Old Project - Observing Users

One of our classes was titled "Observing Users." This class was centered around many of the tenants of user research. I found Walt Dickie's "Seven Rules for Observational Research" to be a short and particularly helpful resource for getting a better handle on doing great research. Two of the best points were that "ordinary is what you are there to observe" and "whatever you saw could have happened differently."

My team and I focussed on observing oil change shops in Chicago. What interested us initially was that oil change shops are known for being less-than-honest because there is an imbalance of knowledge: the mechanic knows everything about your car and how much things cost and the owner of the vehicle knows almost nothing. We wanted to explore these interactions. The following is our final presentation and summarizing poster:

 

 

UX for the Internet of Things (IoT)

Here are my notes on this talk about the IoT and UX:

The Internet of Things for consumers has a very different business model than regular consumer electronics. In normal consumer electronics, somebody buys an object and the relationship is over until the consumer chooses to buy a new device (I would, however, dispute this as being simply a bad paradigm for consumer goods companies). But certainly for IoT, the sale is just the beginning of the relationship. The device alone has almost no value to the customer or the manufacturer, it is just a way to get people to use a service. Hardware will start to become more specialized.

Connecting stuff to the internet is easy but much of it is pointless. The internet can help you maximize efficiency of a fixed process, but that's not a problem that most people have. What is interesting is to make sense of the world on behalf of people: to reduce cognitive load and allow people to interact with services at a higher level of complexity. People are good at pattern finding but when there's lots of data. This is where predictive analytics, machine learning, pattern matching, etc. comes into play.

There are three issues with the UX of predictive analytics. Expectation, Uncertainty, and Control. 

  1. Expectation - How do devices that adapt communicate that they're adaptable? Does a chair vibrate when it changes posture? Should a coffee machine let you know it's schedule for you. What machines are adaptive and what are simply reactive? Have you ever gotten used to an automatic sink that does not require turning a knob and then stood in front of a non-automatic sink for 30 seconds wondering if the thing is broken?
  2. Uncertainty - Predictive systems are unpredictable at first which is not what we expect from them. We judge systems on their mistakes. So a system that works 95% of the time still fails us quite often. And a device is typically just one in a whole bunch so something may always be failing.
  3. Control - How do you control a device when things are wrong? Is there overtraining?

There are also four patterns for addressing predicting UX issues:

  1. Set behavior expectations so that a person can build a mental model for understanding what the computer will do. 
  2. Explain when a state has changed or is going to change.
  3. Create clear ways for people to adjust the predictive behavior.
  4. Support people, don't work to replace them.

What is blockchain? Does it matter?

Blockchain technology has been in the news a lot recently. Unfortunately, what I get from most articles is that the blockchain is not well understood. So I'm going to try to explain it in a way that makes sense (at least to me). 

How it works
A blockchain is a type of database. The blockchain database is made up of lots of tiny transactions at the most basic level. These transactions are currently mostly monetary (like with the use of Bitcoin). However, the blockchain can be used for much more. The transactions can be voting records, medical procedures, certificates, and more (more on this later).

But what separates the blockchain from any other database is how it is distributed. Most databases (like this blog) are records stored in one central location. Each one of my posts is a record that is stored on a single server. This blog uses an undistributed structure. So if somebody were to work their way into my website and change every other word of this post to "poopface" I would be embarrassed the next day. However, in a blockchain database (where the structure is distributed and every record exists across the world), this would not be possible (I will explain this later as well). So no one person or one government is in charge of handling a blockchain (unless it is a private blockchain). But public blockchain databases are held by millions of computers around the world and so it would be very difficult to hack the system (I may get to this later).

 
 

These individual transactions or records are not stored individually though (and this is where the "block" comes in). Each transaction gets sent to a node/computer but it doesn't get sent individually, it gets sent in a block. Each block works as a timestamped cell for all transactions that happened at that time. The ways these blocks are made are by very difficult mathematical equations called a cryptographic hash. It takes all the computers on earth about 10 minutes to solve this equation and so these blocks are made every 10 minutes. Because the math problem is so difficult, it is highly unlikely that any one person would solve two blocks in a row (and thus get to manipulate the system).

Every computer that is connected to this blockchain has every block and every transaction already in its memory. All of the blocks together are called the ledger. This is a lot of data (and it can take up to 24 hours to download all of the ledger) but after the first time, you won't have to do it again. This means that every node is constantly checking the validity of a transaction against the whole previous backlog of transactions. So if somebody is trying to tamper with what already happened (to show that they have more money than they actually do or maybe to show that they have credentials that they don't have), it would get flagged by every node as being a bad transaction. The individual transaction level security gets a bit more complicated. Also not every transaction needs to be stored as its own transaction. Many transactions can be compiled into one in the form of a Merkle tree.

This could go on for a while and the math is pretty complicated, but I think that's an ok introduction. Obviously, a lot of the value comes from a deep understanding of how the security works, but that would get more involved.


You can read more about it here.
You can also watch a video that explains Bitcoin and blockchain in some detail here.

Why it matters
Blockchain technology enables people to converge on a consensus of data even when people are dishonest and malicious. So the blockchain is basically a global distributed ledger where anything of value can be moved and stored securely. Trust is established by strength in numbers.

More than Bitcoin, there are many ways in which the blockchain can be used. Musicians can use a company like Mycelia to build contracts into their songs so that artists can sell directly to consumers without a label or tech company. This means artists get paid first before the labels (or even without them at all). 

Reputation systems could be created so that instead of using rating agencies and credit rating services, trustless transactions will be easier. 

There is the ability for blockchain to be used for intellectual property so that any shared intellectual property will be able to be found no matter where it spreads.

Companies like StubHub could use blockchain to verify that the tickets that are being sold are legitimate.

The Internet of Things will need blockchain technology to manage all of the daily transactions. 

This is just the beginning and it will be interesting to see what actually comes of the blockchain.