existing SQL workloads to get started, and Navigator Optimizer will identify relative risks and development costs for offloading these to Hadoop based on compatibility and complexity.”

 

#3 – UNIFIED STORAGE ARCHITECTURE

Many enterprises experience the same dilemma: unified storage system or traditional file/block storage system?

Randy Kerns, Senior Strategist & Analyst at Evaluator Group describes unified storage as “ A system that can do both block and file in the same system. It will meet the demands for applications that require block access, plus all of the file-based applications and typical user home directories you have.”

With the ability to simplify deployment and manage systems from multiple vendors, unified storage architecture is growing in popularity among storage administrators who are quickly seeing the benefits of the distributed access and centralised control it provides.

An article in TechTarget highlights the key benefits of running and managing files and applications from a single device: “One advantage of unified storage is reduced hardware requirements. Unified storage systems generally cost the same and enjoy the same level of reliability as dedicated file or block storage systems. Users can also benefit from advanced features such as storage snapshots and replication.”

 

#4 – DEDUPLICATION

“Deduplication is touted as one of the best ways to manage today’s explosive data growth.” – Brien Posey: Technology Author at TechRepublic.

Data deduplication is a technique of eliminating redundant or duplicate data in a data set and as a result, maximising storage savings and increasing the speed and efficiency at which data is processed.
By reducing the amount of storage space an organization needs to save its data, you’re not only saving time and money, but you’re preserving the integrity and security of of your data. “The simple truth is that to be effectively managed, adequately protected and completely recovered, your data size must be shrunk.” – Christophe Bertrand: VP of Product Marketing at Arcserve.

Here’s how it works: “Each chunk of data (e.g., a file, block or bits) is processed using a hash algorithm, generating a unique number for each piece. The resulting hash number is then compared to an index of other existing hash numbers. If that hash number is already in the index, the data does not need to be stored again. Otherwise, the new hash number is added to the index and the new data is stored.” – TechTarget.

 

#5 – CROSS-CHANNEL ANALYTICS

“Cross-channel analytics is a where multiple sets of data from different channels are linked together and analyzed in order to provide customer and marketing intelligence that the business can use. This can provide insights into which paths the customer takes to conversion or to actually buy the product or avail of the service. This then allows for proper and informed decision making to be made.” – Techopedia.

Among the many benefits of this process are understanding the impact of each channel, how they work together and determining which channel combinations get the highest results and conversions. It’s an efficient system that generates insights useful to each department within your organisation.

“Business leaders can use this information to design better process flows for customers by creating or revising customer journey maps. Meanwhile, marketers can use behavioral data from customer interactions in different channels for other purposes.” – TIBCO Blog.

 

For more resources, please see below:

 

Data Efficiency

What Are The Data Efficiency Technologies? – Performance: The Key To Data Efficiency

 

Data Compression

How File Compression Works

How Big Is Your Data, Really?

The Basic Principles of Data Compression

Data Compression: Advantages and Disadvantages

 

Cloud Optimisation

Cloudera Navigator Optimiser

Application Performance Tips: Workload Optimisation and Software Pathing

 

Unified Storage Architecture

Advantages of Using Unified Storage!

Unified Storage (Multiprotocol Storage)

Unified Storage Architecture Explained

Unified Storage Architecture: The Path To Reducing Long-Term Infrastructure Costs

 

Data Deduplication

What Is Data Deduplication?

How Data Deduplication Works

10 Things You Should Know About Data Deduplication

The ABCs Of Data Deduplication: Demystifying The Different Methods

Understanding Data Deduplication – And Why It’s Critical For Moving Data To The Cloud

 

Cross-Channel Analytics

What Is Cross-Channel Analytics?

Big Data Analytics: The Key To Understanding The Cross-Channel Customer

Contexti’s Big Data as-a-Service In The Cloud Just Got Better With Cloudera Altus!

We’re excited by the recent announcement of our partner Cloudera on the availability of Altus, which takes the deployment of data platforms and data pipelines in the cloud to the next level.

“Leveraging AWS cloud and Cloudera Enterprise, Contexti has a track record of providing big data-as-a-service / big data platform services for Australian customers including for Seven West Media’s coverage of the Rio Olympic games.” said Sidney Minassian, Founder & CEO of Contexti. “With the availability of Cloudera Altus we’re looking forward to enhancing our service offering for customers who are leveraging their data for value creation.”

Seven West Media taps Cloudera and Contexti for Big Data Solution for Rio Olympics

Cloudera Altus features include:

  • Managed service for elastic data pipelines
  • Workload orientation
  • Backward compatibility and platform portability
  • Built-in workload management and analytics
  • Faster cluster provisioning times
  • Integrated security with cloud service provider solutions

To learn more about Altus, read Cloudera’s blog: Simplifying Big Data in the Cloud