Atlantic Canada Association of IT Architects (ACAITA)

One of the things I wanted to get involved with once I settled in Atlantic Canada was to engage with the IT Architecture community. I wanted this engagement to be at the grass-roots level where the focus is with deepening the skills and knowledge of existing architects and encouraging senior developers to broaden there skills and knowledge into architecture. I see the group starting in St. John's and quickly reaching-out to other areas of Atlantic Canada. To facilitate the geographic distribution I believe we could use many of the on-line tools available to facilitate community engagement. My time with Mozilla, WikiEducator and CLE taught me much about facilitating online community and distributed work teams. I really don't want to build this on my own, so if others know of similar activities I would really appreciate being pointed in there direction.

I was thinking we could name the professional group the Atlantic Canada Association of  IT Architects (ACAITA). It would be very agnostic toward which of the existing professional IT groups it would align itself. The ACAITA would aspire to have associations with all related professional associations.


I also don't believe it should focus on any particular architectural framework or process. The group should work diligently toward building skills and knowledge of all frameworks and processes.

zachman framework	togaf process

I believe the group should meet the 1st and 3rd Thursday evening of every month. Keep in mind that this is a Community of Practice with a focus on creating an association of architects, peer learning, mentorship and improving the quality of IT architecture within Atlantic Canada. I see there will be two different kinds of meetings;

• 1st Thursday is a face-to-face meeting at a predetermined location
• 3rd Thursday is using Google Hangouts and online social media

I am open to changing this schedule, maybe even having meetings during the day rather than in the evening. Or providing a mix of both. This is a collaborative effort and what the membership agrees to is what we could move forward with. All good.

Thursday December 5th - Kickoff and Introductions
Location: Georgetown Pub, Hayward Avenue, St. John's, Newfoundland
Agenda: Introductions, Frameworks and Groups, What are the different roles of the architect?

December 19th - Whats available for the Architect?
Location: Google Hangout
Agenda: Introductions, Certification Options, Online Architecture Resources

2014 and beyond - TBD

If you are interested in joining, participating, providing insight into what has gone on before, hanging with a bunch of other architectural geeks, learning more about IT architecture, etc... please get in contact with me. Comment here on this blog, message me, reach-out... I look forward to connecting...

What is Big Data?

I started my first winter book review with the book titled "Doing Data Science". I found the content very rich and to review the whole book in one post would have been too much to fit into my rule-of-thumb that a book review post should never be over 1000 words. All this goes back to how I review books and how each of my writing and reflection needs to stay to a reasonable amount of content.

I like how this book is written from the implementation of big data perspective, I like how this book is written from the teaching people about data science perspective, I like how this book is written from the hands-on getting it done perspective. I really like this book from the outset of its reading.

The preface and introductory chapter contained a valuable amount of information that helped put the whole data science subject into context. These two chapters each helped in the following ways;

• Preface
  1. Origins - this fell into two main themes, the origins of the course the book was based upon and the origins of the book itself. What became clear is the book was written to bring clarity on the subject of data science and big data. And what is hype and what is concrete and historical. The book sees much of what is currently occurring within big data as hype; data science has existed for a while ( > 10 years ) and the current practices within big data have origins with traditional statistics against big data sets...
  2. Supplemental Reading - this is an amazing reading list and provides valuable insight into the breadth of the data science subject domain. The supplemental reading fell into the following six categories, these speak volumes about the domain of data science; 1. Math, 2. Coding, 3. Data Analysis and Statistical Inference, 4. Artificial Intelligence and Machine Learning, 5. Experimental Design, and 6. Visualization. What surprised me was how a number of the books listed were a part of my statistics, visualization and collective intelligence readings from a few years back. 
• Introduction
  1. The hype - the book acknowledges that the hype around big data and data science is extensive and there are a few drawbacks to all the hype;
     • Currently there is no common terminology around big data or data science.
     • It shows a disrespect to those working working in this field for many years.
     • Creates a noise-to-signal ratio that could turn people away the longer it continues
     • It simplifies the broadness of what is required to be successful in data science
     • Working with large volumes of data is as much a craft as a science
     There is also an amount of truth, and lessons to be learned, within all the hype. The important highlights are;
     • smart people with some of the required skills, should be able to develop the other skills they need to be data scientists
     • its the integration of both on-line and off-line real-time data that is different, we now have a culturally saturated feedback-loop.
     • there are ethical and technical responsibilities to be considered
  2. The role - of the data scientist or team doing the work requires the following skills. Though having these qualities in a team is better than an individual. (and it is difficult to find all these skills in one person).
     
     • Computer Science
     • Math
     • Statistics
     • Machine Learning
     • Domain Expertise
     • Communication and Presentation skills
     • Data Visualization
  3. Team structure - is well described in both the skills listed above and in figure 1-4 from the book. For me its a well balanced team with a variety of people from different fields working together in solving big data problems.
  4. Thought experiments - the introduction (and the whole book) uses well articulated thought experiments. These get the reader thinking about the current subject through questions and problems to solve.

Reflections:
My initial thoughts after skimming the whole book and considering the details of the first few chapters took me back to all my readings with very large databases (VLDB) a number of years back. The VLDB SIG has been around for close to 40 years and if you view some of the early conference agenda... big data hasn't changed that much in 40 years. Extracting, Cleansing, Transforming and Loading the data is as important as it ever was, and there are many well known practices in this domain. The heavy lifting isn't with the implementation of the technology; but with the nature of the content, the goals of the analysis effort, and with well designed reporting and visualizations. The statistics, and related approaches, are as important as ever.

So... what is big data?
In my opinion, it is the coalescing of many large (even humongous) data sources. These sources can be real-time, on-line, off-line and otherwise and the reporting and visualization should represent this dynamic nature of the data. Really smart analysis (statistical and other) should be available ASAP so intelligent and automated decisions can be made. Big data projects should have a balanced team where team members each possess a number of the skills required to make the team complete. The team should be given free reign to experiment and explore while staying rigorous to project management practices (Agile, Lean or otherwise).

A deliberate book review

I'm now an autodidact, and one of the ways I learn is to review books with a deliberate practice. The steps of my approach are as follows;

1. kick-start the brain - this means I read the table of contents, skim each chapter (very quickly), and examine each image in detail. I believe constructivism is how many of us learn, I am no exception. So when I undertake a learning task (like reviewing a book) I need to allow my brain to retrieve as much related information it can conjure to assist in my learning and relate it back to my previous learnings and knowledge.
2. read to seek understanding - every passage of text I read I develop an understanding of it. If this requires me to research the themes and topics in the passage of text, I do. I make notes, draw pictures, create concept maps to deepen my understanding...
3. write and reflect - when I have covered a reasonable amount of content I will write about my understanding with the intention of publishing to my blog. I will reflect upon my learning and my writing, and then review it for completeness and understandability.
4. edit then publish - I will usually leave the related blog post(s) unedited for a few days as I continue to reflect upon its content. I will the return to the post and make edits in preparation for its publishing. 
5. review - I will read most new blog posts a few times after their publishing. I will often make further edits where appropriate. During this review I will think about how its content relates to other reading I have since completed.
6. iterate - I will continue to read with purpose and continue with similar practices until the book is finished. Often I will have multiple blog posts underway as I read a book for the purpose of review.

For me this approach allows me to develop a deep understanding of a books content while contributing to the collective publishing platform known as the internet.

2. Begin a creative project or concept map about the subject domain

This where the work of learning within a new subject domain becomes increasingly fun. Begin a creative project centered around the word or concept that you have chosen as your domain of study. The project should be as fun and creative as you can imagine, or the project could be completely pragmatic and capture details of the learning journey. The main purpose is to fire-up the mind from many different perspectives in relation to the domain of study. The project should be able to grow and change through the full duration of your researching your subject domain. Essentially, the creative project becomes a personal learning portfolio that ties the project together. As you learn more about your chosen subject through your research, reading, creativity, play and discussion it is important that you add to your creative project. A few ideas for creative or pragmatic projects includes (but not limited to);

Build on your idea

• a daily or weekly diary about your subject
• an ever expanding concept map dedicated to your subject
• a learning portfolio with weekly updates
• a wall size collage with images, articles, writings
• the authoring poetry and songs
• a blog reflecting about your learning (include images, photos, references, etc.)
• create your own maker project with an accompanying video blog
• write a play of short story including your subject as a theme or character

The inclusion of the creative project is about play and creating rituals around your study. It really is about the play, about the broadness, and about the engagement. Learning should be about play, with an amount of discipline built in. Why play? Why broadness? Why engagement? Why discipline? Each of these attributes of your self-determined study has a rationale;

The play - is about enjoying what you are doing so you return to do it again and again. It is also about not allowing yourself to be restrained to follow up on related themes or threads within your chosen subject domain. It allows one to learn with reckless abandon, and to be at play.
The broadness - is about exploring the subject in its entirety and allowing yourself to follow-up with related, or seemingly unconnected, ideas and subjects you stumble upon as you deepen your understanding of the subject domain. It's important to remember that learning a subject domain is also about learning how the subject domain relates to others. When constructing knowledge review the learning in different contexts and against other subjects is an important part of learning. It deepens your understanding.
The engagement - is about reaching out to others with similar interests who can provide insight into your subject domain, or you could provide insight into their subject domain. This is about engaging the community or communities in and around your subject domain. It is about adding content, resources, opinions, value, energy, good-faith... its becoming a member of, and engaging, the community of experts who are all attracted to the same subject domain as yourself.

Where to engage? engagement can happen in many places, both online and off. It is strongly encouraged that you find a variety of places to meet with people who are also interested in your chosen domain of study. A list of places includes, and is not limited to;

• everywhere - seek out your peers and mentors, you will be surprised where you find them. Tell people what you are doing ask for assistance.
• online collaborative spaces - different subject communities hang out in different online spaces... find them!
• conferences - attend conferences on your chosen subject.
• the library - go to the library and hang out in the section related to your subject.
• at colleges and universities - yes, you can still learn by taking courses and hanging out with like minded people.
• social media - follow hashtags, join groups, engage in online discussions, find related RSS feeds.

The discipline - is about committing the time to exploring and playing within your chosen subject domain. It is about follow-through and completing tasks related to your understanding. Learning something in-depth takes effort, time and a deliberate practice. And don't think learning is any different because the subjects you have chosen are self-determined, mastery takes time and commitment. Some say mastery of any subject takes over 10000 hours, so be disciplined and enjoy yourself.

Creativity, fun and discipline will get you to finished when choosing to pursue self-determined learning. It will be the amount of personal commitment you make, and how much fun you find the subject and activities. What will it take for you to be attracted, on a regular basis, to study and spend time with your chosen subject domain. A creative project will assist greatly in being attracted to your subject, and it will open your approach to learning to being outside the traditional.

Related searches and references
Learning in depth
Learning portfolio
Maker faire
Learning through play

Big data, winter reading and book reviews

Totally chuffed! I just lined up three books on big data and am looking forward to their being my winter tech reading and related book reviews. The theme for the collection of books is big data. I have been working with data (big and small) for all of my career, and my 1996 undergrad degree is in database management systems (DBMS). Recent projects have exposed me to big data, enterprise environments, global internet standards and open source deployments. A few years back I did a bunch of work with building online learning communities, mobile web and collective intelligence. I even found the time for a bunch of reading in the subject, and wrote reviews for the books I read. So with the acquisition of these three books I look forward to sharing my data experience through three book reviews. Stay tuned!

Data Science for Business - I particularly like how this book focuses on big data in the business environment, and how it relates it back to the traditional terms of data-mining and data-analytics. Its the business focus that really attracts me, event though I find myself with more of a socialist bent, I still see the value of competitive advantage, ROI, analytics feeding strategy, etc. This book looks to focus on all this, with the addition of the technical and algorithmic details of big data in the business environment.

Doing Data Science - I particularly like how this book focuses on the hands-on front line of data science. It has a great amount of focus on implementation and the use of the big data technologies and approaches, all of the keywords floating around in big data and present within this book. If you are thinking about Machine Learning, Naive Bayes, Modeling, MapReduce, Hadoop, etc. The number of case studies that are present in the book look to provide good reflective activities to deepen understanding.

Agile Data Science - I particularly like how this book brings together agile approaches, big data, analytics and hadoop. If you've been following my blog for any length of time you know my experiences and belief with agile approaches, so I am happy to see an agile approach to data emerging. I am also looking forward to how the following technologies are folded into the process of running an agile project for data science; SQL NoSQL, Apache Pig, MongoDB, ElasticSearch, GitHub, AWS, etc.

1. Identify and commit to learning a subject domain

Identifying and committing to learning a subject domain can begin in many ways. One way to start is with a single word or picture drawn in the middle of a page, the back of a napkin or in a blog or social media site. Once the learning has been initiated by the small event of writing a word or drawing a picture, it can grow and deepen by continuing your reading, researching, drawing, painting, playing with, and discussing the word or drawing as a concept. The fun begins as you continue your commitment and move into the next step of learning, which will deepen and provide insight into your approach to learning your chosen subject domain.

Begin it now!

Identifying and committing to your subject domain is not always an easy task. There are so many things to learn and filtering the choices to a focused subject which you are willing to commit a period of time learning can be difficult. This commitment is often influenced by your motivations behind wanting to learn the subject. And why people want to learn a subject often varies as much as there are different people learning subjects. Fortunately, there is no shortage of exceptional resources and people that can assist in focusing your domain of study. How, and why, you focus is a part of your committing to learning the subject. The focus may be due to a financial need, or the desire for change, or wanting to complete a level of study, to become familiar with a genre of music, or to learn a new cooking style, or commit to a graduate level of knowledge is a subject domain. Self- reflection and engaging others can assist greatly in the activities in identifying and committing to your domain of study. As you increase your self-determined learning you will find it is your self-reflection and personal network of people and learning objects than help in deepening your knowledge.

Do not hesitate to jump into any approach or method when developing your commitment to and finding the subject of your study. Do keep in mind, in the end, it is this level of commitment that will get you to completing your learning. And in most situations, this needs to be an internal commitment. It is more difficult for external motivations to help you get to completion of self-directed studies. 

On occasion  I remember a significant conversation when I was developing my ideas around completing an Open and Networked PhD and in being a self-determined learner. In particular, the idea of getting to finished takes a lot of effort and doing it without mentorship is near to impossible. This discussion was mostly about the importance of doing graduate level work at a traditional institution being the only way to complete graduate studies. I disagreed, but what became important is that I needed to be very committed to my chosen subject domain without any external motivation. I need to be committed to completion through mostly internal motivators.

This gets into thinking about intrinsic and extrinsic motivation. Where Intrinsic motivation refers to motivation that is driven by an interest or enjoyment in the task itself. Where Extrinsic motivation refers to the performance of an activity in order to attain an outcome, whether or not that activity is also intrinsically motivated. Focusing on the knowledge domains that you would be intrinsically motivated will go a long way to deepening your learning of most subjects. So it would make the most sense to choose subjects that you are intrinsically motivated.

When choosing a domain of study allow yourself to consider both large and small subject domains. The self-determined learner can pursue learning activities and journey's that are both large and small. The approach may differ due to amount of learning and length of of the commitment, but the amount of learning does not dictate the success a learner can achieve. Throughout this chapter we discuss approaches to self-determined learning and they should be considered a part of your learning toolkit and they can be used for both small and large learning endeavours.

When beginning a self-determined learning journey the concept of schedule changes from the traditional models of learning. Self-determined learning isn't about a schedule, other than a self-imposed schedule. Having to get things done within a semester, term or school year does not apply. It is about life-long learning and if it takes a lifetime to achieve your life learning goals that's the point. It may assist greatly in developing a schedule that would self-motivate, but it only has the follow what works best for the learner themselves. This self-determined schedule only adds to the importance of following your own learning schedule, path and motivation.

Related searches;
Autodidactism
Heutaogogy
Transformative Learning
What colour is your parachute?
Intrinsic and extrinsic motivation