#Dependant on #dumb #data and is making #bad #choices? #Douglas #Adams

Data, Big Data, Artificial Intelligence

Clients don’t understand their customers, they just think they do!

It’s not for the lack of trying or spending millions on developing and building huge data systems, the problems are many but can be traced back to one simple thing;

“Data only describes part of the what is happening and almost nothing of the why, let alone what should be done to change the situation”

Clients have been sold that data gives them the answers and that big data will close the loop for them to understand the upstream and downstream thinking of their customers, WRONG.

Douglas Adams noticed the real problem

Douglas Adam’s said  “But even Amazon has only got part of the picture. Like real world shops, they can only record the sales they actually make. What about the sales they don’t make and don’t know that they haven’t made because they haven’t made them?” Douglas Adams “The Salmon of Doubt” by Permission of Pan Macmillan. That pretty much covers the problem if you extrapolate the thinking for Data Analytics, Big Data or even Artificial Intelligence based Data and Decision systems.

“Data is binary a yes or no (even complex views), it does not capture motivation, intention, desire, cognition, distraction or any other human reasoning or pattern”

child pretending to be robot data prentending to be truth
child pretending to be robot data prentending to be truth

A child pretending to be a robot just as data pretends to be the truth, he is a kind of robot and data is a kind of truth

A pure Data approach to understanding customers will provide the wrong data because data is an absolute and people are not. Even with Artificial Intelligence it only works from the starting point you give it, if any of the perimeters are wrong the whole data sample is wrong.

Guide to understanding Customers

  • Data, Big Data, Artificial Intelligence – Tells you what
  • People in target demographic – Tell you why

People in target demographic

User research answers the question Why have we not made the Sale? through the only people equipped to answer the question, consumers. This is not market research, its scientific without a predetermined agenda or outcome. User Research is a problem solving method that offers solutions by finding the right questions, finding the right people and asking the questions in a way that does not lead or direct the answers.

There are right questions and people to ask?

This may sound a little Adamsesque (if you ask the answer to Life, the Universe and Everything you get 42, because it the wrong question). Getting the questions or setup wrong is the real problem with an Analytics approach to a Diagnostic process. While it may be reasonably expected by a seller to directly ask, why didn’t a visitor become a buyer or register. Visitors may be asking themselves where am I? what does this do? this does not make sense, should that be happening? technology, why do I bother? Why has my screen gone pink? None of these “in mind” experiences are expressed in the data or even a consideration for the data schema design.

A visitors experience is not only defined by the online environment but they bring past experiences, desires and doubts about their current experience. Without these insights from research, it is difficult for clients to grasp potential problems, gain a good return upon their investment (ROI), innovate to fit the market and consumer needs or break into a new market sector.

Reasons that Data is Trusted and People are Not

It appears to come down to scale and a short sighted approach to costs. Buying an Analytics Solution appears to tick all the boxes, even if in reality it does not. While using Research Companies or in-house Research Teams seems expensive in comparison.

“The real trick is to understand you need both, you always did”

retro robot toys, not what you expect when you say robot today
retro robot toys, not what you expect when you say robot today

When I first started using Web Position Gold (the analytics tool), bought by Webtrends long before Google Analytics existed or the current proliferation of products promising the impossible, we used it to spot trouble only. We would then do some user testing in the area, working out possible failure scenarios, from there we would suggest two or three solutions and build them for A/B testing to see what worked and what did not. Everything was monitored and all the data from both analytics and user testing was collated into one final solution. Sometimes there was a single resolution, a re-architecting of a section, in one project I kept 16 pathways active because they all delivered transactions for different types of customers.

The thing is just as there is no absolute way to find out the problem, resolution or adaptive innovation except byDiagnostics a digital and human activity.

Diagnostics

[Data+Human+Solution+Testing=Resolution]

+

[Feedback+Data+Human=Adaptive Innovation]

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#Information #Architecture (IA) the #classification of #information Part 2

Given the response from the last post I’m going to take the educational publishing example a bit further, if I have time before my next contract I will also create an investment banking example. I am also being asked for diagrams that explain these relationships, again if I have time I will do these also.

Educational publishing Information Architecture (IA)

The last point in the previous post was describing multiple audiences looking at the same content from different perspectives. The example in educational publishing the audiences often include;

  • Distributors
  • Sellers
  • Institution
  • Teachers
  • Pupils
  • Parents

Each one of these groups will have a very specific context of use, when looking for content, the descriptions they use and understand to find it and their underlying purpose in doing so. In this case they will each require a separate structure around an entity and may require their own version of the taxonomy.

Additionally there are criteria that operated as informational facets (now commonly associated with faceted search) which act as secondary entities;

  • ISBN
  • Bulk price
  • Unit price
  • Country standards
  • Regional standards
  • Education level
  • Education target
  • Education skills
  • Education method
  • Exam board
  • Exam year
  • Pupil/student age
  • Content subject
  • Content brand
  • Content group
  • Content purpose
  • Language
  • Language Tone of Voice
  • Media type
  • Media format

The above entities enable the audiences to find the content assets that meet their specific needs. It is very important at this stage not to confuse entities with hierarchies. Hierarchies are the structuring of entities in a direct or indirect relationship that are above or below (immediate superior or subordinate) this also includes cross related relationships. As previously mentioned (in the last post) there may already be standard hierarchies in the domain in question that should be observed.

But how do you find these entities in any domain?

Taking the above example the standard hierarchy in publishing is ISBN a review of several entities within a single ISBN item will reveal many of entities above. To get the rest research is required (it cannot be done any other way);

  1. Find out who the audience is and what is their objective?
  2. Find out what are the rules, laws and governance?
  3. Find out who buys, distributes, delivers, services, resells and what their relationship is to the originator?
  4. Find out specifically who the audience is currently, competitor and target audience?

Define ‘What is the smallest component of viable (useful) information?’ and use that to model the information system. I have worked with several huge education providers and universities and the questions I ask is ‘What is a course?’;

  • A course has a title
  • A course has duration, with a start and an end
  • A course has a subject
  • A course has a level
  • A course has prerequisites
  • A course has an outcome, which leads to options
  • A course has a delivery mechanism

I also ask, ‘Who is a student?’, ‘Who is a tutor?’, ‘What is an outcome?’ even ‘What is a college?’, if a course has a regular location then this creates a secondary set of entities.

  • A location has an address, telephone number, email address
  • A location has facilities
  • A location has transportation links
  • A location has a community
  • A location has accommodation

And it goes on and on, this is Information Architecture 101.

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#Information #Architecture (IA) is not another name for #User #Experience (UX)

IA is not another name for UX

User experience (UX) and Information Architecture (IA) are very different and have separate skill sets, processes and outputs.

I often talk to people who add IA on the their CV as if it’s some simple skill;

IA is actually more complex and difficult than UX

IA is also hundreds of years old as an activity while UX is less than twenty in it’s current form.

  • Information architecture is involved in the classification and structure of information.
  • User experience is involved in; defining who the audience is, what they can do, how they can do it and matching the aspiration of the content provider with the desires of the audience.

Related

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#Information Architecture (IA) the #classification of #information

A simple website may only include 8 top level pages, 50 secondary and perhaps only 300 tertiary labelled (taxonomy) navigation elements, that’s only 358 entities. However, Information Architecture tends to be associated with the structure and classification of websites, intranets and software that accesses in excess of 100,000+ separate entities to be classified. I have worked on several huge taxonomies for Government, Publishers, Colleges, Universities, Insurance Companies and Banks involved in trading that involve between 1,000,000 and 25,000,000+ entities.

An Information Architect embarking on a new project will investigate if there is a standardised ontology for the project domain and conduct a content audit.

An ontology is a formal naming and definition of the types, properties, and interrelationships of the entities that really or fundamentally exist for a particular domain.

For example, if the project is a United Kingdom, Government project then there is a standard ontology and a classification of entities within a taxonomy.

If a standard exists the task is relatively simple but highly time-consuming as it then involves matching the in use ontology with the standardise one. If no standard exists a standard needs to be created. Creating a standard ontology is done through domain research. How do other’s of the same domain describe things, at this point it is worth considering ownership of language in the form of brands, trademarks, patients and de facto standards as a source of information, terms and potential restrictions.

Once the entities have been defined with their attributes and all the potential interrelationships then this is combined with or overwrites the content audit to define the new system taxonomy.

However, there may be multiple audiences looking at the same content from different perspectives. For example in educational publishing the audiences could be;

  • Distributors
  • Sellers
  • Institutions
  • Teachers
  • Pupils
  • Parents

Each one of these groups will have a very specific context of use when looking for content, the descriptions they use and understand to find it and their underlying purpose in doing so. In this case, they will each require a separate structure around an entity and may require their own version of the ontology.

Additionally there are criteria that operated as informational facets (now commonly associated with faceted search) which act as secondary entities;

  • ISBN
  • Bulk price
  • Unit price
  • Country standards
  • Regional standards
  • Education level
  • Education target
  • Education skills
  • Education method
  • Exam board
  • Exam year
  • Pupil/student age
  • Content subject
  • Content brand
  • Content group
  • Content purpose
  • Language
  • Language Tone of Voice
  • Media type
  • Media format

The above entities enable the audiences to find the content assets that meet their specific needs. It is very important at this stage not to confuse entities with hierarchies. Hierarchies are the structuring of entities in a direct or indirect relationship that are above or below (immediate superior or subordinate) this also includes cross related relationships. As previously mentioned there may already be standard hierarchies in the domain in question that should be observed.

But how do you find these entities in any domain?

Taking the above example the standard hierarchy in publishing is ISBN a review of several entities within a single ISBN item will reveal many of entities above. To get the rest research is required (it cannot be done any other way);

  1. Find out who the audience is and what is their objective?
  2. Find out what are the rules, laws and governance?
  3. Find out who buys, distributes, delivers, services, resells and what their relationship is to the originator?
  4. Find out specifically who the audience is currently, competitor and target audience?

Define ‘What is the smallest component of viable (useful) information?’ and use that to model the information system. I have worked with several huge education providers and universities and the questions I ask is, ‘What is a course?’;

  • A course has a title
  • A course has duration, with a start and an end
  • A course has a subject
  • A course has a level
  • A course has prerequisites
  • A course has an outcome, which leads to options
  • A course has a delivery mechanism

I also ask, ‘Who is a student?’, ‘Who is a tutor?’, ‘What is an outcome?’ even ‘What is a college?’, if a course has a regular location then this creates a secondary set of entities.

  • A location has an address, telephone number, email address
  • A location has facilities
  • A location has transportation links
  • A location has a community
  • A location has accommodation

And it goes on and on, this is Information Architecture 101.

This is a repost from an article first published in 2007.

About the Author

Karl Smith is Computer Scientist or as he describes himself a Creative Scientist. He has a BA in Design from the 1980’s and an MSc in Interactive Technologies for E-commerce from the 2000’s. His MSc was technically focused with a large portion given over to Transformation and Very Large Databases, data warehouses, Data Mining, online analytical Processing (OLAP), web browsers and search engines, optimisation, recovery and backup, database connectivity technologies. Including writing SQL.

Karl Smith is an acknowledged leader in the field of Human Centred Design, User Experience and Usability and has been honoured with a Fellowship by the British Computer Society. He is also the Founder of several organisations including The Human Centered Design Society.

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O2 support has awful usability

O2 gets points for trying

I like other users were quite happy when O2 decided to spend some time and money to tie together their various online systems to at least give the appearance of a unified system. You can tell it’s all user experience and no information architecture though, everything links so there are no dead ends, unfortunately the information being sought is missing it’s an annoying merry go round.

Lucy help box in O2O2 fakes live support

The thing that I cannot think of an excuse for is an automated support search tool built to look like live support. Dear Lucy you gave me hope, then you took it away, you made me think O2 cared about me and the business I bring them. But no your not real your a search tool, that’s not really all that good. Changing interactive and interaction metaphors is indeed very brave and extraordinarily stupid as it aggravates users. For users in a support system the normal response is to look for another service supplier or phone up to vent on someone.

Still at least there was a feedback form for me to say how pathetic the experience was. Feedback form for fake live support

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#UX #requirements #gathering methods #determine #value

Requirements gathering methods determine value

There are lots ways to elicit UCD requirements so I don’t intend on listing them all here, what I will note are some of the effective ways that I utilise. They can be described as structured, unstructured or a mixture of the two, but importantly the methods produce differing depth of requirements dependant not only on the method but on the skill of the facilitator and the characteristics of physical location used. In effect the method used is limited by the capability of the facilitator.

Research Structure

It is critical to determine if there is enough usable information around the project to be able to establish a valid research start point. Get the questions right and the answers follow, get the questions wrong and you’ll waste a fortune. If your unsure carry out a pilot study to find out what the questions are.

Methods

Observational / Reflective

Asking users to carry out their normal activities while being observed does take quite a bit of setting up so that the activity data is not skewed as a result of the observation. It is critical not to participate or lead the user. It is especially common when users are told the fine detail about the project; they will try to give you what they think you want. Often this is because they feel threatened by the process, the old notion of time and motion leading to them getting sacked is embedded in British culture. However this type of method is very useful in a pilot study to determine what type of activities should form the basis of questioning or workshop based co-creation.

Narrative

Narrative is about stories, like the narrator in a play, they see the whole thing being able to talk in the present, remind of the past, note the future or describe a similie that others can relate to even if they cannot yet relate to the main story.

Asking users to describe how they think something should work (Cognitive Walkthrough), what the differences might be for different user types, how it’s done now, is there anything in other technology or experience that they can relate it to. In this way a multi-path picture can be built up from each participant for each interactive pathway within the new system or technology.

Diagnostic

Diagnostic is about testing something this will often manifest through workshops with groups of people. By setting a group a task or co-creation ‘Design the front page of your new website’ spending ‘K10,000,000 that’s Karl’s currency’ on to meet your goals and noting the group dynamic, decision making and outputs, very rich information is revealed by participants.

Show and Tell

Show and tell is imperative to feed back to participants what has happened to the information they have provided. More it can be used to validate key concepts and project directions in a fairly light environment, where participants can be highly critical knowing that everyone else is in the same frame of reference for that meeting.

Some of the Problems

Although the whole process exists to elicit information suitable to influence the project, some participants will have pet requirements that if not given the correct level of sycophantic response they will seek to invalidate the entire project as it does not represent their vision of what the project is any more. This type of attitude is very common when working with low pay low (self) risk bureaucrats, as they can personally destroy projects then blame it on ‘expensive’ consultants.

Case Study 4

On a national census project that was due to be run online and offline a member of the bureaucratic project team insisted that their opinions were correct on the basis of having won an award for a website ten years earlier. The opinions were very dated to the point of being bad practice, incorrect and were a constant disabler to the project. This client refused to consider an open support system, marketing website (to prepare country) or full language landing pages to support the full ethnic background of the country in question. The day to day engagement was painful, but the UX for the several forms, question logic and user interactions were eventually signed off even though the client never signed off the personas based upon the requirements they gave.

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