Mobile Game Discoverability

 

The mobile gaming market in North America continues to grow. As of Q3 2014there are 141.9MM NA mobile gamers, up from 111.3MM NA mobile gamersin 2013. These mobile gamers spent an average of $32.65 in the last year,generating $4.63B in NA mobile gaming revenue.
The graph below provides a view of how gamers discover games (click on it to get a larger view)

sources_of_mobile_game_discoverability

 

 

For a great analysis please see this  free report.

Asana on How to Start Small and Scale Over Time

A nice blog post from Asana on How to Start Small and Scale Over Time:

Recently, we’ve made a series of changes to our data infrastructure that have all proven extremely valuable:

  • Investing in monitoring, testing, and automation to reduce fire-fighting
  • Moving from MySQL to Redshift for a scalable data warehouse
  • Moving from local log processing to Hadoop for scalable log processing
  • Introducing Business Intelligence tools to allow non-experts to answer their own data questions

Got this from Hadoop Weekly, Issue #95, 9 November 2014. It has many other valuable articles on scaling.

Dart, Swift and Popularity of Big Data and Computational Statistics

Watching programming language popularity is one of my hobbies. The TIOBE index Nov 2014, shows some interesting trends. Let us take a look.

 

Click on these images to see a full page view.

TIOBE_2014

 

TIOBE2014-8-20

 

 

This para from the TIOBE is worth noting.

Thanks to the big data hype, computational statistics is gaining attention nowadays. The TIOBE index lists various of these statistical programming languages available, e.g. Julia (position #126), LabView (#63), Mathematica (#80), MATLAB (#24), S (#84), SAS (#21), SPSS (#104) and Stata (#110). Most of these languages are getting more popular every month. The clear winner of the pack is the open source programming language R. This month it jumped to position 12, while being at position 15 last month.

Other trends:

  1. The top 7 languages (from a year ago) retain their spots, but all of them drop a bit in popularity.
  2. Dart, a programming language from Google,  jumps into Top 20 from a previous rank of #81. Dart is language for  building web and cloud apps.
  3. Swift comes from nowhere and enters #18 spot. Swift is a new programming language from Apple for iOS and OS X.
  4. Perl and Visual Basic.NET stay in Top 10. It will be interesting to watch their moves.
  5. F# keeps moving up (from #23 to #16)
  6. Watch the Top 50 languages (#21-#50). Some of them are leading indicators to future of computing.
  7. To see potential new entrants into Top 20, you may want to watch the other languages in Top 50 in the  TIOBE site.
  8. I expected Scala to be in this list but for some reason, I don’t see it. I think it will soon move up into the Top 20 list.
  9. Three SQL dialects are still in Top 20. I am not surprised by that since SQL is still one of the most popular languages for database programming.
  10. I keep hearing a lot about Julia. I will be watching it with interest.

The images in this page are from InfoMinder. InfoMinder is a tool for tracking web pages. I use it to track a few interesting pages on the web. When InfoMinder detects change in a page, it highlights it  and creates a new changed page. It is one of the tools we built over a decade ago and is still chugging along, helping me and others watch the web.

Startups in the Early Stages are Super Unglamorous

It is always nice to see when some one leaves a big pay check, a prestigious job with one of world’s biggest companies and ventures out into the unknown. It takes a lot of conviction, grit and perseverance to make it. Nest Co-Founder Matt Rogers explains why careers can be made on taking on the challenges and projects unloved by others.

A few notes from one of the most inspiring startup stories.

  • Growing up with technology at a young age changes your mindset and how you interact with the world.
  • Doing end to end in technology is really complicated.
  • The best teams are the ones with a culture that gets going when things go wrong.
  • For startups, PR is the best means of early marketing.
  • Growth metrics are very different for physical items. For  apps it may be  downloads in millions. For physical items, a few sales from each store is good.
  • The early stages of a startup are fun but may be very glamorous – lots of market research, cold calling, talking to people to hire, white boarding, sketching, prototyping, brainstorming, trying out different things and building.
  • To be a good startup, you need to have an intentional processes, deep technological integration and great design.
  • You need to build things that are really easy to use.

I enjoy listening to ETL (Entrepreneur Thought Leaders) podcasts. They normally get good speakers and the audience ask great questions. I hope you enjoy this podcast as much as I did.

 

How Much should You Know About Computing?

How much should you know about computing, if you are not a software developer? In his podcast titled To Code or Not to Code, Grady discusses how much a functioning member of society today should know about computing.

A couple of my favorite snippets from the podcast:

Knowledge and understanding have a funny way of expanding.

Creating to code is a gateway to thinking computationally

Thinking computationally? What does that mean? Why is that important?

Computational Thinking (CT) is a problem solving method that uses computer science techniques. The term computational thinking was first used by Seymour Papert in 1996.

Jeannette Wing, Head of the Department of Computer Science at Carnegie Mellon University (CMU) has been one of the most eloquent Computer Scientists to argue the case. Computational Thinking isthe skill of the 21st century

So what is Computational Thinking? Well it is a collection of diverse skills to do with problem solving that result from studying the nature of computation. It includes some obviously important skills that most subjects help develop, like creativity, ability to explain and team work. It also consists of some very specific problem solving skills such as the ability to think logically, algorithmically and recursively. It is also about understanding people. Computer Science is unique in the way it brings all these diverse skills together.

So back to the original question – how much should you know about computing? It depends.

  • Everyone should have a  high level understanding of what computers are capable of, how they work and where they exist (in desktops, laptops, mobile devices, tablets, cloud and even in cars, smart devices)
  • Engineers/Scientists should know how to use them as tools to improve their work. They may need to learn simple scripting language like Python
  • Students should know “computational thinking” since it will help them build problem solving skills.
  • Not every one needs to be a programmer but learning a simple language will give them a chance to appreciate thinking like a programmer. The essential skill one need to acquire is to take a complex problem, break it into simpler/manageable problems and apply existing knowledge to solve the simple problems.
  • To find patterns (some level of abstract thinking) to apply solutions from one domain to an entirely different domain.

What do you think? Do you think Computational Thinking is an essential basic skill?

 

Focusing Illusion

“Nothing In Life Is As Important As You Think It Is, While You Are Thinking About It”

 

Education is an important determinant of income — one of the most important — but it is less important than most people think. If everyone had the same education, the inequality of income would be reduced by less than 10%. When you focus on education you neglect the myriad other factors that determine income. The differences of income among people who have the same education are huge.

 

Recipient, Nobel Prize in Economics, 2002; Eugene Higgins Professor of Psychology; Author, Thinking Fast and Slow

Are You a Multiplier or a Diminisher?

Liz Wiseman talks about The Power of Not Knowing . She talks about multipliers and how they work.

Multipliers are different. Some of the common attributes you can find in multipliers are that:

  • They are supportive
  • They trust people
  • They listen a lot
  • They make you feel important
  • They seek help
  • They give appreciation
  • They get out of the way
  • They are demanding and have high expectations of you
  • They challenge you and let you suffer a bit
  • They operate from a place of inquiry
  • They push you out of your comfort zone into  spaces unknown
  • They ask questions that focus the energy and intelligence of the group

Diminishers hire great talent, but do not allow them to operate at their full potential. Here is how to identify them.

  • They micro manage people
  • They adopt a subtle attitude of  “my way or high way”
  • They typically operate in “tell” mode
  • They constantly emphasize their superiority
  • They remind you that you don’t know enough
  • They constantly interrupt
  • They operate from a place of knowledge (their knowledge)
  • They create stress in the teams that work for them

Listening to this talk, made me think, how we have a mixture of the qualities of both multipliers and diminishers. I have noticed that good multipliers are comfortable in their own skin and make great mentors and coaches.

After listening to Liz, I am putting her books on my reading list. I highly recommend listening to this podcast.