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Insights On Advanced Analytics Unlocking Opportunities

Though many businesses understand the importance of Big Data Analytics and its potential to impact business growth in the areas of marketing, finance and operations, not every organisation knows how to achieve these benefits. One key to unlocking value is harnessing the power of Advanced Analytics.

“Advanced Analytics is the autonomous or semi-autonomous examination of data or content using sophisticated techniques and tools, typically beyond those of traditional business intelligence (BI), to discover deeper insights, make predictions, or generate recommendations.” – Gartner.

Data mining, machine learning, predictive & prescriptive analytics, pattern matching, neural networks and location intelligence are just some of the categories that make up Advanced Analytics. Whilst the applications of Advanced Analytics are many, here are five ways it may help your business.

 

#1 – RISK MINIMISATION

All businesses have some level of risk, including possibilities of fraud, intellectual property theft and ransomware. Fortunately with advanced analytics, these risks can be measured, identified and acted upon.

“Advanced analytics capabilities enable clearer visibility into the challenges associated with managing the many types of risk in such key areas as operations, regulatory compliance, supply chain, finance, ecommerce and credit. By using analytics to measure, quantify and predict risk, leaders can rely less on intuition and create a consistent methodology steeped in data-driven insights.” – Deloitte.

 

#2 – INCREASING CUSTOMER LOYALTY

“With improved customer experience and service, and more efficient operations leading to increased customer acquisition and retention, companies are realising what advanced analytics can do for their operations. And as these data-driven strategies take hold, they will become an increasingly important point of competitive differentiation.” – Tribridge Connections.

Advanced analytics is changing the way we engage with customers. We are now able to use data to predict consumer buying behaviours that help with micro-targeting, up-selling and churn management.

 

#3 – EFFECTIVE PROMOTIONAL STRATEGIES

Ensuring that marketing efforts are effective requires an organisation to invest in promotional strategies that are based on data rather than theory. Today’s business environment requires data to support effectiveness claims and seeks marketing results that are not usually achieved without the sophistication that advanced analytics enables.

Predictive analytics based on machine learning technologies can help in this regard, as the various predictive models can be used for customer segmentation, analysing customer engagement, collaborative filtering, up-selling and prioritising leads.

“Predictive analytics appears to have the potential to double marketing success measures in customer engagement and targeted sales across B2B and B2C industries.” – Daniel Faggella: CEO & Founder of TechEmergence.

 

#4 – BETTER DECISION MAKING

Data-driven decision making derived from Advanced Analytics enables businesses to decrease costs, increase revenue and achieve regulatory compliance.

“Companies that make better decisions, make them faster and execute them more effectively than rivals nearly always turn in better financial performance. Not surprisingly, companies that employ advanced analytics to improve decision making and execution have the results to show for it.” – Bain & Company.

 

#5 – IMPROVING EFFICIENCY

“A Big Data and Analytics Implementation can help companies uncover ways to make operations more efficient and effective by improving asset efficiency and streamlining global operations.” – IBM.

Predictive analytics tools such as Optimotive, Infer and SAP Predictive Analytics allow businesses to optimise operations and be better prepared to respond to changes in the marketplace.

Companies that are actively analysing and using data are experiencing the rewarding benefits of staff and operational efficiency. For example, companies can now build forecasting models that accurately predict sales volumes, optimise preventative maintenance or perform optimal resource scheduling. These models are swiftly trained, self-optimise and can accommodate highly complex input considerations, or computations at great scale. This allows businesses to consider the use of data types they’d not previously have been able to harness, such as detailed data from customer website use or use of assets such as machines and vehicles.

 

 

To discuss Advanced Analytics and other topics, please contact the team at Contexti – +61 2 8294 2161 | connect@contexti.com

 

For more resources, please see below:

IT’s 9 Biggest Security Threats

Smarter Insights With Risk Analytics

5 Hottest Trends In Advanced Analytics

The Five Top Uses For Advanced Analytics

Creating Value Through Advanced Analytics

Advance Your Business With Advanced Analytics

Neural Designer | Advanced Analytics At Your Hands

Advanced Analytics Driving Better Business Decisions

What Are Neural Networks & Predictive Data Analytics

Improving Operational Efficiency With Big Data Analytics

Using Machine Learning To Improve Contact Centre Effectiveness

Predictive Analytics For Marketing – What’s Possible and How It Works

Stop Chasing Opportunities You Can’t Win. Using Advanced Analytics For Microsoft Dynamics

Appointing A Chief Data Officer To Increase The Economic Value Of Businesses

“There is a new position, the Chief Data Officer. It’s a good idea, but there has been poor execution. What has been happening is taking a CIO and giving them a new title of CDO. However, it should be the Chief Data Monetisation Officer. The job is to determine how to monetise the data you have available. This should be an economics person rather than IT person.” – Jacob Morgan: Principal of Chess Media Group.

Businesses making it a top priority to bring in a Chief Data Officer are doing so as a means to ensure the quality, governance and performance of their Big Data projects are at their best. The threat of losing opportunities from disruptive innovation and the fear of being unable to manage the exponential growth of data has been a key reason for the large increase in hiring for this role.

“For some organisations today, data has become such an explosive part of business that they have created a Chief Data Officer (CDO) position to reside next to the Chief Information Officer and the Chief Technology Officer. This evolution clearly acknowledges that data in the business setting is separate from the systems running it. Beyond that, it recognises that data has a value that, if fully exploited, can help drive profitable business.” – Wired.

With business acumen, ability to lead change and suitable IT awareness as initial qualifiers, there are many other factors that an executive leader should take into consideration before taking the leap and appointing a Chief Data Officer. Here are a few;

 

#1 – Establishing A Clear Outline Of Roles & Responsibilities

First and foremost, in order ensure the new executive you’re bringing on board is set up on a path to success, it’s important to present the leadership team with a clear definition of roles & responsibilites, and a solid understanding of what the organisation is hoping to achieve. This will help the CDO create a roadmap that is aligned with the organisation’s goals and highlights potential obstacles that need to be addressed, as well as minimise border skirmishes with CIO and CTO peers. The CDO must be suitably empowered and supported to equip them to succeed. The role is unlikely to deliver the required value to the business without authority and support, especially given the CDO’s remit can include challenging existing practices and contributing to digital transformation within the organisation.

“A successful roadmap should divide the implementation into logical phases in order to reduce implementation risk. Phases should be around three months in duration. Taking on all the metrics and goals at the same time or in large chunks is very risky primarily because business users lose interest if they are not engaged on an ongoing basis. Prioritise your roadmap phases in order of importance to your business so that you reap the most benefits from your analytics early in your roadmap and provide justification for additional phases. Strong early success provides the critical mass and positive impression about analytics which leads to stronger business adoption.” – StatSlice.

 

#2 – Building The Right Team

“As well as a financial cost, there’s obviously also a cost in human resources and time. If you have data scientists bumbling their way through hundreds of projects with no clear aim, or decoding terabytes of data you have no clear, immediate use for, they’re likely to be unavailable, or distracted, when something of real value comes along. Having the right people with the right skills in the right place is essential.” – Talend.

Part of the responsibility of a Chief Data Officer is to hire the right team and effectively navigate the success of Big Data projects. In order to put together an A-level team, there needs to be a clear set of qualities, characteristics and expectations of prior experience that the CDO must look out for in the hiring process. A considered approach to recruitment and selection, recognising the change process the business must navigate, will help to select the stand-out candidates that are most suitable for the role.

One example is hiring a Data Scientist. Some of the most important traits include statistical thinking, good communication skills, creativity, curiosity, and of course, the right technical skills.

“A great data scientist has a hacker’s spirit. Technical flexibility is as important as experience, because in this field the gold standards change with an alarming rate. Data scientists work together, love open source, and share our knowledge and experience to make sure that we can move at the speed of demand. If your data scientist is a quick study, you’ve made a sound investment beyond the current trend cycle.” – Datascope Analytics.

 

#3 – Strategic Allocation Of Budget & Resources

“Analytics – the ability to find meaningful patterns in data – can help manage costs, lead to efficiency and better decisions, increase services and make better use of capital.” – Carlos Londono: Global Supply Chain VP at Owens Illinois Inc.

A CDO is responsible for the cost, schedule, delegation of tasks, coaching and technical performance of a Big Data project. In order to be able to implement change, invest in the right technology and systems for processing data, oversee and guide the team and achieve a profitable outcome, effective project management techniques must be adopted to keep track of whether objectives and KPIs are being met.

Among these is also the responsibility to determine which which project management method is most suitable for the project, a popular choice among many organisations being the Agile method.

“By delivering the work in small increments of working – even production ready – software, those assumptions are all validated early on. All code, design, architecture and requirements are validated every time a new increment is delivered, even the plan is validated as teams get real and accurate data around the progress of the project. But the early validation is not the only benefit that Agile brings, it also allows projects to learn from the feedback, take in new or changing requirements and quickly change direction when necessary, without changing the process at all.” – Gino Marckx: Founder & Business Improvement Consultant at Xodiac Inc.

 

 

For more resources, please see below:

The Rise Of The Chief Data Officer

Six Qualities Of A Great Data Scientist

Developing A Business Analytics Roadmap

Where Is Technology Taking The Economy?

Staffing Strategies For The Chief Data Officer

12 Qualities Your Next Chief Data Officer Should Have

Why Businesses That Use “Big Data” Make More Money

Making Data Analytics Work For You – Instead Of The Other Way Around

How To Turn Any Big Data Project Into A Success (And Key Pitfalls To Avoid)

 

To discuss this and other topics, please contact the team at Contexti – + 61 28294 2161 | connect@contexti.com

Big Data Analytics – Enabling The Agile Workforce

In a fast-paced, constantly evolving digital era, time waits for no one. Agility, fast recovery from failure and adaptability to change continue to grow in importance for businesses who want to remain competitive. The capability to be this sort of business requires the right team and right technology.

Agile refers to a type of project management used often for software development. Tasks are divided into short phases of work and plans are frequently reassessed and adapted.

Here’s three ways an agile approach to Big Data Analytics can improve the success of an organisation.

 

#1 – DECISIONS & DELIVERY IN NEAR-REAL TIME

Data science allows us to make sense of the information we collect, and identify the valuable insights hidden in terabytes of structured and unstructured data. That being said, Big Data projects can be uncertain in nature if the right delivery methods are not used. This is where agile comes in.

“The principles and practices that are collected under the Agile umbrella all focus on validating assumptions as early as possible in the delivery lifecycle, significantly reducing the risk exposure as the project continues. By delivering the work in small increments, even with production-ready software, those assumptions are all validated early on. All code, design, architecture and requirements are validated every time a new increment is delivered. Even the plan is validated as teams get real and accurate data around the progress of the project.” – Gino Marck: Head of Agile Competency Center, EPAM Canada.

 

#2 – TEAM EFFICIENCY & COMMUNICATION

For a team to truly embrace agile, an interactive, adaptable and feedback-driven culture must be fostered in the organisation. The ability of a team to communicate progress and change direction when needed is crucial to the success of any Big Data project.

“For example, Ruben Perez, who runs a digital project management team at Scholastic Corporation, has his managers hold a daily scrum. When work is moving fast, you have to ensure that everyone is moving in the same direction, he says. “The scrum manager holds a 15-minute check-in every day to ensure that the tasks that have been slotted for a particular sprint are on track and that nothing is blocking forward progress. Anything that is standing in the way is assigned to someone to resolve—separately.” – The Economist.

 

#3 – IMPLEMENTING THE RIGHT TECHNOLOGY

“Shorter product cycles, compressed delivery times and pressures from a global economy require employees to thrive on change and be empowered to make decisions in near–real time. To power this sort of agility, companies must have the right technology—tools that allow for instant communication, collaboration and centralised platforms. And they’ve also got to establish and nurture an adaptive culture. Changing directions in a large organisation with long-established processes isn’t easy.” – The Economist.

Once the right culture is set in place, working with the right tools is what makes agile possible. An organisation must select the technology that will best cater to the transformation they’re after. Luckily, there’s no shortage of options. We’ve rounded up a few that you may find useful.

 

#1 – JIRA

With key features like issue tracking, bug tracking, kanban boards, workflows and the ability to customise the dashboard to meet the needs of your business, this software is the among the most popular project management tools available.

 

#2 – PLANBOX

Built as a four-level platform supporting Scrum Methodology, Planbox allows members across the business to collaborate, plan, and deliver projects, as well as enabling agile software development. Its features include release management, iterations, stories, backlog, prioritisation, scrum roles, sprints, estimated hours and story points.

 

#3 – ASANA

Asana is the ultimate progress tracker that helps you visualise your team’s work and follow up on individual tasks on a kanban board, calendar or list. It’s a flexible tool that adapts easily to an organisation’s scrum practices. With work efforts and communication in one place, team members can ensure that they have full clarity on sprint plans, milestones, launch dates and backlog.

 

For more resources, please see:

Powering The Agile Workplace

Big Data Analytics – What It Is And Why It Matters

How To Choose The Right Technology For Agile Transformation

 

Agile project management tools:

Asana

Planbox

Jira – Atlassian

 

To discuss this and other topics, please contact the team at Contexti – +61 2 8294 2161
| connect@contexti.com

Deep Learning Technologies Enabling Innovation

“Deep Learning has had a huge impact on computer science, making it possible to explore new frontiers of research and to develop amazingly useful products that millions of people use every day.” – Rajat Monga, Engineering Director at TensorFlow & Jeff Dean, Senior Fellow at Google.

With innovation driving business success, the demand for community-based, open-source software that incorporates AI & deep learning is taking over start-ups and enterprises alike. We’ve rounded up a few successful deep learning technologies that are making a big impact.

 

#1 – TensorFlow

TensorFlow is an open source software library that uses data flow graphs for numerical computation. Nodes in the graph represent mathematical operations, while the graph edges represent the multidimensional data arrays communicated between them. With extensive built-in support for deep learning, TensorFlow can compute any algorithm that can be expressed in a computational flow graph.

“TensorFlow was built from the ground up to be fast, portable, and ready for production service. You can move your idea seamlessly from training on your desktop GPU to running on your mobile phone. And you can get started quickly with powerful machine learning tech by using our state-of-the-art example model architectures.” – Google Research Blog.

 

#2 – IBM PowerAI

Offering a collection of open-source frameworks for deep learning in one installable package, IBM Power AI claims to simplify  the installation and system optimisation required to bring up a deep learning infrastructure.

“PowerAI makes deep learning, machine learning, and AI more accessible and more performant. By combining this software platform for deep learning with IBM® Power Systems™, enterprises can rapidly deploy a fully optimised and supported platform for machine learning with blazing performance. The PowerAI platform includes the most popular machine learning frameworks and their dependencies, and it is built for easy and rapid deployment. PowerAI requires installation on IBM Power Systems S822LC for HPC server infrastructure.” – IBM

 

#3 – Intel Nervana

Nervana Systems, acquired by Intel last year, is now known as Intel Nervana and referred to as ‘the next big shift inside corporate data centers.’

“Nervana has built an extensive machine learning system, which runs the gamut from an open-sourced software platform all the way down to an upcoming customised computer chip. The platform is used for everything from analysing seismic data to find promising places to drill for oil to looking at plant genomes in search of new hybrids.” – Aaron Pressman: Senior Writer at Fortune.

This state-of-the-art deep learning system is made up of curated, enterprise-grade collections of the world’s most advanced deep learning models and is updated on a regular basis.

“The Intel® Nervana™ Deep Learning Studio, a suite of tools with an easy-to-use interface, dramatically simplifies the deep learning process and accelerates time-to-solution. After you import your data, you can extend one of our state-of-the-art models or build your own. Then, you can kick off training with single click and track progress on the dashboard. All the capabilities of the platform are also accessible via a powerful command line interface.” – Intel Nervana.

 

#4 – NVIDIA Deep Learning SDK

‘The NVIDIA Deep Learning SDK provides high-performance tools and libraries to power innovative GPU-accelerated machine learning applications in the cloud, data centers, workstations, and embedded platforms.’ – NVIDIA.

Offering a comprehensive development environment for building new GPU-accelerated deep learning algorithms, and the inclusion of libraries for deep learning primitives, inference, video analytics, linear algebra, sparse matrices, and multi-GPU communications, your business could dramatically increase the performance of existing applications.

“With the updated Deep Learning SDK optimised for Volta, developers have access to the libraries and tools that ensure seamless development and deployment of deep neural networks on all NVIDIA platforms, from the cloud or data center to the desktop to embedded edge devices. Deep learning frameworks using the latest updates deliver up to 2.5x faster training of CNNs, 3x faster training of RNNs and 3.5x faster inference on Volta GPUs compared to Pascal GPUs.” – NVIDIA.

 

 

For more resources, please see below:

IBM Power AI

Intel Nervana Platform

Why Deep Learning Is Suddenly Changing Your Life

Nividia Accelerated Computing – Deep Learning Software

Why Intel Bought Artificial Intelligence Startup Nervana Systems

TensorFlow – Google’s Latest Machine Learning System, Open Sourced For Everyone

Intel Is Paying More Than $400 Million To Buy Deep-Learning Startup Nervana Systems

PowerAI: The World’s Fastest Deep Learning Solution Among Leading Enterprise Servers

Preparing Your Business For Digital Transformation With Data Science & Cloud Computing

“Modern enterprise technologies generate vast amounts of data, which can be challenging and time-consuming to analyse. By building data science models that are accessible, meaningful, and actionable, however, you can spot new opportunities quickly and speed up decision-making.” – The Infor Blog.

When it comes to speed of execution and reliability of insights, data science is a key component for success. Analysing data patterns allow businesses to build models that create forecasts of what can happen in different scenarios, create solutions and generate actionable insights.

 

THE KEY DRIVERS OF DIGITAL TRANSFORMATION

“Digital transformation can be defined as the acceleration of business activities, processes, competencies and models to fully leverage the changes and opportunities of digital technologies and their impact in a strategic and prioritised way.” – Mark Edmead: IT Transformation Consultant & Trainer at MTE Advisors.

Organisations are faced with the constant challenge of adapting to a changing business landscape in order to remain competitive. This includes keeping up with demand, changes in customer behavior and adopting new and innovative technologies into the business model. The key drivers behind digital transformation include profitability, scalability, and added value propositions to product and service offerings.

“Enterprises should be able to deliver custom applications at the speed of ideas. That’s the way to stay ahead in competition in today’s world. Lowering operational costs and enhancing customer experience is the core of digital transformation.” – Forbes.

When it comes to implementing digital transformation effectively, it’s not just about technology, it’s about building the right team. Organisational culture, the right mindset, good communication skills, and a clear understanding of the digitisation strategy are key players in the speed and effectiveness of digital transformation. Employees that are empowered by IT leaders and have received the right training feel more encouraged and enabled to embrace technology and data-driven decision making.

This is why it’s important to address any skill gaps before digital transformation takes place, and to also have a criteria of skill sets required for new employees, based on the goals your organisation is trying to achieve.

“The people you need to hire are the flexible, innovative and entrepreneurial ones. They’re not afraid to fail. They can pick up new techniques very quickly. They’re curious.” – Talent Sonar.

 

DATA SCIENCE IN THE CLOUD

“Data science is enabling the next generation of enterprise software, resulting in solutions that tell users what is going to happen and what they should do about it today.” – Ben Rossi: Contributor at Information Age.

Exploding data volumes are increasing the complexity of analysis, and with most data scientists running tools like AWS Machine Learning, Azure, Python and R in the cloud, it’s safe to say that data science and cloud computing go hand-in-hand.

Many organisations are investing a lot of time and resources on Big Data and making sure it stays in the cloud, in order to experience benefits like flexibility, ability to collaborate, reduced IT costs, and easy access to data.

“Businesses need to be continuously embracing new online marketing channels, bringing new digitally-evolved products to market, refreshing the value propositions of their offerings, and utilising cloud technologies to enable scaling and globalisation at pace.” – Conservit.

 

DATA STORAGE & ACCESSIBILITY

“Processing data and shifting it to Cloud organisations avails two benefits, including tackling large sets of data for decision making and reducing the overall cost of infrastructure.” – Edureka!

Data storage is a challenge for many businesses, but Cloud computing has made storing and analysing data much easier by simplifying IT management, data maintenance and infrastructure updates.

“Value of data is dependent on frequency and speed of access needed to deliver business requirements. Digital enterprises operate with radically different datanomics than conventional physical businesses. Here, digital information is the business.Yes, that means that there is exponentially more data to store and manage. But it also means a fundamental difference in how that data needs to be stored and managed.” – Ash Ashutosh: Contributor at InfoWorld,

Cloud computing, built around a series of hardware and software that can be accessed remotely through any web browser, can greatly assist businesses looking to improve their service offerings through accessible, machine readable data.

“Cloud computing offers your business many benefits. It allows you to set up what is essentially a virtual office to give you the flexibility of connecting to your business anywhere, any time. With the growing number of web-enabled devices used in today’s business environment (e.g. smartphones, tablets), access to your data is even easier.” – Business Queensland.

 

 

For more resources, please see below:

Benefits Of Cloud Computing

Why Your Business Needs Digital Transformation

5 Effective Steps To Hire For Digital Transformation

Why Digital Transformation Is Not Just About Technology

The Importance Of Data Science With Cloud Computing

Running Scalable Data Science on Cloud With R & Python

How Digital Transformation Disrupted The Storage Industry

How To Digitally Transform Your Business With Data Science

Digital Transformation: Why It’s Important To Your Organisation

Digital Transformation and Innovation In Today’s Business World

How Data Science Is The Driving Force Behind Successful Digital Transformation

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

 

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