Getting started in Machine Learning: Google vs Databricks vs AzureML vs R

Executive Summary

MLpngMachine learning is high on the agenda of cloud providers. From startups to global companies, technology decision makers are watching the achievements of Google and Amazon Alexa with a view to implementing Machine Learning in their own organizations. In fact, as you read this article, it is highly likely that you have interacted with Machine learning in some way today. Organizations such as Google, Netflix, Amazon, and Microsoft have Machine learning as part of their services.  Machine Learning has become the ‘secret sauce’ in business and consumer facing spheres, such as online retail, recommendation systems, fraud detection and even Digital Personal Assistants such as Cortana, Siri and Amazon’s Echo.

The goal of this paper is to provide the reader with the tools necessary to select wisely between the range of open source, hybrid and proprietary machine learning technologies to meet the technical needs for providing business benefit. The report will offer support for devising a long-term strategy for machine learning for existing and future implementations. Here, we compare the following technologies:

  • Google Tensorflow
  • R
  • Databricks
  • AzureML
  • Google Cloud Machine Learning


Introduction and Methodology

A major challenge facing most organizations today is the decision whether to go open-source, hybrid, or proprietary with their technology vision and selection process.

Machine learning refers to a series of techniques where a machine is trained how to solve a problem. Machine Learning algorithms often do not require to be explicitly programmed, but they respond flexibly to the environment after receiving intensive training. Broader experiences work to improve the efficiency and the capabilities of the machine learning algorithms.  Machine Learning is proving immensely useful to help cope with the sheer speed of results required by the business, along with more advanced techniques.

The decisions on machine learning technology choice goes beyond regular technology choice, since it involves a leap of faith that the technology will offer the promised insights.  Machine Learning requires a process of creating, collating, refining, modelling, training and evaluating models on an ongoing process. It is also determined by how organizations intend to use machine learning technology.

Clearly, organizations see Machine Learning as a growing asset for the future, and they are adding the capability now. Machine Learning will increase in adoption in tandem with other opportunities in related technologies, such as Big Data and cloud technologies, and open source becomes more trusted in organizations. This data takes the form of clickstreams, and logs, sensor data from various machines, images and videos. The business teams will want to know more about deceptively simple business questions, where the answer lies in Big Data sources. However, data sources can be difficult to analyze for in. Using insights from this data, companies across various industries can improve business outcomes.

What opportunities are missed if it is not used? By adopting ML, enterprises are looking to improve their business, or even radically transform it. Organizations are potentially losing ground against competitors, if they are not working towards automation or machine learning in some way. They are also not making use of their existing historical data, and their data going forward.

In terms of recent developments, Machine Learning has changed to adapt to the new types of structured and unstructured data sources in the industry. It is also being utilized in real-time deployments. Machine Learning has become more popular as organizations are now able to collect more data, including big data sources, cheaply through cloud computing environments.

In this Roadmap, we will examine the options and opportunities available to businesses as they move forward into Machine Learning opportunities, with a focus on whether organizations should use open source, proprietary or hybrid solutions. The Roadmap focuses on the important decisions that the organization can make is on the choice of technology, and whether this should be open source, proprietary, or a hybrid architecture. The Roadmap also introduces a maturity map to investigate how the choice of machine learning technology can be influenced by the maturity of the organization in delivering machine learning solutions overall.

Evolution of Machine Learning

Though machine learning existed for a long time, it is the cloud that made the technology more accessible and usable to businesses of every size. The cloud offers a complete data storage solution with everything that machine learning needs to run, such as tools, libraries, code, runtime, models and data.

According to Google Trends, the term Machine Learning has increased in popularity six-fold since July 2012.  In response to this interest, established Machine Learning organizations are leading the way by provisioning their technology through open source. For example, Google has TensorFlow, the open source set of machine learning libraries that Google open sourced in 2015.  Amazon has made its Deep Scalable Sparse Tensor Network Engine (DSSTNE – pronounced ‘Destiny’) library available on GitHub under the Apache 2.0 license. Technology innovator and industry legend Elon Musk has ventured out with OpenAI, which bills itself as a ‘non-profit AI research company, discovering and enacting the path to safe artificial general intelligence.’ The technical community has a great deal of Machine Learning energy, evidenced by the fact that Google recently announced its acquisition of online data scientist community Kaggle, which has an established community of data scientists and potential employee pool, as well as one of the largest repositories of datasets that will help train the next generation of machine-learning algorithms.

Evolution of Open Source

Why has Open Source achieved so much prevalence in Machine Learning in recent years? Open Source has been a part of Machine Learning, right from its inception, but it has gained attention in recent years due to significant successes. For example, AlphaGo, produced using Torch software. AlphaGo’s victory over the human Go champion, Lee Sedol, wasn’t simply an achievement for artificial intelligence; it was also a triumph for Open Source software.

Evolution of Hybrid and Proprietary Systems

What problems are hybrid and proprietary systems trying to solve? The overarching theme is that proprietary organizations are aiming themselves at the democratization of data, or the democratization of artificial intelligence. This is fundamentally changing the Machine Learning industry, as organizations are taking Machine Learning away from academic institutions and into the hands of business users to support business decision making.

Proprietary solutions can be deployed quickly, and they are designed to be scalable and work at global scale. Machine Learning is de-coupled from the on-premises solution to a solution that can be easy to manage, administer and cost. Vendors must respond nimbly to these changes as data centers make the transition towards powerhouses for analytics and machine learning at scale.

In the battle for market share, innovation is expended at the cloud level to ensure that standards in governance and compliance are met, with government bodies in mind. As the threat of cybercrime increases, standards of compliance and governance have become a flashpoint for competition.

How are they distinguished? Open source refers to source code that is freely available on the internet, and is created and maintained by the technical community. A well-known example of open source software is Linux. On the other hand, proprietary software could also be known as closed-source software, which is distributed and sold under strict licensing terms and the associated copyright and intellectual property is owned by a specific organization. Hybrid architectures are based on a mix of open source and proprietary software.


For this analysis, we have identified and assessed the relative importance of some key differentiators. These are the key technologies and market forces that will redefine the sector in which technologies will strive to gain an advantage. Technology decision makers can use the Disruption Vector analysis in choosing the approach that aligns with their business requirements.

Decision Makers can use the Disruption Vector analysis to support them in selecting the right technologies that best suit their own requirements.

Here, I assigned a score from 1 – 5 to each company for each vector. The combination of these scores and the relative weighting and importance of the vectors drives the company index across all differentiators.

Usage Scenarios

Machine learning, regardless of approach, has several common usage scenarios.



One popular use case of machine learning is Finance. As with other areas of Machine Learning, a trend perpetuated by more accessible computing power and more accessible machine learning tools and open source packages dedicated to finance. Machine Learning is pervasive in Finance in terms of business and consumer solutions. Machine Learning is used in activities such as approving loans, risk management, asset management, and currency forecasting.

The term robo-advisor is a new term, which was unheard of, five years ago.

Robo-advisors are used to adjust a financial portfolio to the goals and risk tolerance of the consumer based on factors such as attitude to saving, age, income, and current financial assets. The robo-advisor then adapts these factors to reach user’s financial goals.

Bots are also used in providing customer service in the Finance industry. For example, they can interact with consumers using natural language processing and speech recognition. The bot capability for language, combined with the robo-advisor capability for predicting factors that meet financial goals, mean that the Finance world is fundamentally impacted by machine learning from the customer perspective, and the finance professionals’ perspective as millennials fuel the uptake of automated financial planning services.


Why should enterprises care about healthcare? Enterprises have an interest in keeping healthcare costs low as the hard cost of healthcare premiums increase, and they can even impact the enterprises’ ability to invest in itself. Employee sickness costs affect productivity, and it is in the interests of the enterprise to invest in employee health. As an industry, U.S. health care costs were $3.2 trillion in 2015, making healthcare one of the country’s largest industries, equaling to 17.8 percent of US gross domestic product. Rising healthcare issues, such as diabetes and heart disease, are caused by lifestyle factors.

Machine learning is one of the tools being deployed to reduce healthcare costs. As the use of health apps and smartphones increases, there is increased data from the Internet of Things technology. This is set to drive health virtual assistants, which can take advantage of IoT data, increased natural language processing and sophisticated healthcare algorithms to provide quick patient advice for current health ailments, as well as monitor for future potential issues.

Machine Learning can assist healthcare in improving patient outcomes, preventative medicine and predicting diagnoses. In the healthcare industry, it is used for the reduction of patient harm events, reduction of hospital acquired infections, right through to more strategic inputs such as revenue cycle management, and patient care management. Machine Learning in healthcare specifically focuses on long-term and longitudinal questions, and helps to evaluate patient care through risk-adjusted comparisons. For the healthcare professional, it can help to have simple data visualizations which display the message of the data, and deploy models for repeated use to help patients long-term.

There are open source machine learning offerings which are aimed specifically at healthcare. For example, is accessible to the thousands of healthcare professionals who do not have data science skills, but would like to use machine learning technology to help patients.


Machine learning has a wide range of applications in marketing. These range from techniques to understand existing and potential customers, such as social media monitoring, search engine optimization and quality evaluation.

There are also opportunities to offer excellent customer service, such as tailoring customers, personalized customer recommendations and improved cross-selling and up-selling opportunities.

There are a few open source marketing tools which use machine learning. These include Datumbox Machine Learning Framework. Most machine learning tools aimed at marketing are proprietary, however, such as IBM Watson Marketing.

Key Differentiators

Machine learning decisions are crucial in developing a forward-thinking machine learning strategy that ensures success throughout the enterprise.

Well-known organizations have effectively used machine learning as a way of demonstrating prominence and technical excellence through flagship achievements. Enterprises will need a cohesive strategy that facilitates adoption of machine learning right across the enterprise, from delivering machine learning results from business users through to the technical foundations.

In this section, we discuss the five most important vectors that contribute to this disruption in the industry, which also correspond to factors that are crucial to consider when adopting machine learning as part of an enterprise strategy. The selected disruption vectors are focused on the transition of an organization towards the generation of a successful enterprise strategy of implementing and deploying machine learning technology.

The differentiators identified are the following:

  • Ease of Use
  • Core Architecture
  • Data Integration
  • Monitoring and Management
  • Sophisticated Modelling

Ease of Use

Technology name Approach Commentary Score
R Open Source Data Scientists 1
Databricks Hybrid Data Scientists but it has interfaces for the business user 4
Microsoft AzureML Hybrid Business Analysts through to Data Scientists 5
Google Cloud Machine Learning Proprietary Data Scientists 3
Google Tensorflow Proprietary Data Scientists 3

Generally, Machine Learning technology is still primarily aimed at data scientists to deliver and productize the technology, but there is a recognition that other roles can play a part, such as data engineers, devops engineers and business experts.

In this disruption vector, one clear differentiator between Microsoft and the other technologies is that they locate the non-technical business analyst at the front and center of the AzureML technology. In AzureML, more complex models can be built in R and loaded up to AzureML., with a drag-and-drop interface and embeddable R.  Databricks focus on a more end-to-end solution, which utilizes different roles at different points in the workflow. Databricks have different tools for different parts of the process. The need for a data scientist is balanced out by the provision of tools specifically targeted at the business analyst. Both AzureML and Databricks allow for the consumption of machine learning models by the business user. Google Cloud Machine Learning Engine, Google Tensorflow and the open-source R have firmly placed the development of machine learning models in the data scientist and developer spheres. As Google Tensorflow and R are both open-source, this is to be expected.

Google’s Cloud Machine Learning Engine combines the managed infrastructure of Google Cloud Platform with the power and flexibility of open-source Google TensorFlow. Google Cloud Machine Learning Engine has a clear roadmap in terms of people starting off in R and Google TensorFlow open source, and then porting those models into Google Cloud Machine Learning. RStudio, an R IDE, allows Tensorflow to be used with R, so this enables R models to be imported into Google Tensorflow, and then into Google Cloud Machine Learning Engine.

Business users can access their data through a variety of integrations with Google, including Informatica, Tableau, looker, Qlik, snapLogic and the Google Analytics 360 Suite. This means that Machine Learning is embedded in the user’s choice of interface for the data.

The risk for enterprises is that the multiple Google integration points introduce multiple points of failure and numerous points of complexity in putting the different Google pieces of the jigsaw together, which is further exacerbated with the presence of third-party integrations into tools which are visible from the user perspective. In the future, this scenario may change, however, as Google put Machine Learning at the heart of their data centers and user-oriented applications. The business user is seeing an increase of the presence of machine learning in their everyday activities, even including the creation and development of business documents. Google is aimed firmly at the data scientist and the developer, but it does offer its pre-build machine learning intelligence platform. That said, the competition is heating in this space as Google are now bringing machine learning to improve data visualization for business users so that they can make better use of their business data. Microsoft are also adding some machine learning into Microsoft Word for Windows, so that it now offers intelligent grammar suggestions.

Core Architecture

Machine Learning solutions should be resilience, robust and have a clear separation between storage and compute so that models are portable.

There are variations in the core technology which differentiate open source technologies from the large vendors. Embarrassingly parallel workloads separate a technical problem into many parallel tasks with ease. The tasks are run in parallel, with no interdependencies between the tasks or data. R does not naturally work easily with embarrassingly parallel workloads. Many scientific and data analysis require parallel workloads, and packages such as Snow, Multicore, RHadoop and RHIPE can help R to provision embarrassingly parallel workloads.

As an open-source technology, R is not resilient or robust to failure. R works in-memory, and it is only able to hold the data that resides in memory. Its power comes from open-source packages, but these can over-write one another’s functions. This can be an issue because it can cause problems at the point of execution, which can be difficult to troubleshoot without real support.

On the other hand, proprietary cloud-based machine learning solutions offer the separation between storage and compute with ease. Google Tensorflow can use the Graphical Processing Unit (GPUs) that Google has in place in its data centres, which Google are now rebranding as AI centres. To mitigate against technical debt, both Databricks and Google Cloud Machine Learning Engine both have a clear trajectory from the open source technology of Google Tensorflow towards the full enterprise solution which provides confidence for widespread enterprise adoption. As a further step, Databricks also allow porting models from Apache Spark ML and Keras, as well as other languages such as R, Python or Java.  As a further signal of the importance of the open source path with a view to reducing technical debt, Google have released a neural networking package, Sonnet, as a partner to Tensorflow to reduce friction to model switching during model development.

Technology name Approach Commentary Score
R Open Source No; liable to errors 1
Databricks Hybrid Reduce technical debt by being open to most tech 4
Microsoft AzureML Hybrid R and Python are embedded. Solution is robust. Not open to Tensorflow or other packages 4
Google Tensorflow Open Source APIs are not yet stable. 2
Google Cloud Machine Learning Engine Proprietary Cloud architecture with a clear onboarding process of open source technology 4

Data Integration

Data is more meaningful when it is put with other data. Here is the ways in which the technologies differentiate:

Technology name Approach Commentary  
R Not Open R is one of the spoke programming languages, but it is not a hub in itself. 1
Databricks Highly Open Databricks is highly open, facilitating SQL, R, Python, Scala and

Java. It also facilitates machine learning frameworks/libraries such as Scikit-learn, Apache Spark ML, TensorFlow,


Microsoft AzureML Moderately Open R and Python are embedded. Solution is robust 3
Google Tensorflow Open Source Tensorflow offers a few APIs but these are not yet stable. Python is considered stable, but the others, C++, Java and Go are not considered stable. 3
Google Cloud Machine Learning Engine Proprietary APIs are offered through Tensorflow 3

To leverage Machine Learning on the cloud without significant rework, solutions should support data import to machine learning systems. We need to see improved support for databases, time period definition, referential integrity and other enhancements.

The Machine Learning models run in IaaS and PaaS environments, which consume the APIs and services exposed by the cloud vendors and produce an output of data, which can be interpreted as the results. The cloud environment prevents portability of workloads, and organizations are concerned about vendor lock-in of cloud platforms for machine learning.

The modelling process itself involves taking a substantial amount of inbound data, analyzing it, and determining the best model. The machine learning also needs to be robust and recover gracefully from failures during processing, such as network failures.

In terms of the enterprise transition to the cloud for machine learning, it should not impact the optimization of the machine learning technology, and it should not impact the structures used or created by the machine learning process.

Machine learning solutions should be able to ingest data in different formats, such as JSON, Xml, Avro and Parquet.  The models themselves should be able to be in a portable format, such as PMML.

The range of modelling approaches within data science means that data scientists can approach a modelling problem in many ways. For example, data scientists can use scikit-learn, Apache Spark ML, TensorFlow or Keras. Data Scientists can also choose from a number of different languages: SQL, R, Python, Scala or Java, to name a few.

Of all the packages and frameworks, Databricks scored the best in terms of data integration. Dependent on the skill set, data scientists can use scikit-learn, Apache Spark ML, TensorFlow or Keras. Data Scientists can also choose from several different languages – R, Python, Scala or Java.  AzureML and Google Cloud Machine Learning Engine are more restrictive in terms of their onboarding approach. AzureML will integrate with R and Python languages, and it will ingest data from Azure data sources and Odata. Google Cloud Machine Learning Engine will accept data that it serialized in a format that Tensorflow can accept, normally CSV format. Further, like Azure ML, Google data has to be stored in a location where the data can be accessed, such as Google BigQuery or Cloud Storage.

AzureML’s dependency on R may be the popular choice, but there is a risk of inheriting issues in R code that is not high quality. The sheer volume of R packages is often given as a reason to adopt R. However, these does not mean that the packages are quality; some of these packages are small and not developed often, but they are still maintained on the R repository, CRAN, with no real distinction between these packages and the well-maintained, more robust packages. Google Tensorflow, on the other hand, has a large, well-maintained library which is extending to Sonnet.

Monitoring and Management

What you don’t monitor, you can’t manage.

Technology name Score
R 1
Databricks 5
Microsoft AzureML 5
Google Tensorflow 2
Google Cloud Machine Learning Engine 5

Machine Learning has a rich set of techniques to develop and optimise all aspects of Machine Learning. This ranges from the onerous task of cleaning the inbound data, to deploying the final model to production.

As Machine Learning becomes embedded in the enterprise data estate, it should also be robust. Machine learning models should not be able to run out of space. Instead, machine learning solutions should be able to accommodate elasticity of cloud-based data storage. Since many queries will span clouds and on-premises as the business requirements for expanded data sources will increase, the machine learning solution needs to keep up with ‘data virtualisation’ requirement.

As a result of its natural operation, machine learning modelling and data processing can be time-consuming. If the machine learning algorithm takes extensive delay to conduct functions such as repartitioning data, scaling up or down, or copying large data sources around, then this will add unnecessary delay to the machine learning model processing. From the business perspective, lengthy machine learning process will adversely impact user adoption, subsequently introducing an obstacle to business value. The more intervention required by the machine learning solution needs to support its operation, then the less automated and less elastic the solution it becomes. It will also mean that the business isn’t taking advantage of the key benefits of cloud. Systems that are built for the cloud should dynamically adapt to the business need.

If the machine learning solution is built for the cloud, then there should be efficient separation of compute and storage. This means that the storage and the compute can be managed and costed independently. In many cases, the data will be stored, but the actual machine learning model creation and processing will require bursts of processing and model usage. This requirement is perfect for cloud, allowing customers to take advantage of cloud computing so that they can dynamically adapt the technology to meet the business need.

R has no ability to monitor itself, and issues are resolved by examining logs and restarting the R processes. There is no obvious way to identify where the process started or stopped processing data so it is best to simply start again, which means that time can be lost. Both AzureML and Google Cloud Machine Learning provide user-focused management and monitoring via a browser based option, with Google providing a command line option too.

Sophisticated Modelling

Data modelling is still a fundamental aspect of handling data. The technologies differ in terms of their levels of sophistication in producing models of data.

Technology name Approach Commentary  
R Open Source No; liable to errors 1
Databricks 5
Microsoft AzureML Hybrid R and Python are embedded. Solution is robust 4
Google Tensorflow Proprietary 5
Google Cloud Machine Learning Engine Proprietary

Google Cloud Machine Learning Engine allows users to create models with Tensorflow, which are then onboarded to produce cloud-based models. TensorFlow can be used via Python or C++ APIs, while its core functionality is provided by a C++ backend. The Tensorflow library comes with a range of in-built operations, such as matrix multiplications, convolutions, pooling and activation functions, loss functions and optimizers. Once a graph of computations has been defined, TensorFlow executes it efficiently across different platforms.

The Tensorflow modelling facility is flexible, and it is aimed at the deep learning community. Tensorflow is created in well-known languages of Python and C++ so there is a fast ramp-up of skill sets to reach a high level of Tensorflow model sophistication. Tensorflow allows data scientists to roll their own models but they will need a deep understanding of machine learning and optimization in order to be effective in generating models.


Azure ML Studio is a framework to develop machine learning and Data Science applications. It has an easy to use graphical interface that allows you to quickly develop machine learning apps. It saves you a lot of time by making easier to do tasks like data cleaning, feature engineering and test different ML algorithms. It allows to add scripts in python and R and also includes deep learning.

Further, AzureML comes ready-prepared with some pre-baked machine learning models to help the business analyst on their way. AzureML offers in-built models, but it is not always very clear how the models got to their results. As the data visualization aspect grows in Power BI and in the AzureML Studio, this is a challenge which will be handled in the future.

Company Analysis

In the recent history of the industry, Sophos acquired Invincia, Radware bought Seculert and Hewlett Packard bought Niara.


R is possibly the most well-known data science open source tool. It was developed in academia, and has had widespread adoption throughout the academic and business community.  R has a range of machine learning packages, which are downloadable from the CRAN repository. These include MICE for imputing values, rpart and caret for classification and regression models, and PARTY for partitioning models. It is completely open source, and it forms part of the offerings discussed in this company analysis.

When did R start? Who are the R customers? Are they a leader, in terms of installations? Average size of customers? Enterprise scale customers? What are customers’ concerns and benefits? Are companies just flirting with R because it’s the buzzword at the moment?


Google is appealing to enterprises as it solves many different enterprise technology solutions from infrastructure consolidation and data storage right through to business user-focused solutions in Google office technologies. As an added bonus, enterprise customers can leverage Google’s own machine learning technology which underpins functionality such Google Photos.

Tensorflow is open source, and it can be used on its own. Tensorflow appears in Google Cloud Machine Learning capabilities, which is a paid solution. Google also has a paid offering, which clearly chains together cloud APIs with machine learning, and unstructured data sources such as video, images and speech.

Google’s Tensorflow has packages aimed at verticals, too, such as Finance and cybercrime. It is also used for social good projects. For example, Google made the headlines recently when a self-taught teenager used Google Tensorflow to diagnose breast cancer.


Now that Databricks is now in Azure, it is well worth a look for streamlined data science and machine learning at scale. This will appeal more to the coders, but this brings the benefit of customization. AzureML is a great place to get started, and many Business Intelligence professionals are moving into Data Science via this route.

Open Source tools such as R and Google’s Tensorflow are not enterprise tools. They are missing key features, such as the ability to connect with a wide range of APIs. Further, it does not have key enterprise features such as security, management, monitoring and optimization. It is projected that Tensorflow will start to have more of these enterprise features in the future. Also, organisations do not always want open source used in their production systems.

Despite an emphasis on being proprietary, both IBM Watson and Microsoft offer free tiers of their solutions, which are limited by size. In this way, both organizations compete with the open source, free offerings with the bonus of robust cloud infrastructure to support the efforts of the data scientists. Databricks offer a free trial, which converts into a paid offering. The Databricks technology is based on Amazon, and they distinguish between data engineering and data analytics workloads. The distinction may not always be clear to business people, however, as it is couched in terms of UIs, APIs and notebooks and these terms will be more meaningful to technical people.

Future Considerations

In the future, there will need to be more reference architecture for common scenarios. In addition, best practice design patterns will become more commonplace as the industry grows in terms of experience.

How do containers impact machine learning? Kubernetes is an open source container cluster orchestration system. Containers allow for the automation and deployment, scaling, of operations, and it’s possible to envisage machine learning manifested in container clusters. Containers are built for a multi-cloud world: public, private, hybrid. Machine Learning is going through a data renaissance, and there may be more to come. From this perspective, the winners will be the organizations who are most open to changes in data, platform and other systems, but this does not necessarily mean open source. Enterprises will be concerned about issues which are core to other software operations, such as robustness, mitigation of risk and other enterprise dependencies.


Machine learning is high on the agenda of cloud providers. From startups to global companies, technology decision makers are watching the achievements of Google and Amazon Alexa with a view to implementing Machine Learning in their own organizations. In fact, as you read this article, it is highly likely that you have interacted with Machine learning in some way today. Organizations such as Google, Netflix, Amazon, and Microsoft have Machine learning as part of their services.  Machine Learning has become the ‘secret sauce’ in business and consumer facing spheres, such as online retail, recommendation systems, fraud detection and even Digital Personal Assistants such as Cortana, Siri and Amazon’s Echo.

From the start of this century, machine learning has grown from belonging to academia and large organizations who could afford it, to a wide range of options from well-known and trusted vendors who propose a range of solutions aimed at small and large organizations alike. The vendors are responding to the driving forces behind the increasing demand for machine learning solutions as organizations are inspired by the promise that Machine Learning offers, the accessibility offered by cloud computing, open source machine learning and big data technologies as well as perceived low cost and easily-accessible skills.

In this paper, we with the tools necessary to select wisely between the range of open source, hybrid and proprietary machine learning technologies to meet the technical needs for providing business benefit. The report has offered some insights into how the software vendors stack up against one another.

Why UK Power BI Summit? Derive business value from your data

I’ve created UK Power BI Summit in response to an industry need for Power BI to have its own event, and I hope to produce a repeatable model for other Power BI groups globally. I am working with Microsoft in Redmond at the moment, in the hope that I can spread the world globally about the power of enabling businesses through data, via easily-accessible tools.

What’s the rationale? Personally, the next step in my career is to continue my trajectory from the data center towards boardroom level leadership and consultancy, in order to help organisations become 21st century, data-driven organisations. Data is at the foundation of businesses. Data, in turn, leads to insights and better decisions that improve the business. Ideally, businesses should have data as part of their DNA. This does not mean that there is not a place for context or for ‘gut instinct’. Data gives businesses new insights, and, in turn, it gives them new options.


My favourite bookshop in the world: The Strand Bookstore, New York, on the corner of Broadway and E 12th Street.

With my business and technical skills in mind, I am doing my MBA at this stage in my career to focus on building businesses as data-driven organisations. The MBA will help me to combine my technical and business expertise within an established framework that will help me to be more effective in a leadership role. I believe that the MBA will help me to articulate and achieve a strategic viewpoint, which, in turn, will help businesses to use their data more effectively.

I am not alone in this data-driven journey. My industry experience tells me that many organisations suffer from one thing: hype about the possibilities and opportunities in data, and, particularly Big Data, but they don’t know how to get started in terms of technology, people, and enabling business processes that would consume these services.

Organisations can find it difficult to know where to start, or even how to start. Very often, businesses simply store all of their data, rather than think proactively about the data that they have, and how they could use it. As businesses continue to get excited about the opportunities of Big Data, they will also need Data Thought Leadership in order to guide them effectively towards success.

Digital Transformation is a much bandied about term. It isn’t simply whacking a few Virtual Machines in Azure, moving data to the cloud and – yay – digital transformation. It’s about transforming the business through the use of technology, and it has the business at the front-and-center of the activity.

Now is the time for businesses to bring their data and their strategy together, using the latest technologies – but they can’t do that, until they see their data. This is where Power BI come in.

Processed with VSCOcam with hb2 preset

The Power BI event is aimed at those people in the organisation who are aware of business needs, user needs, and have winning ideas and who are willing to learn about user-oriented technology to make that happen. The event is aimed at helping these people to learn about the technology from beginner to advanced, according to their needs.

Although the event is about technology, it’s also about the business, and deriving business value from your data. It’s not a straightforward technology event. It’s about the business as well as the technology, and how it’s used. It’s about bringing you along the journey, further.

I thought that the difference between UK Power BI Summit and other events such as PASS SQLSaturday events, SQLBits were fairly clear, but it would seem from my email traffic that my assumption wasn’t correct.

Just to be clear:

  • I am not part of the SQLBits committee and I have nothing to do with their leadership. I don’t represent them and I’m not featured on their promotional video. I’ve been speaking there since SQLBits 7 through to SQLBits XV. You can look for my SQLBits 7 – 15 sessions here.
  • I am part of PASS and a non executive Director, and I sit on the PASS Board as an elected Director. I don’t represent PASS here. If you want a PASS-validated blog, then please head over to their site. This isn’t a PASS event.

Let’s look at the SQLBits mission statement, taken from their site:

SQL Bits was started by a group of individuals that are passionate about the SQL Server product suite. There is a breadth of knowledge in the SQL Community that will benefit everyone in the community. We want to spread that knowledge. We all work with the SQL community, some of us for many years and have all been given the MVP award by Microsoft.

Let’s look at the PASS Mission Statement, taken from their site:

PASS is an independent, not-for-profit organization run by and for the community. With a growing membership of more than 100K, PASS supports data professionals throughout the world who use the Microsoft data platform.

PASS strives to fulfill its mission by:

  • Facilitating member networking and the exchange of information through our local and virtual chapters, online events, local and regional events, and international conferences
  • Delivering high-quality, timely, technical content for in-depth learning and professional development

PASS was co-founded by CA Technologies and Microsoft Corporation in 1999 to promote and educate SQL Server users around the world. Since its founding, PASS has expanded globally and diversified its membership to embrace professionals using any Microsoft data technology.

So, the UK Power BI Summit is ultimately looking at using Power BI to transform businesses, through expertise in the technology, embedded in business-oriented discussions. The technology should support the business in its mission to adapt to the new world of data.

If you’d like to register, click below:

Eventbrite - UK Power BI Summit

Guess who is appearing in Joseph Sirosh’s PASS Keynote?

This girl! I am super excited and please allow me to have one little SQUUEEEEEEE! before I tell you what’s happening. Now, this is a lifetime achievement for me, and I cannot begin to tell you how absolutely and deeply honoured I am. I am still in shock!

I am working really hard on my demo and….. I am not going to tell you what it is. You’ll have to watch it. Ok, enough about me and all I’ll say is two things: it’s something that’s never been done at PASS Summit before and secondly, watch the keynote because there may be some discussion about….. I can’t tell you what… only that, it’s a must-watch, must-see, must do keynote event.

We are in a new world of Data and Joseph Sirosh and the team are leading the way. Watching the keynote will mean that you get the news as it happens, and it will help you to keep up with the changes. I do have some news about Dr David DeWitt’s Day Two keynote… so keep watching this space. Today I’d like to talk about the Day One keynote with the brilliant Joseph Sirosh, CVP of Microsoft’s Data Group.

Now, if you haven’t seen Joseph Sirosh present before, then you should. I’ve put some of his earlier sessions here and I recommend that you watch them.

Ignite Conference Session

MLDS Atlanta 2016 Keynote

I hear you asking… what am I doing in it? I’m keeping it a surprise! Well, if you read my earlier blog, you’ll know I transitioned from Artificial Intelligence into Business Intelligence and now I do a hybrid of AI and BI. As a Business Intelligence professional, my customers will ask me for advice when they can’t get the data that they want. Over the past few years, the ‘answer’ to their question has gone far, far beyond the usual on-premise SQL Server, Analysis Services, SSRS combo.

We are now in a new world of data. Join in the fun!

Customers sense that there is a new world of data. The ‘answer’ to the question Can you please help me with my data?‘ is complex, varied and it’s very much aimed at cost sensitivities, too. Often, customers struggle with data because they now have a Big Data problem, or a storage problem, or a data visualisation access problem. Azure is very neat because it can cope with all of these issues. Now, my projects are Business Intelligence and Business Analytics projects… but they are also ‘move data to the cloud’ projects in disguise, and that’s in response to the customer need. So if you are Business Intelligence professional, get enthusiastic about the cloud because it really empowers you with a new generation of exciting things you can do to please your users and data consumers.

As a BI or an analytics professional, cloud makes data more interesting and exciting. It means you can have a lot more data, in more shapes and sizes and access it in different ways. It also means that you can focus on what you are good at, and make your data estate even more interesting by augmenting it with cool features in Azure. For example, you could add in more exciting things such as Apache Tika library as a worker role in Azure to crack through PDFs and do interesting things with the data in there. If you bring it into SSIS, then you can tear it up and down again when you don’t need it.

I’d go as far as to say that, if you are in Business Intelligence at the moment, you will need to learn about cloud sooner or later. Eventually, you’re going to run into Big Data issues. Alternatively, your end consumers are going to want their data on a mobile device, and you will want easy solutions to deliver it to them. Customers are interested in analytics and the new world of data and you will need to hop on the Azure bus to be a part of it.

The truth is; Joseph Sirosh’s keynotes always contain amazing demos. (No pressure, Jen, no pressure….. ) Now, it’s important to note that these demos are not ‘smoke and mirrors’….

The future is here, now. You can have this technology too.

It doesn’t take much to get started, and it’s not too far removed from what you have in your organisation. AzureML and Power BI have literally hundreds of examples. I learned AzureML looking at the following book by Wee-Hyong Tok and others, so why not download a free book sample?

How do you proceed? Well, why not try a little homespun POC with some of your own data to learn about it, and then show your boss. I don’t know about you but I learn by breaking things, and I break things all the time when I’m  learning. You could download some Power BI workbooks, use the sample data and then try to recreate them, for example. Or, why not look at the community R Gallery and try to play with the scripts. you broke something? no problem! Just download a fresh copy and try again. You’ll get further next time.

I hope to see you at the PASS keynote! To register, click here: 

Upcoming Microsoft Azure webinars on Azure Machine Learning and Cortana Analytics

I found these webinars over at the Microsoft site, and I’m reposting them here for you:

Introduction to Azure Data Factory with Wee Hyong Tok, Senior Program Manager at Microsoft
August 4, 2015 at 10am PDT 

This webinar is held by Microsoft, and I recommend you tune in if you want to learn more about Azure Data Factory. It enables you to process on-premises data like SQL Server, together with cloud data like Azure SQL Database, Blobs, and Tables. Wee Hyong Tok will help you to understand Data Factory capabilities, and the scenarios where Data Factory can be applied. Click here to register.

If you want to translate the time for this webinar into your own timezone, please click here.

Harness Predictive Customer Churn Models with Cortana Analytics Suite with Wee Hyong Tok, Senior Program Manager at Microsoft
August 18, 2015 at 10am PDT

This webinar is held by Microsoft, and I will be tuning in so I can drink all the good Cortana Analytics goodness!

In this session, Wee Hyong Tok will show you how to build a real-life churn model with Azure Machine Learning, make it enterprise-ready with Azure Data Factory, and deliver data insights with Power BI. Click here to register.

If you want to translate the time for this webinar into your own timezone, please click here.

Click on the image for the original Cortana announcement at WPC15.