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What a Data-Driven Life Should Look Like

self

Right now, sites like Netflix or Amazon make recommendations to you on what you should buy based on the data they have about your activity / history. Yet the real power of big data, machine learning, and predictive analytics will eventually be to empower people to use the data they have on themselves to make better decisions about life. And this frontier is in its earliest stages of exploration.

// Data-Driven life decisions are difficult

Just one early example of data-driven life complications is recommending events you should be going to. This is tricky because deciding to go to an event has a lot of variables

  • Education Data Will I learn something at this event that I don’t already know? A platform would need to compare your skills / knowledge with what is being taught.
  • Network Data Will I be able to reconnect to people I already know via Facebook / Linkedin or will I be able to meet a target market of people I want to get in front of?
  • Ratings Data Have people like me positively reviewed this event (or this type of event) in the past? Scheduling Data Will this event fit in my schedule?

Since our minds are inherently neural networks, we can make weighted decisions like this quickly based on our memories / experiences / available data. Yet allowing a platform to made recommendations about what events I should attend – accounting for all the above variables – is something at the crossroads of the API Economy (getting access to apps that already have this data about me) and machine learning (weighing these values based on experience)

// Data-Driven life decisions could be everywhere

Making data-driven decisions about what events to go to does not necessarily require recommendation engines / machine learning – but it does require pulling in your life data – and then presenting it to you in a way that you can sort / filter / analyze events based on the things that matter to you.

That same logic could be applied to dozens of situations in our lives:

  • Career What skills do I have, what jobs / careers can I get with those current skills, and what new education / skills should I acquire to reach career goals? Sites like Dice, CollegeBoard, and GlassDoor offer partial solutions, yet how much data am I truly using in choosing my education / career path?
  • Health & Fitness Sites like RunCoach customize fitness plans for you based on your run goals, yet there is a great deal of data that could be integrated between MapMyFitness, Strava, races you’ve registered for, and perhaps even the analytics / data in WebMDM. I would like to be able to use data to evaluate realistic fitness / training goals for myself based on my fitness history.
  • Public Knowledge Many of us write blog posts, commit code, or comment on / review articles online. Often there are comment / review aggregators that collect this data – and sites like Quora help create a single ecosystem of public knowledge that’s peer-reviewed with an expert-rank type system. Yet based on my company, title, experience, skills, and interests, I would like to see real data on what content / articles I should be writing (committing to public knowledge) to best help my industry – or humanity. After all, why write / rewrite knowledge that already exists?
  • Travel
  • Social Life
  • Personal Finances
  • Psychology / Wellness
  • Etc…

// Capturing “Experience Data”

Aside from any big data technologies or machine learning systems, one major bottleneck to enabling people to make data-driven life decisions is creating a behavior of capturing experience data. What that means is capturing contextual information about a person (profile, history, etc) and then allowing them to add and review experiences such as “I tried the Atkins diet and didn’t see any results” or “offering yoga classes at our company offices reduced workplace injuries this year”.

Often a large life decision (career change, moving cities, buying a house) can be reduced down to a large array of small decisions and experiences – and currently those human experiences are mostly organized haphazardly around poorly categorized blogs and forums. It should not just be an academic researcher’s job to gather data on the benefits of yoga for the workplace – those conclusions should just emerge from looking at experience data! However this requires a large behavior shift – people will need to submit actions and outcomes online as they share videos or notes on Facebook.

A data-driven life requires a foundation of gathering, organizing, and perhaps even predictive analytics / recommendation engines built on experience data. As sensors become universal and metatags for web information more standard, a data-driven life will become much more feasible.


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The Dev Technology Landscape

DevTechLandscape6

As developers are empowered to make more influential decisions about the strategy of their company, the tools available to them are becoming equally as powerful and diverse. We at DevNetwork wanted to help explain the dev tech ecosystem with the below list (and infographic!) See the bottom of the article for our methodology – and contact us if you believe we missed an important technology! (Note – we’re limiting Version 2 to 200 total technologies so the infographic does not get overly complicated and therefore cannot include everything!)

Data Dev Technologies

are any technologies, platforms, or libraries helping developers work on or manage the data layer of their applications


Big Data Dev Platforms
Hadoop Dev Technologies
NewSQL Technologies
NoSQL Technologies
Graph Database Technologies
SQL Technologies

API Technologies

are any technologies or solutions that enable companies to deploy, manage, integrate, or market their API’s

API Infrastructure
API Services
API Middleware
API Markets / Directories

DevOps Technologies

are any solutions that help developers manage the “DevOps” layer of their technology stack including server configuration and scalability, security, and provisioning / management of libraries, frameworks, or services

Server Management
Content Delivery
Ops Management
Log management
Configuration Management

Dev Platforms

are technologies that handle the cloud hosting and DevOpps configuration of your application, letting developers build web and mobile apps without having to think about what’s going on below the application layer

Web App Platforms
Mobile App Platforms
Commercial Language Platform / Libraries

Coding Tools

include coding environments, code collaboration tools, and code version management tools which help developers store, share, and deploy each edit to their code

Version Control
Coding Environment
Code Collaboration
Open Source Code Frameworks
Open Source Code Languages
Continuous Integration

App Analytics

is provide analytics dashboards for developers to test and monitor apps at the programmatic level

Web App Analytics Technologies
App Performance Management
Mobile App Analytics
App Testing / Automation Technologies

Cloud Tools

include any specific cloud-based hosting environment or cloud-based infrastructure service

Cloud Hosting
Processing-as-a-Service
User Cloud Services

Operating System Dev

includes operating systems that are maintained by the developer community

Open Source Desktop OS
Commercial Desktop OS

Mobile Device Dev

includes mobile devices with developer platforms

A Note on Methodology

Neatly organizing the entire developer technology ecosystem is only possible in broad strokes and broad generalizations. Many of the listed technologies / companies belong in multiple categories, and we focused their categorization on the main category / subcategory that is most associated with how they market their main product(s). This landscape is not meant to be comprehensive, since there are hundreds of coding languages and frameworks we could have listed, but instead serves to group types of technologies at a high level. For simplicity’s sake, Version 2.0 will not exceed 200 technologies, which means if you submit a new technology or company, it may not get placed either because it is not itself a standalone company / organization, it is not established enough in the market, or we simply have too many technologies within that category already. We did not add API’s since there thousands of API’s across travel, social networks, news, software platforms, etc. and ProgrammableWeb already does a good job organizing API’s by category.


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