Business Transformation Google Cloud
https://www.coursera.org/learn/business-transformation-google-cloud
Module 1: Why cloud technology is revolutionizing business
Module 2: Foster an innovation culture
Module 3: Define the ideal business transformation challenge
Module 4: Build trust with availability, security, and compliance
Module 5: Build a business case for your transformation challenge
M1:
Each age innovation age is a wave, now is data science and cloud tech. An improvement in supply increases demand. Cloud is software, computers, network and security. Use shared resources for cost and elasticity gains. Drive innovation in collaboration, perception, categorization, prediction, and recommendation. The danger of status quo is a risk, always be moving to the next platform. Be a why organization, not a how organization. Sometimes you have to move up the abstraction chain, ex. movie creation to movie distribution to movie recommendation (to personal move creation?!?!) Based on prevalence of IOT, we are generating more data and thus can predict complex behaviour and anticipate intent. Can also use cloud to improve operational metrics. First you need computation, TPU are computationally better that CPUs for training type of operations. Data is currency, provide inputs and outputs and cloud will generate the algo. Need large volumes of data for the machine to learn, humans are limited in their ability to see patterns because of cognitive overload and parameter complexity. We make emotional and unconscious decisions. Data needs to be secure too. Cloud is great for higher level business value. Cloud tech for any IT expert for any industry for any activity to get running in weeks. Would predicting the future of cost, revenue, risk and innovation help save money and time.
M2:
Apply garage-innovation mindset. A mission that matters, continuous learning, psychological safety. Focus on the user, think 10x, generate big ideas, launch/iterate. User expectations are access, engagement, customization, communication.
M3:
Improvements pave the way for transformation. Create challenge in form of question. Use question starters, use different personas, subtract a core component. To use data you must map your data ecosystem (end to end user process), including user data, corporate data, and industry data. Ex. Milk farm, look at cow's milk production which is a dependency on activity level, health, diet, weather. Create trackers to measure cow (corporate data) and weather (industry data). For transformational changes start with quick wins, developments, disruptions, transformations. As the hows: might we, could we, can we rethink, can we re-imagine, reinvent, then adjust the scope of the answers to slightly above your capabilities.
Personal note: The role of the domain expert is not stated. Only utilizing business data is stated. Domain experts can utilize operational data, and make more informed decisions for the value of data aggregates.
M4:
Privacy (data a user has access and can share with), security (policy and controls to keep data safe), compliance (meeting third-party standards), availability (reliability, access data when you want). Be aware about constant criminal attacks, physical damage, malware and viruses, unsecured third party-systems, lack of expert knowledge. Cloud has a shared security approach. Cloud is the data processor, managing the security of its infrastructure and data centres for defence in depth using custom hardware, software, storage, identity, network, and operations. Your responsibility is to secure access to your data. You need to be granular to control access to viewing logs and monitoring, modifying settings, modifying users, modifying applications, and a data breach plan. Compliance and data privacy also has a shared approach between cloud provider and the org. Google Cloud is certified at the highest global standards (ISO, GDPR, HIPAA). Ask: what are the cloud security capabilities, who owns the data, how is the cloud provider using your data, what happens if there is a data incident, what about data deletion, is your data portable, what type of data are you storing, where is the data stored, does the cloud provider allow third party security audits to check their security. Categorize each data bucket, then categorize sensitivity granularity.
M5:
How to decide on projects: feasibility (tech and non-tech), differentiation (competitive advantage), business impact (how beneficial is the solution to your business). Start with transformation and build a case for it using quick wins, disruptions, then developments. Identify datasets for your own goal, list 10 data sets (doesn't need to be realistic, ex. age, demo, color pref, style, habits, leisure activities, life evens, purchasing history, loyalty programs, feedback, interactions), the categorize (do you have, can you acquire). Add sensors to measure, ex clothes sensors and thermostat to understand temp when worn. Identified the business challenge, broken it down into smaller projects, plotted them along your transformation road map, built a competitive data strategy and the last stage is to build a business case for the project. Include descriptions of problem you are solving, who the customer of the solution, are they different from the end user, how is the challenge approached at the moment, what is the business process we are transforming, don't limit yourself to metrics also include targets. Then include success metrics, and constraints, like technical, legal, other. Think about target environment, is it actionable, what platform, in store, web, mobile app. Look for flaws and then be the advocate, use advocates. Create a pitch and project slide for key points about your solution. Socializing your business case to leadership: project name, slogan with concrete language, answer questions like how does the project meet org objectives, how does the project meet mission success criteria, how does the project create new value for business.
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