New York Tech Journal
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Structured and Scalable Probabilistic Topic Models

Posted on March 24th, 2017

Data Science Institute Colloquium

03/23/2017 Schapiro Hall (CEPSR), Davis Auditorium @Columbia University

John Paisley, Assistant Professor of Electrical Engineering, spoke about models to extract topics and the their structure from text. He first talked about topic models in which global variables (in this case words) were extracted from documents. In this bag-of-words approach, the topic proportions were the local variables specific to each document, while the words were common across documents.

Latent Dirichlet Analysis captures the frequency of each word. John also noted that #LDA can be use for things other than topic modeling.

  1. Capture assumptions with new distributions – is the new thing different?
  2. Embedded into more complex model structures

Next he talked about moving beyond the “flat” LDA model in which

  1. No structural dependency among the topics – e.g. not a tree model
  2. All combinations of topics are a prior equally probable

To a Hierarchical topics model in which words are placed as nodes in a tree structure with more general topics are in the root and inner branches. He uses #Bayesian inference to start the tree (assume an infinite number of branches coming out of each node) with each document a subtree within the overall tree. This approach can be further extended to a Markov chain which shows the transitions between each pair of words.

He next showed how the linkages can be computed using Bayesian inference to calculate posterior probabilities for both local and global variables: The joint likelihood of the global and local variables can be factors into a product which is conditional on the probabilities of the global variables.

He next compared the speed-accuracy trade off for three methods

  1. Batch inference – ingest all documents at once, so its very slow, but eventually optimal
    1. optimize the probability estimates for the local variables across documents (could be very large)
    2. optimize the probability estimates for the global variables.
    3. Repeat
  2. Stochastic inference – ingest small subsets of the documents
    1. optimize the probability estimates for the local variables across documents (could be very large)
    2. take a step toward to improve the probability estimates for the global variables.
    3. Repeat using the next subset of the documents
  3. MCMC, should be more accurate, but #MCMC is incredibly slow, so it can only be run on a subset

John showed that the stochastic inference method converges quickest to an accurate out-sample model.

posted in:  applications, Big data, data analysis, Data science    / leave comments:   No comments yet

Critical Approaches to #DataScience & #MachineLearning

Posted on March 18th, 2017

#CodesAndModes

3/17/2017 @Hunter College, 68th & Lexington Ave, New York, Lang Theater

Geetu Ambwani @HuffingtonPost  @geetuji spoke about how the Huffington Post is looking at data as a way around the filter bubble in which separates individuals from views that are contrary to their previously help beliefs. Filter bubbles are believed to be a major reason for the current levels of polarization in society.

The talked about ways that the media can respond to this confirmation bias

  1. Show opposing point of view
  2. Show people their bias
  3. Show source crediability

For instance, Chrome and Buzzfeed have tools that will insert opposing points of view in your news feed. Flipfeed enables you to easily load another feed. AlephPost clusters articles and color codes them indicating the source’s vantage view. However, showing people opposing views can backfire.

Second, Readacross the spectrum will show you your biases. Politico will show you how blue or red you by indicating the color of your information sources.

Third, one can show source credibility and where it lies on the political spectrum

However, there is still a large gap between what is produced by the media and what consumers want. Also this does not remove the problem that ad dollars are given for “engagement” which means that portals are incented to continue delivering what the reader wants.

Next, Justin Hendrix @NYC Media Lab (consortium of universities started by the city of NY) talked about  emerging media technologies. Examples were

  1. Vidrovr – teach computers how to watch video – produce searchable tags.
  2. Data selfi project – from the new school. See the data which Facebook has on us. A chrome extension. 100k downloads in the first week.
  3. Braiq – connect the mind with the on-board self-driving software on cars. Build software which is more reactive to the needs and wants of the passenger. Technology in the headrest and other inputs that will talk to the self-driving AI.

The follow up discussion covered a wide range of topics including

  1. The adtech fraud is known, but no one has the incentive to address. Fake audience – bots clicking sites
  2. Data sources are readily available lead by the Twitter or Facebook APIs. Get on github for open source code on downloading data
  3. Was the 20th century an aberration as to how information was disseminated? We might just be going back to a world with pools of information.
  4. What are the limits on what points of view any media company is willing to explore?
  5. What is the future of work and the social contract as jobs disappear?

@GAMtrader

posted in:  applications, data analysis, Media    / leave comments:   No comments yet

#Holograms, #VR, #Technology for Kids, #HomeSecurity

Posted on February 15th, 2017

#HardwiredNYC

02/15/2017 @ Wework Chelsea, 115 West 18th Street, NY, 4th floor

The speakers were

The first speaker, David @ PRSONAS  spoke about their product which is a hologram persona that can serve as a greeter at retail stores: provide product information, financial guidance, intake of medical symptoms, etc. The greeter is a flat holographic image of a person in what David called 2 ½-d display.

The software behind the hologram can provide appropriate hand gestures, show videos, instruct users to input data on a tablet, etc.

David talked about how they have customized the image to avoid falling into the uncanny valley (close to human-looking so feels creepy) by modeling the image as a non-human character.

++

Next, Sophia @ SVFR spoke about how her company is striving to become the common site for distribution of VR, #AR and #MR videos. She likened today for VR as the early 1990’s were for Yahoo, when distribution of web content was still in its infancy.

She talked about the barriers to widespread VR production. These include lack of universally available hardware to record VR, lack of editing tools, but most importantly, we don’t yet know how to tell a story taking advantage of the VR experience.

++

Next, Bethany @TechnologyWillSaveUs spoke about how her company is creating kits for students to experiment in creating their own technology. The kits contain sensors, motors, etc. and are linked to a programming language on their web portal which is an extension of Scratch.

As an example she demonstrated a programmable wrist band that can react to motion, etc.

Bethany then talked about their company strategy which emphasizes a range of products.

  1. Create a range of products: variety of prices, can create bundled products
  2. Product-market-fit: hardware is more difficult, so put development is on a tight production schedules with lots of feedback. Monitor ROI for various products.
  3. By having a range of product, there are activities for all parts of the company at any given time.

She talked about how the company strives to stay ahead of the competitions (Little Bits, Lego Mindstorms) by carefully target price points and creating a wide range of products for different age groups.

Finally, John @Canary talked about their stand-alone, in-home security system which is connected to an app on your phone.

He emphasized the importance of Product design = Relationship design

You need

  1. Quality time – the app needs to interactive. They made it easier to access the time line of videos taken by the system
  2. Crisis management – can contact the police if there is a notification – help the homeowner overcome a crisis: the assist the home-owner filing an insurance claim.
  3. Trust – connected-home customers are concerned about privacy. Use ICASlabs recently released device security protocols
  4. A little magic – surprise and delight. Good example is Netflix onboarding that asks for your movie preferences then starts recommending movies upon the first use.

John also mentioned that they store videos of arrivals and departures, temperature, air quality, how active are occupants. Videos are stored 24 hours or 1 month depending on the contract. They are partnering with insurance companies to get homeowner discounts for using Canary.

 

posted in:  applications, Hardwired NYC, Internet of Things, startup, VR    / leave comments:   No comments yet

#VideoStreaming, #webpack,#diagrams

Posted on January 18th, 2017

#CodeDrivenNYC

01/17/2017 @FirstMarkCapital, 100 Fifth Ave, NY 3rd floor

Tim Whidden, VP Engineering at 1stdibs: Webpack Before It Was Cool – Lessons Learned

Sarah Groff-Palermo, Designer and Developer: Label Goes Here: A Talk About Diagrams

Dave Yeu, VP Engineering at Livestream: A Primer to Video on the Web: Video Delivery & Its Challenges

Dave Yeu @livestream talked about some of the challenges of streaming large amounts of video and livestreaming: petabytes storage, io, cpu, latency (for live video)

Problems

  1. Long-lived connections – there are several solutions
    1. HLS (Http live streaming) which cuts video into small segments and uses http as the delivery vehicle. Originally developed by Apple as a way to deliver video to iPhone as their coverage moves from cell tower to cell tower. It uses the power of http protocol = a play list & small chunks which are separate url’s: m3u8 files that point to the actual files.
      1. But there are challenges – if you need 3 chunks in your buffer, then you have a 15 second delay. As you decrease the size of each chunk, the play list gets longer so you need to do more requests for the m3u8 file.
    2. DASH – segments follow a template which reduces index requests
    3. RTMP – persistent connections, extremely low latency, used by Facebook
  2. Authorization – but don’t want you to rebroadcast. (no key, so not DRM).
    1. Move authentication to cache level – use Varnish.
    2. Add token to the playlist, Varnish vets the token and serves the content. => all things come through their api.
    3. But – you expand the scope of your app = cache + server.
  3. Geo-restrictions
    1. Could do this: IP address + restrictions. But in this case you need to put geo-block behind the cache and server.
    2. Instead, the api generate s geo-block config. Varnish loads in a memory map and checks
    3. If there is a geo violation, then Varnish returns a modified url, so the server can decide how to respond

++

Tim Whidden @1stdibs, an online market place for curated goods –“ ebay for rich people” spoke about Webpack, a front end module system. He described how modules increase the usability of functions and performs other functions like code compression.

++

Finally, Sarah Groff-Palermo @sarahgp.com spoke about how diagrams help her clarify the code she has written and provide documentation for her and others in the future.

She described a classification of learning types from sequential learner (likes tutorials) to global learners (like to see the big picture first) (see http://www4.ncsu.edu/unity/lockers/users/f/felder/public/ILSdir/styles.htm) . Sarah showed several diagrams and pointed out how they help her get and keep the global picture. She especially likes the paradigm from Ben Schneiderman  – overview, zoom and filter then details-on-demand

For further ideals she recommended

  1. the book Going Forth – lots of diagrams
  2. Now you see it by Stephen Few
  3. Flowing data – blog by Nathan Yau
  4. Keynote is a good tool to use for diagrams

posted in:  applications, Code Driven NYC, video    / leave comments:   No comments yet

Listening to Customers as you develop, assembling a #genome, delivering food boxes

Posted on September 21st, 2016

#CodeDrivenNYC

09/21/2016 @FirstMark, 100 Fifth Ave, NY, 3rd floor

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JJ Fliegelman @WayUp (formerly CampusJob) spoke about the development process used by their application which is the largest market for college students to find jobs. JJ talked about their development steps.

He emphasized the importance of specing out ideas on what they should be building and talking to your users.

They use tools to stay in touch with your customers

  1. HelpScout – see all support tickets. Get the vibe
  2. FullStory – DVR software – plays back video recordings of how users are using the software

They also put ideas in a repository using Trello.

To illustrate their process, he examined how they work to improved job search relevance.

They look at Impact per unit Effort to measure the value. They do this across new features over time. Can prioritize and get multiple estimates. It’s a probabilistic measure.

Assessing impact – are people dropping off? Do people click on it? What are the complaints? They talk to experts using cold emails. They also cultivate a culture of educated guesses

Assess effort – get it wrong often and get better over time

They prioritize impact/effort with the least technical debt

They Spec & Build – (product, architecture, kickoff) to get organized

Use Clubhouse is their project tracker: readable by humans

Architecture spec to solve today’s problem, but look ahead. Eg.. initial architecture – used wordnet, elastic search, but found that elastic search was too slow so they moved to a graph database.

Build – build as little as possible; prototype; adjust your plan

Deploy – they will deploy things that are not worse (e.g. a button that doesn’t work yet)

They do code reviews to avoid deploying bad code

Paul Fisher @Phosphorus (from Recombine – formerly focused on the fertility space: carrier-screening. Now emphasize diagnostic DNA sequencing) talked about the processes they use to analyze DNA sequences. With the rapid development of laboratory technique, it’s a computer science question now. Use Scala, Ruby, Java.

Sequencers produce hundreds of short reads of 50 to 150 base pairs. They use a reference genome to align the reads. Want multiple reads (depth of reads) to create a consensus sequence

To lower cost and speed their analysis, they focus on particular areas to maximize their read depth.

They use a variant viewer to understand variants between the person’s and the reference genome:

  1. SNPs – one base is changed – degree of pathogenicity varies
  2. Indels – insertions & deletions
  3. CNVs – copy variations

They use several different file formats: FASTQ, Bam/Sam, VCF

Current methods have evolved to use Spark, Parquet (columnar storage db), and Adam (use Avro framework for nested collections)

Use Zepplin to share documentation: documentation that you can run.

Finally, Andrew Hogue @BlueApron spoke about the challenges he faces as the CTO. These include

Demand forecasting – use machine learning (random forest) to predict per user what they will order. Holidays are hard to predict. People order less lamb and avoid catfish. There was also a dip in orders and orders with meat during Lent.

Fulfillment – more than just inventory management since recipes change, food safety, weather, …

Subscription mechanics – weekly engagement with users. So opportunities to deepen engagement. Frequent communications can drive engagement or churn. A/B experiments need more time to run

BlueApron runs 3 Fulfillment centers for their weekly food deliveries: NJ, Texas, CA shipping 8mm boxes per month.

posted in:  applications, Big data, Code Driven NYC, data, data analysis, startup    / leave comments:   No comments yet

Hardwired: product #design and delivering #magic

Posted on June 11th, 2016

#HardwiredNYC

06/07/2016 @ WeWork, 115 West 18rd Street, NY, 4th floor

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New Lab and Techstars talked briefly before the four speakers:

In the first presentation, Bob Coyne @Wordseye talked about his utility that takes a text description of a scene and creates an image matching that description. This allows users to create 3-d mages without complicated #3-d graphics programs.

They parse sentences to create a semantic map which can include commands to place items, change the lighting, reorient objects, etc. They see uses in education, gaming, and image search.

[Graphics are currently primitive and the manipulations are rough, but there are only 7 months old. Has promise for creating avatars and scenes for game prototypes. Text lack the subtly of gestures, so  text may need to be supplemented by gestures or other inputs.]

In the second presentation, Chris Allen @ iDevices – developers of connected home products and software – talked about the evolution of the company from an initial product in 2009 which was a connected grill.

Since then they have raised $20 million, were asked by Apple to develop products for HomeKit, currently market 7 HomeKit enabled products.

Experiences he communicated:

  1. Do you own research (don’t rely on conventional wisdom): despite being told that $99 was too high a price, they discovered that reducing the price to $75 did not increase sales.
  2. Resist pivoting away from your vision, especially when you have not intellectual property advantage: a waterproof case for phones failed.
  3. Create a great work environment and give your workers equity
  4. They build products that are compatible across platforms, but concentrate on just the three main platforms: Siri, Google, Amazon.

Next, Josh Clark @BigMedium talked about his vision of the future of interfaces: they will leap off the screen combining #speech and #gestures. They will be as magically as the devices in the world of Harry Potter. Unlike the Google glass, which was always an engineering project, we should be asking how can we make any object (even of a coffee cup) do more: design for the thing’s essential ‘thingness’.

Technology should be invisible, but magical:

  1. You can stand in front of a mirror memory and see how you look with a different color dress, or replay a video of what you look like when you turn around or do a side-by-side comparison with a previously worn dress.
  2. Asthmapolis site – when you have an asthma attack, you tap an app. Over time you can see across individuals their locations when they have an attack.
  3. A hackathon app using the Kinect in which one gestures to grab an image off a video so a still image from that moment appears on the phone.

It’s a challenge of imagination.

If the magic fails, we need to make sure the analogue device still works.

[In some cases, magic may not be enough. For instance, Asthmapolis pivoted away from ashma alone and now concentrates on a broader range of symptoms ]

In the last presentation, Martin Brioen@Pepsi talked about how his design team uses #prototyping to lead the development of new ideas.

Different groups within Pepsi have different perspectives and different priorities, so each views ideas differently, but to the get a consensus they all was to need to interact with the new product so they can see, touch, …

At each phase of development you use a different tools concentrated on the look of it, the feel of it, the functionality, etc. At each stage people need to interact with it to test it out. Don’t wait until you have a finished product. Don’t skip steps. Consider the full journey of the consumer;

Employ the least expensive way to try it out

They are not selling product, they are selling experiences: they create a test kitchen for the road.

posted in:  Apple, applications, hardware, Hardwired NYC, Internet of Things, psychology, startup    / leave comments:   No comments yet

How to Build a Bulletproof #SDK

Posted on June 3rd, 2016

#YahooTechTalksNYC

06/02/2016  @Yahoo, 229 West 43rd Street, NY, 10th floor

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The speaker from @Flurry emphasized four main themes on the way to making happy developers using your SDK:

  1. Respect users (hardware, not people in this case)
  2. Respect developers (people)
  3. Clarify assumptions (more about developers)
  4. Things you can’t control

Within each theme

  1. Respect users.
    1. Be considerate of battery life. Actions include
      1. Limit network calls
      2. Illuminate the screen only when necessary
    2. Network time is expensive – keep it to a minimum by downloading once and keeping the download in memory
    3. Phone space is limited – when you are done with the data you have downloaded, delete it.
    4. Minimize startup time
      1. Use techniques to keep startup time to below 2 seconds
      2. Don’t block the main thread
      3. If possible defer loading until after startup
  2. Respect developers
    1. Don’t do anything that causes the app to be rejected from the store, such as renaming system variables in iOS or using Id’s in Android that you should not reference
      1. Don’t violate any store policies
      2. Don’t request information that is off limits
      3. Don’t call private APIs
    2. Don’t put all your good ideas in a single SDK
      1. Bloatware is not welcome (see phone space and startup time above)
      2. It’s often better to have several small SDKs
    3. Create slim SDKs and ones that don’t leak
  1. Clarify assumptions
    1. Document all your assumptions
    2. Even better, design the API’s so developers can’t violate assumptions
    3. If the SDK fails, complain LOUDly in the debug logs
  2. Things you can’t control
    1. You need to be vigilant for system changes
    2. There is nothing you can do about them, but react quickly

There are differences between #iOS and #Android that require some modifications in the SDK. One example is in the speed of the exit from an app. Apple devices tend to have less memory, so they are more aggressive in terminating apps quickly and reclaiming memory. This is less so in Android.

posted in:  Android, Apple, applications, iOS, Yahoo Tech Talks    / leave comments:   No comments yet

#Wearable future – panel discussion

Posted on May 24th, 2016

#WearableTechNYC

05/24/2016 @Samsung Accelerator Chelsea, 30 W 26th, NY,  7th floor

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Panelists were:

Moderated by:

The discussion including the following points

  1. Successful wearable don’t ask people to adopt new behaviors
  2. Statement pieces are the exception. E.g. some handicapped people want to show off the technology (rather than hide it). In that case, the challenge is to design the wearable so it can be worn both as a normal as well as a statement piece.
  3. Battery technology is the limiting factor of wearables. Until batteries hold a charge for longer, advances in wearables will emphasize doing more with less power. E.g. meditation bracelet needs to be small since it needs to have high accuracy without being bulky. Also half of the weight of Ryan’s smart helmet is from batteries.
  4. Wearables, like other devices, need to be designed so the product line can be broadened over time. The smart helmet started as a motorcycle helmet, but that would have limited the ability to widen the product line.
  5. The mainstream fashion industry wants to get into tech, but does not want to be cheesy and don’t want to undercut their brand.
  6. For connected devices, Apple and Google Fit are often the best way to store data while preserving privacy. Eventually there may be specific cloud appliances to store data.
  7. The panelist were excited about other wearbles including
    1. Meta – “eyes up” display system – motorcycle helmet – AR display
    2. Military has invested in smart textiles – medical applications.
    3. Soles – 3d printing of soles for shoes which offers an apparel alternative to hand sewing and injection molding.
    4. Carbon3d- changing the way we 3d print.
    5. Exo-skeletons so workers can lift heavier loads.

posted in:  applications, Internet of Things, startup, Wearables    / leave comments:   No comments yet

#DataDrivenNYC: #FaultTolerant #Web sites, #Finance, Predicting #B2B buying behavior, training #DeepLearning

Posted on May 18th, 2016

#DataDrivenNYC

05/18/2016 @AXA auditorium, 787 7th  Avenue, NY

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Four speakers presented:

First, Nicolas Dessaigne @Algolia (Subscription service to access a search API) talked about the challenges building a highly fault-tolerant world-wide service. The steps resulted from their understanding of points of failure within their systems and the infrastructure their systems depend on.

Initially, they concentrated on their software development process including failed updates.  To overcome these problems, they update one server at a time (with a rack of servers), do partial updates, use Chef to automate deployment.

Then they migrated their DNS provider from .io to .net TLD to avoid slow response times they had seen intermittently in Asia. This was followed by the upgrades:

Feb 2015. Set up clusters of servers world-wide , so users have a server in their region:  lower latency

March 2015. Physically separate server clusters within a region to different providers

May 2015. Create fallback DNS servers

July 2015. Put a third data center online to make indexing robust

April 2016. Implement  a 1 second granularity for their system monitoring

Next, Matt Turck interviewed Louis DiModugno @AXA . In the US, AXA’s main focus is on predictive underwriting of insurance process. They also have projects to incorporate sensors into products and correctly route queries to call centers based on the demographics of the customer. World-wide they have three analysis hubs: France, US, Singapore (coming online).

Louis oversees both data and analytics in the U.S. and both he and the CTO report to the CIO.  They are interested in expanding their capabilities in areas such as creating unstructured databases from life insurance data that are currently on microfiche.

In the third presentation, Amanda Kahlow @6Sense talked about their business model  to provide information to customers in B2B commerce. They analyze business searches, customer web sites, visits to publisher’s (e.g. Forbes) web sites. Their goal is to determine the timing of customer purchases.

B2B purchases are different from B2C purchases since

  1. Businesses research their purchases online before they buy
  2. The research takes time (long sales cycle)
  3. The decision to buy involves multiple people within the company

So, there are few impulse buys and buyer behavior signals that a purchase is imminent.

The main CMO question is when (not who).

6sense ties data across searches (anonymous data). The goal is to identify when companies are in a specific part of the buying cycle, so sales can approach them now. (Example: show click-to-chat when the analytics says that the customer is ready to buy)

Lastly, Peter Brodsky @HyperScience  spoke about tools they are developing to speed machine learning. These include

  1. Tools to make it easier to add new data sets
  2. need to match fields, such as date which may be in different formats
  3. what to do with missing data
  4. need labeled data – lots of examples
  5. Speed up training time

The speed up is done by identifying subnets within the larger neural network. The subnets perform distinct functions. To determine if two subnets (in different networks) are equivalent, move one subnet from one network to replace another subnet in another network and see if the function is unchanged: Freeze the weights within the subnet and outside the subnet. Retrain the interface between the net and the subnet.

This creates building blocks which can be combined into larger blocks. These blocks can be applied to jump start the training process.

 

posted in:  AI, applications, Big data, data analysis, Data Driven NYC, startup    / leave comments:   No comments yet

DesignDrivenNYC: growing the design group and design function in organizations

Posted on May 11th, 2016

#DesignDrivenNYC

05/10/2016 @WeWork, 115 West 18th Street, 2nd floor, NY

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Three speakers spoke about their methods to integrate #design processes into all parts of product #development within retail-facing #financial firms. Betterment is a small, but rapidly growing company that is upsizing the design team. Citi and Learnvest are moving from a haphazard view of design to a single customer-centric design process throughout the organization:

In the first presentation, Jamie Strollo @Betterment spoke about the challenges the UI/UX design team faces as the company goes from a small startup to 9 designers to doubling by year end. Originally Betterment had a flat organization, but now there are challenges: integration of new people and avoiding the bad dynamics of large meetings. Design is the only shared resource across the company, so there was duplication in work. Some strategies for tackling a new design challenge are:

  1. Kick off strong – ask what is the problem?, what is success?, how do we measure?, constraints? Initially concentrate on measuring the drop-off rate when evaluating a design change. But eventually shift to measuring the effect on profitability.
  2. Assumption gathering – for stakeholders, high-level activity, focus on fears and confidence, agree of riskiest assumptions, talk to customers to validate.
  3. Focus on Top 5’s
    1. Great for large groups, Iterations
    2. High-level activity
    3. Select only top 5 design needs
    4. Helps to Establish patterns
    5. Bridge the “delete” conversation
  4. Ideation / paper prototyping – better for smaller groups. Bring in other areas of expertise, let others have a voice
  5. Managing feedback – decide who are the decision makers, who to inform. No big UI critiques (a polished presentation gives a finished feeling and makes it hard to change), share often and early, speak about objectives and key results. Start conversation by what is the objective.
  6. Invest in relationships – customers and coworkers

Another challenge as the company grows is creating a method to give better estimates of the time to complete a design. This is hard since much of time goes into understanding the problem

In the second presentation, Billy Seabrook @Citi spoke about how Citi has created a single world-wide design team. The goal of better design is to move customer’s view of the bank from a transactional experience to a relational experience.

Starting six months ago, Billy has approached the following challenges within the bank:

  1. Organization – create agile groups adding individuals looking at strategies; research and usability studies; producers to keep on time and budget.
  2. Skills – Create a cohort of coaches to teach design thinking throughout the bank. Minimal viable product is at the intersection of business viability + customer desirability + technical feasibility; Partner with IDO to foster agile design thinking throughout all parts of the bank
  3. Applied Projects – Citi Fintech launched 6 months ago to launch the bank of the future: focus on mobile (mobile only), speed and simplicity (2 weeks of design thinking + 2 week dev sprints)

To foster common branding and look-and-feel world-wide, document templates are shared world-wide. Senior people in each location are in constant contact.

Coaches have backgrounds of policy or planning. The main thing is the mind set of being customer centric.  In the past, most of the product design was outsourced, so design principals were inconsistent also there was a lack of consistency in evaluating designs.

The Design group reports to the COO of Fintech and is considered a cost center (despite its’ close affiliation with profit centers).

In the final presentation, Abigail Hart Gray @Learnvest (help financial planners create simple, effective, .., plans for retirement…) talked about the challenges of integrating a unified design process into product development at Northwestern Mutual (acquired Learnvest last year).

Abigail started with the question of a Defining Design Driven? She interviewed experienced design professionals and found commonalities:

  1. Team structure – be at the table when decision are made
  2. Process – iterative process
  3. Outcomes – but interviewees disagreed up whether good design resulted in products that were best for customer or best for business.

She talked about becoming a champion of design within the company:

  1. know the capabilities
  2. need to invest in design
  3. designers must educate their audience and sell their vision.

If you need to explain the interface, it stinks!

Steps to get started (data-driven, customer centric, outputs oriented):

  1. pick something with low stakes – no bottom line implications
  2. research well
  3. measure everything
  4. share results
  5. repeat

As an aside on the measurement process, one needs to consider the possibility that customer behavior changes by knowing that they are being observed. The Hawthorne effect can elevate or suppress responses depending on prior customer engagement (friends&family vs. the general population), frequency of engagement (daily vs. occasional), etc.

posted in:  applications, DesignDrivenNYC, finance, UI, UX    / leave comments:   No comments yet