Understanding Digital Transformation
There’s a lot of terminology thrown around when it comes to digital transformation, things like mobile technology, cloud, A.I., block chain, cultural change, and even things like new ways of working. It can all get a bit confusing. So before we get into understanding what digital transformation is all about, let’s start in this first lecture with really understanding what transformation is. I’m not sure about you, but when I speak to people and ask them, what do you think transformations?
Digital Transformation Framework
So in this part of the course, we get into the digital transformation framework, which forms the backbone of digital transformation.
Digital Transformation – Technology IR 4.0
Let’s be honest, technology is fun. But it’s important to remember that digital transformation isn’t digital technology transformation. Instead, use cases for digital technology and data need to be first identified before investment is made in digital. Here will see different pillars which related to IR 4.0
Enlightenment : Big Data, Machine Learning & AI
Data science, machine learning and AI have affinities and support each other in analytics applications and other use cases, their concepts, goals and methods differ in significant ways. It focuses on extracting information needles from data haystacks to aid in decision-making and planning
Enlightenment : RPA (Robotic Process Automation)
Based on current projections, AI is expected to have the ability to increase labour productivity by up to 40% by 2035. Although some may see robotics and AI as tools to replace human workers, the International Federation of Robotics believes that less than 10% of jobs could be fully automated; robots are generally designed to take on repetitive tasks and allow workers to focus on more intensive duties.
Betta Health – Disruption and Value Opportunities
Betta Health is a leading healthcare organization, with doctor’s offices and well-being centres located across 20 states in the USA. They target an increasingly ageing baby boomer and Generation Y market. Both markets are very health conscious.
Baby boomers are focused on dealing with age related conditions – like arthritis, heart disease, dementia, diabetes and obesity. Generation X patients tend to more focused on work and lifestyle related conditions – so stress, mental health, obesity and diet.
Both patient segments are finding it increasingly difficult to make it into Betta Health’s offices to manage their health. Baby Boomers are increasingly facing mobility issues and Generation X patients are increasingly finding it hard to make the time commitment to attend medical appointments. This is because of work pressures and many holding more senior roles which do not permit much free time.
Betta Health is nervous about disruption happening at the moment in their industry and within their targeted segments.
You have been hired by Betta Health as an expert in Digital Transformation – and they want you to provide answers to key concerns they have.
- Define 3 key areas that you think would cause disruption for Betta Health. Provide details for each disruptor and the impact it could have on Betta Health’s current business models.
- Identify, based on Betta Health’s current business models, ideas for Betta Health to mitigate this disruption by creating value for its clients/patients.
Robot 1: Clothing consultant
1. Ask City Name (Get Input from User) :: << Input Dialog >>
1.1 Save the City Name :: << Create a String Variable and Store Data >>
2. Google City Name Temperature in Fahrenheit
2.1. Open Browser :: << Open Browser >>
2.2. Put City (which is stored in Previous step) in search panel :: << Type into >>
2.3. Press Enter << Send Hotkey >>
3. Scrape Data
3.1. Scrape Temperature & Condition from Google Result :: << Get Full Text >>
3.2. Store data (Temperature & Condition) << variable >>
3.3. Close the Browser << Close Tab >>
(Optional)3.4. Debug whether our Variables are storing Correct Data. << Write Line >>
4. Make Suggestion about my Clothing ( Condition :: True / False)Temperature
Decision 1: if Temp<30F then “You can put Some Jackets on”
Decision 2: if Temp>60F then “Put your Tees or Shorts”
Decision 3: if Temp>30 or Temp<60 then “Put Summer Jackets on”
:: << Flow Chart >>:: << Flow Decision>>:: << Assign >>>
5. Show output :: << Message Box >>
Predicting Used Car Prices
The prices of new cars in the industry is fixed by the manufacturer with some additional costs incurred by the Government in the form of taxes. So, customers buying a new car can be assured of the money they invest to be worthy. But due to the increased price of new cars and the incapability of customers to buy new cars due to the lack of funds, used cars sales are on a global increase (Pal, Arora and Palakurthy, 2018). There is a need for a used car price prediction system to effectively determine the worthiness of the car using a variety of features. Even though there are websites that offers this service, their prediction method may not be the best. Besides, different models and systems may contribute on predicting power for a used car’s actual market value. It is important to know their actual market value while both buying and selling.
To be able to predict used cars market value can help both buyers and sellers.
Used car sellers (dealers): They are one of the biggest target group that can be interested in results of this study. If used car sellers better understand what makes a car desirable, what the important features are for a used car, then they may consider this knowledge and offer a better service.
Online pricing services: There are websites that offers an estimate value of a car. They may have a good prediction model. However, having a second model may help them to give a better prediction to their users. Therefore, the model developed in this study may help online web services that tells a used car’s market value.
Individuals: There are lots of individuals who are interested in the used car market at some points in their life because they wanted to sell their car or buy a used car. In this process, it’s a big corner to pay too much or sell less then it’s market value.
The data used in this project was downloaded from Kaggle. It was uploaded on Kaggle by Austin Reese who Kaggle.com user. Austin Reese scraped this data from craigslist with non-profit purpose. It contains most all relevant information that Craigslist provides on car sales including columns like price, condition, manufacturer, latitude/longitude, and 22 other categories.
Dataset Collected from here : https://www.kaggle.com/austinreese/craigslist-carstrucks-data
There are two ways we can do this; either we can solve this with Azure ML Designer (No Code) way or We can do this using python notebook.
- Let’s do this using Azure ML Designer (Azure ML Studio -Classic)
- If you’re Python savvy you can follow [this] link for get your ipynb files.