Journey Maps are excellent as a tool for deriving requirements, as well as better understanding the customer. Similar to a paper-based, use case process to understand an “Actor” on their business workflow, journey maps visualize the customer/user experiences. The article below is a primer to the creation and usage of a Journey Map.
Summary: Journey maps combine two powerful instruments—storytelling and visualization—in order to help teams understand and address customer needs. While maps take a wide variety of forms depending on context and business goals, certain elements are generally included, and there are underlying guidelines to follow that help them be the most successful.
What Is a Customer Journey Map?
In its most basic form, journey mapping starts by compiling a series of user goals and actions into a timeline skeleton. Next, the skeleton is fleshed out with user thoughts and emotions in order to create a narrative. Finally, that narrative is condensed into a visualization used to communicate insights that will inform design processes.
Anyone who is anti “Big Brother”, this may not be the article for you, in fact, skip it. 🙂
The Pendulum Swings Away from GDPR
In the not so distant future, “Data Bank” companies consisting of Subject Matter Experts (SME) across all verticals, may process your data feeds collected from your purchase and user behavior profiles. Consumers will be encouraged to submit their data profiles into a Data Bank who will offer incentives such as a reduction of insurance premiums to cash back rewards.
Everything from activity trackers, home automation, to vehicular automation data may be captured and aggregated. The data collected can then be sliced and diced to provide macro and micro views of the information. On the abstract, macro level the information may allow for demographic, statistical correlations, which may contribute to corporate strategy. On a granular view, the data will provide “data banks” the opportunity to sift through data to perform analysis and correlations that lead to actionable information.
Is it secure? Do you care if a hacker steals your weight loss information?May not be an issueif collected Purchase and Use Behavior Profiles aggregate into a Blockchain general ledger. Data Curators and Aggregators work with SMEs to correlate the datainto:
Canned, ‘intelligent’ reports targeted for a specific subject matter, or across silos of data types
‘Universes’ (i.e. Business Objects) of data that may be ‘mined’ by consumer approved, ‘trusted’ third party companies, e.g. your insurance companies.
Actionable information based on AI subject matter rules engines and consumer rule transparency may be provided.
“Data Banks” may be required to report to their customers who agreed to sell their data examples of specific rows of the data, which was sold on a “Data Market”.
Consumers may have the option of sharing their personal data with specific companies by proxy, through a ‘data bank’ granular to the data point collected. Sharing of Purchase and User Behavior Profiles:
may lower [or raise] your insurance premiums
provide discounts on preventive health care products and services, e.g. vitamins to yoga classes
Targeted, affordable, medicine that may redirect the choice of the doctor to an alternate. The MD would be contacted to validate the alternate.
The curriated data collected may be harnessed by thousands of affinity groups to offer very discrete products and services. Purchase and User Behavior Profiles, correlated information stretches beyond any consumer relationship experienced today.
At some point, health insurance companies may require you to wear a tracker to increase or slash premiums. Auto Insurance companies may offer discounts for access to car smart data to make sure suggested maintenance guidelines for service are met.
You may approve your “data bank” to give access to specific soliciting government agencies or private firms looking to analyze data for their studies. You may qualify based on the demographic, abstracted data points collected for incentives provided may be tax credits, or paying studies.
Purchase and User Behavior Profiles: Adoption and Affordability
If ‘Data Banks’ are allowed to collect Internet of Things (IoT) device profile and the devices themselves are cost prohibitive. here are a few ways to increase their adoption:
[US] tax coupons to enable the buyer, at the time of purchase, to save money. For example, a 100 USD discount applied at the time of purchase of an Activity Tracker, with the stipulation that you may agree, at some point, to participate in a study.
Government subsidies: the cost of aggregating and archiving Purchase and Behavioral profiles through annual tax deductions. Today, tax incentives may allow you to purchase an IoT device if the cost is an itemized medical tax deduction, such as an Activity Tracker that monitors your heart rate, if your medical condition requires it.
Auto, Life, Homeowners, and Health policyholders may qualify for additional insurance deductions
Affinity branded IoT devices, such as American Lung Association may sell a logo branded Activity Tracker. People may sponsor the owner of the tracking pedometer to raise funds for the cause.
The World Bank has a repository of data, World DataBank, which seems to store a large depth of information:
World Bank Open Data: free and open access to data about development in countries around the globe.”
Here is the article that inspired me to write this article:
Initiatives such as General Data Protection Regulation (GDPR) and other privacy initiatives which seek to constrict access to your data to you as the “owner”, as a byproduct, create opportunities for you to sell your data.
Blockchain: Purchase, and User Behavior Profiles
As your “vault”, “Data Banks” will collect and maintain your two primary datasets:
As a consumer of goods and services, a Purchase Profile is established and evolves over time. Online purchases are automatically collected, curated, appended with metadata, and stored in a data vault [Blockchain]. “Offline” purchases at some point, may become a hybrid [on/off] line purchase, with advances in traditional monetary exchanges, and would follow the online transaction model.
User Behavior (UB) profiles, both on and offline will be collected and stored for analytical purposes. A user behavior “session” is a use case of activity where YOU are the prime actor. Each session would create a single UB transaction and are also stored in a “Data Vault”. UB use cases may not lead to any purchases.
Not all Purchase and User Behavior profiles are created equal. Eg. One person’s profile may show a monthly spend higher than another. The consumer who purchases more may be entitled to more benefits.
There are, of course, 3rd party platforms that perform very well, are feature rich, and agnostic to all file types. For example, within a very short period of time, low cost, and possibly a few plugins, a WordPress site can be configured and deployed to suit your needs of Digital Asset Managment (DAM). The long-term goal is to incorporate techniques such as Auto Curation to any/all files, leveraging an ever-growing intelligent taxonomy, a taxonomy built on user-defined labels/tags, as well an AI rules engine with ML techniques. OneDrive, as a cloud storage platform, may bridge the gap between JUST cloud storage and a DAM.
Content Creation Apps and Auto Curation
The ability for Content Creation applications, such as Microsoft Word, to capture not only the user-defined tags but also the context of the tags relating to the content.
When ingesting a Microsoft PowerPoint presentation, after consuming the file, and Auto Curation process can extract “reusable components” of the file, such as slide header/name, and the correlated content such as a table, chart, or graphics.
Ingesting Microsoft Excel and Auto Curation of Workbooks may yield “reusable components” stored as metadata tags, and their correlated content, such as chart and table names.
Ingesting and Auto Curation of Microsoft Word documents may build a classic Index for all the most frequently occurring words, and augment the manually user-defined tags in the file.
Ingestion of Photos [and Videos] into and Intelligent Cloud Storage Platform, during the Auto Curation process, may identify commonly identifiable objects, such as trees or people. These objects would be automatically tagged through the Auto Curation process after Ingestion.
Ability to extract the content file metadata, objects and text tags, to be stored in a standard format to be extracted by DAMs, or Intelligent Cloud Storage Platforms with file and metadata search capabilities. Could OneDrive be that intelligent platform?
A user can search for a file title or throughout the Manual and Auto Curated, defined metadata associated with the file. The DAM or Intelligent Cloud Storage Platform provides both search results. “Reusable components” of files are also searchable.
For “Reusable Components” to be parsed out of the files to be separate entities, a process needs to occur after Ingestion Auto Curration.
Content Creation application, user-entry tag/text fields should have “drop-down” access to the search index populated with auto/manual created tags.
Auto Curation and Intelligent Cloud Storage
The intelligence of Auto Curation should be built into the Cloud Storage Platform, e.g. potentially OneDrive.
At a minimum, auto curation should update the cloud storage platform indexing engine to correlate files and metadata.
Auto Curation is the ‘secret sauce’ that “digests” the content to build the search engine index, which contains identified objects (e.g. tag and text or coordinates) automatically
Auto Curation may leverage a rules engine (AI) and apply user configurable rules such as “keyword density” thresholds
Artificial Intelligence, Machine Learning rules may be applied to the content to derive additional labels/tags.
If leveraging version control of the intelligent cloud storage platform, each iteration should “re-index” the content, and update the Auto Curation metadata tags. User-created tags are untouched.
If no user-defined labels/tags exist, upon ingestion, the user may be prompted for tags
Auto Curation and “3rd Party” Sources
In the context of sources such as a Twitter feed, there exists no incorporation of feeds into an Intelligent Cloud Storage. OneDrive, Cloud Intelligent Storage may import feeds from 3rd party sources, and each Tweet would be defined as an object which is searchable along with its metadata (e.g. likes; tags).
Operating System, Intelligent Cloud Storage/DAM
The Intelligent Cloud Storage and DAM solutions should have integrated search capabilities, so on the OS (mobile or desktop) level, the discovery of content through the OS search of tagged metadata is possible.
OneDrive has no ability to search Microsoft Word tags
The UI for all Productivity Tools must have a comprehensive and simple design for leveraging an existing taxonomy for manual tagging, and the ability to add hints for auto curation
Currently, Microsoft Word has two fields to collect metadata about the file. It’s obscurely found at the “Save As” dialog.
The “Save As” dialogue box allows a user to add tags and authors but only when using the MS Word desktop version. The Online (Cloud) version of Word has no such option when saving to Microsoft OneDrive Cloud Storage
Auto Curation (Artificial Intelligence, AI) must inspect the MS Productivity suite tools, and extract tags automatically which does not exist today.
No manual taging or Auto Curation/Facial Recognition exists.
about deconstructing existing functionality of entire Photo Archive and Sharing platforms.
to bring an awareness to the masses about corporate decisions to omit the advanced capabilities of cataloguing photos, object recognition, and advanced metadata tagging.
Backstory: The Asks / Needs
Every day my family takes tons of pictures, and the pictures are bulk loaded up to The Cloud using Cloud Storage Services, such as DropBox, OneDrive, Google Photos, or iCloud. A selected set of photos are uploaded to our favourite Social Networking platform (e.g. Facebook, Instagram, Snapchat, and/or Twitter).
Every so often, I will take pause, and create either a Photobook or print out pictures from the last several months. The kids may have a project for school to print out e.g. Family Portrait or just a picture of Mom and the kids. In order to find these photos, I have to manually go through our collection of photographs from our Cloud Storage Services, or identify the photos from our Social Network libraries.
Social Networking Platform Facebook
As far as I can remember the Social Networking platform Facebook has had the ability to tag faces in photos uploaded to the platform. There are restrictions, such as whom you can tag from the privacy side, but the capability still exists. The Facebook platform also automatically identifies faces within photos, i.e. places a box around faces in a photo to make the person tagging capability easier. So, in essence, there is an “intelligent capability” to identify faces in a photo. It seems like the Facebook platform allows you to see “Photos of You”, but what seems to be missing is to search for all photos of Fred Smith, a friend of yours, even if all his photos are public. By design, it sounds fit for the purpose of the networking platform.
Automatically upload new images in bulk or one at a time to a Cloud Storage Service ( with or without Online Printing Capabilities, e.g. Photobooks) and an automated curation process begins.
The Auto Curation process scans photos for:
“Commonly Identifiable Objects”, such as #Car, #Clock, #Fireworks, and #People
Auto Curation of new photos, based on previously tagged objects and faces in newly uploaded photos will be automatically tagged.
Once auto curation runs several times, and people are manually #taged, the auto curation process will “Learn” faces. Any new auto curation process executed should be able to recognize tagged people in new pictures.
Auto Curation process emails / notifies the library owners of the ingestion process results, e.g. Jane Doe and John Smith photographed at Disney World on Date / Time stamp. i.e. Report of executed ingestion, and auto curation process.
After upload, and auto curation process, optionally, it’s time to manually tag people’s faces, and any ‘objects’ which you would like to track, e.g. Car aficionado, #tag vehicle make/model with additional descriptive tags. Using the photo curator function on the Cloud Storage Service can tag any “objects” in the photo using Rectangle or Lasso Select.
Curation to Take Action
Once photo libraries are curated, the library owner(s) can:
Automatically build albums based one or more #tags
Smart Albums automatically update, e.g. after ingestion and Auto Curation. Albums are tag sensitive and update with new pics that contain certain people or objects. The user/ librarian may dictate logic for tags.
Where is this Functionality??
Why are may major companies not implementing facial (and object) recognition? Google and Microsoft seem to have the capability/size of the company to be able to produce the technology.
Is it possible Google and Microsoft are subject to more scrutiny than a Shutterfly? Do privacy concerns at the moment, leave others to become trailblazers in this area?
Protecting the Data Warehouse with Artificial Intelligence
Teleran is a middleware company who’s software monitors and governs OLAP activity between the Data Warehouse and Business Intelligence tools, like Business Objects and Cognos. Teleran’s suite of tools encompass a comprehensive analytical and monitoring solution called iSight. In addition, Teleran has a product that leverages artificial intelligence and machine learning to impose real-time query and data access controls. Architecture also allows for Teleran’s agent not to be on the same host as the database, for additional security and prevention of utilizing resources from the database host.
Key Features of iGuard:
Policy engine prevents “bad” queries before reaching database
Patented rule engine resides in-memory to evaluate queries at database protocol layer on TCP/IP network
Patented rule engine prevents inappropriate or long-running queries from reaching the data
70 Customizable Policy Templates
SQL Query Policies
Create policies using policy templates based on SQL Syntax:
Require JOIN to Security Table
Column Combination Restriction – Ex. Prevents combining customer name and social security #
Table JOIN restriction – Ex. Prevents joining two different tables in same query
Equi-literal Compare requirement – Tightly Constrains Query Ex. Prevents hunting for sensitive data by requiring ‘=‘ condition
By user or user groups and time of day (shift) (e.g. ETL)
Blocks connections to the database
White list or black list by
DB User Logins
OS User Logins
Applications (BI, Query Apps)
Rule Templates Contain Customizable Messages
Each of the “Policy Templates” has the ability to send the user querying the database a customized message based on the defined policy. The message back to the user from Teleran should be seamless to the application user’s experience.
Machine Learning: Curbing Inappropriate, or Long Running Queries
iGuard has the ability to analyze all of the historical SQL passed through to the Data Warehouse, and suggest new, customized policies to cancel queries with certain SQL characteristics. The Teleran administrator sets parameters such as rows or bytes returned, and then runs the induction process. New rules will be suggested which exceed these defined parameters. The induction engine is “smart” enough to look at the repository of queries holistically and not make determinations based on a single query.
The ultimate goal, in my mind, is to have the capability within a Search Engine to be able to upload an image, then the search engine analyzes the image, and finds comparable images within some degree of variation, as dictated in the search properties. The search engine may also derive metadata from the uploaded image such as attributes specific to the image object(s) types. For example, determine if a person [object] is “Joyful” or “Angry”.
As of the writing of this article, search engines Yahoo and Microsoft Bing do not have the capability to upload an image and perform image/pattern recognition, and return results. Behold, Google’s search engine has the ability to use some type of pattern matching, and find instances of your image across the world wide web. From the Google Search “home page”, select “Images”, or after a text search, select the “Images” menu item. From there, an additional icon appears, a camera with the hint text “Search by Image”. Select the Camera icon, and you are presented with options on how Google can acquire your image, e.g. upload, or an image URL.
Select the “Upload an Image” tab, choose a file, and upload. I used a fictional character, Max Headroom. The search results were very good (see below). I also attempted an uncommon shape, and it did not meet my expectations. The poor performance of matching this possibly “unique” shape is mostly likely due to how the Google Image Classifier Model was defined, and correlating training data that tested the classifier model. If the shape is “Unique” the Google Search Image Engine did it’s job.
Google Image Search Results – Max Headroom
Google Image Search Results – Odd Shaped Metal Object
The Google Search Image Engine was able to “Classify” the image as “metal”, so that’s good. However I would have liked to see better matches under the “Visually Similar Image” section. Again, this is probably due to the image classification process, and potentially the diversity of image samples.
A Few Questions for Google
How often is the Classifier Modeling process executed (i.e. training the classifier), and the model tested? How are new images incorporated into the Classifier model? Are the user uploaded images now included in the Model (after model training is run again)? Is Google Search Image incorporating ALL Internet images into Classifier Model(s)? Is an alternate AI Image Recognition process used beyond Classifier Models?
I’m not sure if the Cloud Vision API uses the same technology as Google’s Search Image Engine, but it’s worth noting. After reaching the Cloud Vision API starting page, go to the “Try the API” section, and upload your image. I tried a number of samples, including my odd shaped metal, and I uploaded the image. I think it performed fairly well on the “labels” (i.e. image attributes)
Using the Google Cloud Vision API, to determine if there were any WEB matches with my odd shaped metal object, the search came up with no results. In contrast, using Google’s Search Image Engine produced some “similar” web results.
Finally, I tested the Google Cloud Vision API with a self portrait image. THIS was so cool.
The API brought back several image attributes specific to “Faces”. It attempts to identify certain complex facial attributes, things like emotions, e.g. Joy, and Sorrow.
The API brought back the “Standard” set of Labels which show how the Classifier identified this image as a “Person”, such as Forehead and Chin.
Finally, the Google Cloud Vision API brought back the Web references, things like it identified me as a Project Manager, and an obscure reference to Zurg in my Twitter Bio.
The Google Cloud Vision API, and their own baked in Google Search Image Engine are extremely enticing, but yet have a ways to go in terms of accuracy %. Of course, I tried using my face in the Google Search Image Engine, and looking at the “Visually Similar Images” didn’t retrieve any images of me, or even a distant cousin (maybe?)
Businesses already exist which have developed and sell Virtual Receptionist, that handle many caller needs (e.g. call routing).
However, AI Digital Assistants such as Alexa, Cortana, Google Now, and Siri have an opportunity to stretch their capabilities even further. Leveraging technologies such as Natural language processing (NLP) and Speech recognition (SR), as well as APIs into the Smartphone’s OS answer/calling capabilities, functionality can be expanded to include:
Call Screening – The digital executive assistant asks for the name of the caller, purpose of the call, and if the matter is “Urgent”
A generic “purpose” response or a list of caller purpose items can be supplied to the caller, e.g. 1) Schedule an Appointment
The smartphone’s user would receive the caller’s name, and the purpose as a message back to the UI from the call, currently in a ‘hold’ state,
The smartphone user may decide to accept the call, or reject the call and send the caller to voicemail.
Call / Digital Assistant Capabilities
The digital executive assistant may schedule a ‘tentative’ appointment within the user’s calendar. The caller may ask to schedule a meeting, the digital executive assistant would access the user’s calendar to determine availability. If calendar indicates availability, a ‘tentative’ meeting will be entered. The smartphone user would have a list of tasks from the assistant, and one of the tasks is to ‘affirm’ availability of the meetings scheduled.
Allow recall of ‘generally available’ information. If a caller would like to know the address of the smartphone user’s office, the Digital Assistant may access a database of generally available information, and provide it. The Smartphone user may use applications like Google Keep, and any notes tagged with a label “Open Access” may be accessible to any caller.
Join the smartphone user’s social network, such as LinkedIn. If the caller knows the phone number of the person but is unable to find the user through the social network directory, an invite may be requested by the caller.
Custom business workflows may also be triggered by the smartphone, such as “Pay by Phone”.
The Digital Executive Assistant capabilities:
Able to gain control of your Smartphone’s incoming phone calls
Able to interact with the 3rd party, dial in caller, on a set of business dialog workflows defined by you, the executive.
Are you ready for a challenge, and 150,000 USD to begin to pursue your challenge?
That’s just SBIR Phase I, Concept Development (~6 months). The second phase, Prototype Development, may be funded up to 1 MM USD, and last 24 months.
The Small Business Innovation Research (SBIR) program is a highly competitive program that encourages domestic small businesses to engage in Federal Research/Research and Development (R/R&D) that has the potential for commercialization. Through a competitive awards-based program, SBIR enables small businesses to explore their technological potential and provides the incentive to profit from its commercialization. By including qualified small businesses in the nation’s R&D arena, high-tech innovation is stimulated and the United States gains entrepreneurial spirit as it meets its specific research and development needs.
The program’s goals are four-fold:
Stimulate technological innovation.
Meet Federal research and development needs.
Foster and encourage participation in innovation and entrepreneurship by socially and economically disadvantaged persons.
Increase private-sector commercialization of innovations derived from Federal research and development funding.