December Insights On Maximising The Value Of Big Data With Business Intelligence

It’s been a big year for Big Data, with continued advances of interconnected technologies creating immensely large datasets. The challenge that comes with uncovering insights and developing strategies from such a colossal variety of data sources has led to the development of faster and smarter Business Intelligence tools that have changed the way businesses work, interact, collaborate and secure information.

“The new benefits that Big Data Analytics brings to the table are speed and efficiency. A few years ago, a business would have gathered information, run analytics and unearthed information that could be used for future decisions. Today, that business can identify insights for immediate decisions. The ability to work faster – and stay agile – gives organisations a competitive edge they didn’t have before.” – SAS.

Big Data is a corporate asset that’s most valuable when delivered with speed and accuracy, and measured for competitive advantage with the best tools in the market. To increase the opportunities it can bring, an organisation must ensure that there are no shortcomings in the tools used to make sense of information that could magnify their competitive advantage.

“To truly maximise the value from Big Data, your data must reflect the real state of affairs at any given moment in time. Any insights generated by AI apps must be able to adapt rapidly to fluctuations in the dynamic business ecosystem — otherwise, you’re wasting the valuable time of your data scientists and senior leadership.” – Phani Nagarjuna: Founder & CEO of Nuevora Analytics.

 

BI TOOLS IN THE MARKET

A critical driver for making better decisions lies in how data is analysed and how to make sense of the information it uncovers. We’ve compiled an illustrative set of BI tools available in the market for your business to harness the most value from your data in the coming year.

 

Tableau

“Tableau helps people transform data into actionable insights. Explore with limitless visual analytics. Build dashboards and perform ad hoc analyses in just a few clicks. Share your work with anyone and make an impact on your business. From global enterprises to early-stage startups and small businesses, people everywhere use Tableau to see and understand their data.” – Tableau.

 

Qlik

“Search and explore vast amounts of data – all your data. With Qlik, you’re not constrained by preconceived notions of how data should be related, but can finally understand how it truly is related. Analyse, reveal, collaborate and act. Qlik lets you turn data into insights across all aspects of your business.” – Qlik.

 

Microsoft Power BI

“Power BI is a suite of business analytics tools to analyse data and share insights. Power BI dashboards provide a 360-degree view for business users with their most important metrics in one place, updated in real time, and available on all of their devices. With one click, users can explore the data behind their dashboard using intuitive tools that make finding answers easy.” – Microsoft Power BI.

 

Yellowfin BI

“Yellowfin offers the only analytics platform that combines machine learning, visualisation, collaboration and storytelling to provide customers with the quickest time to value.” – Yellowfin.

 

SAS Visual Analytics

“With SAS® Visual Analytics, now everyone can discover, share and collaborate on insights. Visualise data in new ways with features in our new release – including third party customisable graphs, a refined user experience to improve productivity, self-service data preparation, and the power of location analytics to visualise data in new contexts and bring the ‘where’ dimension to the forefront. These enhancements will highlight and provide understanding for key relationships, outliers, clusters and more, revealing vital insights that inspire action.” – Andrei M: CTO at Data Science Central.

 

Sisense

“Sisense’s BI software makes it easy to instantly reveal business insights from complex data – any data source, any size.” – Sisense.

 

Gartner Magic Quadrant For BI & Analytics 2017

There are a lot of players in the BI market, and Gartner Magic Quadrant 2017 highlights which tools are the top performers.

 

BI PREDICTIONS FOR THE COMING YEAR

“We asked users, consultants and software vendors of BI and data management technology to rate their personal view of the importance of twenty trending topics that we presented to them. Data quality/master data management, data discovery/visualisation and self-service BI are the three topics BI practitioners identify as the most important trends in their work.” – BARC’s BI Trend Monitor 2018.

Here in Australia, Contexti’s own experiences, and those reported by our partners, align to these top three trending topics. Whilst visualisation and self-service BI are well recognised, the strength of the current trend toward Data Governance (data quality, MDM, security) has strongly spiked.

 

Data Quality & Master Data Management (MDM)

Master data management is achieved by standardising, matching and consolidating common data elements across Big Data sources such as customer, supplier or product data from disparate applications or silos into a single master view of an organisation’s data.

Data quality plays a big part in this, as post-validation is essential for master data. This includes conducting a baseline assessment to identify any potential data quality issues that must be addressed.

For an organisation to be successful with MDM, a clear strategy must be put in place, including KPIs, data management process, and documentation of data domains.

 

Data Discovery & Visualisation

Data discovery is about mining through the data your business has collected from its many sources by visually navigating through it to detect patterns and outliers. Data visualisation is critical because it facilitates a better understanding among key decision makers in an organisation of what the information represents.

Data discovery tools such as Microsoft Power BI, Qlik Sense and many more have enabled businesses to overcome many business problems through fast access to advanced functions, algorithms and interactive dashboards.

 

Self-Service BI

Self-service BI allows business users to access and work with corporate data even though they do not have a background in data mining or statistical analysis, giving them ability to carry out BI tasks without involving the IT department.

“Functional workers can make faster, better decisions because they no longer have to wait during long reporting backlogs. At the same time, technical teams will be freed from the burden of satisfying end user report requests, so they can focus their efforts on more strategic IT initiatives.” – Information Builders.

 

 

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

 

For more resources, please see below:

Self-Service BI

Big Data Analytics – What It Is & Why It Matters

What’s Big In Big Data: Predictions For 2018

Data Governance, Data Quality & Master Data Management

Data Visualisation Vs. Data Discovery: What’s The Difference?

Key Challenges For Monetising Big Data Powered AI – Analytic Continuity

The Incredible Ways Heineken Uses Big Data, The Internet Of Things and Artificial Intelligence (AI)

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

October Insights From Five Companies Winning With Big Data Analytics

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

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

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

 

#1 – AMAZON

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

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

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

 

#2 – GOOGLE

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

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

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

 

#3 – NETFLIX

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

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

 

#4 – AMERICAN EXPRESS

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

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

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

 

#5 – APPLE

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

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

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

 

 

For more resources, please see below:

10 Companies That Are Using Big Data

How Companies Are Using Big Data & Analytics

6 Ways To Win In Business With Big Data Analytics

16 Case Studies of Companies Proving ROI of Big Data

 

Google

Wow! Big Data At Google

How Google Applies Big Data To Know You

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

 

Apple

How Apple Is Using Big Data

How Apple Uses Big Data To Drive Business Success

 

Amazon

Amazon EMR

How Amazon Is Leveraging Big Data

7 Ways Amazon Uses Big Data To Stalk You

How Amazon Became The World’s Largest Online Retailer

 

American Express

Inside American Express’ Big Data Journey

American Express Charges Into The World of Big Data

How Predictive Analytics Is Tackling Customer Attrition At American Express

 

Netflix

Big Data: How Netflix Uses It To Drive Business Success

How Netflix Uses Big Data Analytics To Ensure Success

Key Players In Automation & Artificial Intelligence

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

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

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

 

#1 – DeepMind Technologies

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

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

 

#2 – IBM Automation With Watson

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

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

 

#3 – Amazon Echo

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

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

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

 

#4 – Google Home

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

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

 

 

 

For more resources, please see below:

Google Home

DeepMind: Inside Google’s Super-Brain

IBM Shaping The Future Of Cognitive Automation

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

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

IBM Watson takes on IT Services With New Automation Platform

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

Smart Home Assistant Showdown: Amazon Echo Vs. Google Home

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

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

Faster & Smarter Insights With Predictive Analytics & Machine Learning

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

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

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

 

PREDICTIVE ANALYTICS

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

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

 

RapidMiner

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

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

 

SalesPRISM

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

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

 

GraphLab Create

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

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

 

MACHINE-LEARNING

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

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

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

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

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

 

Amazon Machine Learning (AML)

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

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

 

IBM Watson Machine Learning Service

The implementation of this service claims to increase productivity of your data science team by allowing them to create, deploy and manage high quality self-learning behavioural models securely and in real-time.

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

 

Anaconda

As a technology that Contexti has been exploring, this data science platform can enable your business to work with both R and Python. Anaconda is a package and environment manager for data science, built with different versions of R, Python and their associated packages, allowing for easy management.

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

 

Google Cloud Machine Learning

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

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

 

 

For more resources, please see below:

 

Predictive Analytics

6 Practical Predictive Analytics Tools

10 Ways Predictive Analytics Improves Innovation

Real Stories Of Challenges That Slow Digital Transformation

The Future Of Big Data: Smart Data Innovations & Challenges

 

Machine Learning

IBM Machine Learning

Anaconda – Predictive Analytics

Google Cloud Platform

A Tour Of Machine Learning Algorithms

Anaconda Data Science Platform For R, Python Or Both

Should Amazon Be Your AI And Machine Learning Platform?

Amazon Machine Learning – Predictive Analytics With AWS

Use Data To Tell The Future: Understanding Machine Learning

Getting Started With GraphLab For Machine Learning In Python

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

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

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

#1 – TARGETING THE OMNI-CHANNEL CUSTOMER

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

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

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

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

 

#2 – WHAT GETS MEASURED, GETS MANAGED

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

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

 

Mixpanel

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

 

Kissmetrics

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

 

Google Analytics

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

 

Kapost

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

 

#3 – IDENTIFYING OPPORTUNITIES

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

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

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

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

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

 

 

 

For more resources, please see below:

 

The Omni-Channel Customer

What Is Omnichannel?

Targeting Omni-Channel Shoppers

The Definition of Omni-Channel Marketing – Plus 7 Tips

Ten Ways Big Data Is Revolutionising Marketing & Sales

 

Marketing Tools

Kapost

Mixpanel

Kissmetrics

8 Big Data Solutions For Small Businesses

Big Data Trends: Top Eight Analytics Lessons For Business

4 Marketing Analytics Tools That Are Shaping The Industry

 

Identifying Opportunities

Lattice Engines

RapidMiner: Data Science Platform

Why Big Data Is A Must In E-Commerce

How Predictive Analytics Is Transforming eCommerce & Conversion Rate Optimisation