MIST 2090 Midterm 1

types of partnerships

1. Strategic alliances between non-competitors 2. "Cooptition"- strategic partnerships between competitors; engage in superior goal 3. Joint ventures to develop new businesses 4. Buyer-supplier relationships to assure reliable supplies Partnerships CAN exist between competitors

Definition of MIS

A management information system is an organized integration of hardware and software technologies, data, processes, and human elements designed to produce timely, integrated, relevant, accurate, and useful information for decision-making purposes

Role of humans in algorithmic development (proactive vs. reactive)

A proactive approach focuses on eliminating problems before they have a chance to appear and a reactive approach is based on responding to events after they have happened.

cloud

Accelerates the Cambrian Explosion> lowers barriers to entry, allows robot and drone designers to explore the trade off of local versus central computation, and every member of a robot tribe can quickly know what every member does

RDB use of social media and analytics

Asks for feedback and input from potential customers, instagram influencers, giveaways

RDB value proposition & customer segments

Cheaper clothes, personal touch > handwritten note, curated outfit; People who don't have a lot of time, stay at home moms, people in that price range

DANCE of the robots

Data, algorithms, networks, the cloud, and exponentially improving hardware.

business model

Describes the rationale of how an organization creates, delivers, and captures value

jobs least likely to be affected by machine progress

Environmental control is necessary when pieces of automation have primitive brains and no ability to sense their environments. As the machines are able to do more work in the physical world, we'll do less and less of it. -Let computers take the lead on making decisions, and then let people take the lead if others need to be convinced or persuaded to go along with these decisions -Ability to work effectively with people's emotional states and social drives will remain a deeply human skill

broken leg role

If a professor goes to the movies every Tuesday night, it is likely for a model to predict that she will go again next week. Then, she breaks her leg. Any human will instantly know that the professor will not be going to the movies. This is not duplicated by an algorithm; there are too many "distinct, unanticipated factors". Humans have a more comprehensive model of the world.

Distinction between "bits" and "molecules" (properties of information goods)

Information encoded in bits (digital) has free reproduction cost ("approaches zero") and perfect quality ("bit-for-bit copy"). Information encoded in molecules (analog) has higher reproduction costs and copies generally suffer from degradation. This distinction between bits and molecules is one of the three main distinctions between the 1st and 2nd phases of the Second Machine Age (the First Machine Age is the steam power/electric power shift). EX: decentralized processing, cloud computing, scale, value of data

Changes in how a company sells to customers (flower shop example)

Initially, flower shops had single, stand alone shops. Florist projected sales based on availability, and customers bought what was available in the store. Today, online florists operate in networks. There are more varieties of flowers available. Sophisticated ERP systems predict sales, manage inventory, etc. Business intelligence and data mining manage customer relationships. All of this requires information. It is often difficult to gauge demand. Today, there are new business models enabled by technology, data driven advertising, the importance of habits, etc.

Look out the window

It is a good idea for a person to check the computer's decisions to make sure they make sense. This can be very helpful for preventing errors as well as managing a company's reputation. EX: Many people tried to leave and Uber when an Iranian clerk took 18 ppl hostage in a cafe. Uber reacted to the spike in demand by initiating surge pricing, which they did not stop → heavy criticism

Counterparts

Machine= human mind, Platforms= products, Crowd= core → Companies will need to rethink the balance between all of these counterparts

"What happened last time?"

Many successful incumbent companies—in fact, most of them—did not survive the transition from one power source to the other. Businesses need to understand why this happened and to head some critical lessons from the past.

how the three lenses are changing business

New combinations of minds and machines are changing the ways businesses execute their most important processes. Pioneering companies are bringing together products and platforms to transform their offerings. The core and crowd are altering what organizations themselves look like and how they work.

algorithms

Ones currently dominating share the basic property that their results get better as the amount of data they're given increases. The performance of most algorithms usually levels off or asymptotes, but this is not the case for many present today

Machine Age & Phases

Phase 1: Mid 90s; technology cover routine work Phase 2: Around 2010; "science fiction technologies" began to appear in the world → Can perform non-routine tasks → Ubiquitous processing and connectivity → Information encoded as bits vs. molecules

types of work that robots succeed at

Robots are best suited for work that is dull, dirty,and dangerous, and dear (expensive). EX: visiting construction sites to check on progress. Agriculture could soon be transformed by drones. Insurance companies can use drones to assess how badly a roof was damaged after a tornado, and many other tasks

Rule-based systems vs. pattern recognition systems

Rule-based or symbolic artificial intelligence was established, while the other built statistical pattern recognition systems. At first, it looked like the symbolic approach was going to dominate. Symbolic AI systems generated deeply underwhelming results. There are a lot of rules in the world, and it's generally not enough to know and follow most of them, and there are rules within rules. The world is lousy at sticking to one set of rules.

system 1 vs system 2 thinking

System 1 is fast, automatic, evolutionarily ancient, and requires little effort; it is closely associated with intuition. System 2 is the opposite: slow, conscious, evolutionarily recent, and a lot of work. The operations of System 2 are often associated with the subjective experience of agency, choice, and concentration. System 2 is refined by taking math or science courses, while System 1 gets better more naturally and broadly by living life. Because System 1 operates automatically and cannot be turned off at will, errors of intuitive thought are often difficult to prevent. Biases cannot always be avoided, because System 2 may have no clue to the error.

failures of long term prediction and "how to be good"

System 1's many shortcuts and bugs keep us from making good predictions. More accurate predictors tend to take in information from many sources and show an ability to adopt multiple viewpoints when looking at a situation. Foxes (more successful predictors) versus hedgehogs present multidimensional, multi-perspective reasoning and analysis. Move towards short-term iteration, experimentation, and testing.

Analytics strategy at Target ("How Companies Learn Your Secrets")

Target was able to determine that a girl was pregnant before her father knew. When analyzing data, they noticed some new patterns. Women were buying large quantities of lotion around the beginning of their second trimester. Then, they could estimate the woman's due date so that Target could advertise certain coupons and tailor other products to them. The hope was that once they begin shopping at Target, they'll subsequently pick up other items, since they're already there.

networks

Technologies and protocols for communicating wirelessly over both short and long distances are improving rapidly; speed improvements mean better and faster data accumulation

Polanyi's Pervasive Paradox

The strange phenomenon that we know more than we can tell. We simply don't and we can't know what rules we ourselves are using to get something right.If no one on Earth knows the rules by which humans accomplish something, how can we ever create a rule-based system to emulate these accomplishments? Attempt to overcome the paradox by building systems through experience, repetition, and feedback.

the triple revolution

The three lenses to understand how technology is changing modern businesses

machines and creative production

Together, the elements of DANCE are causing the Cambrian explosion in robots, drones, autonomous cars and trucks, and many other machines that are deeply digital. Cheaper gear enables higher rates of innovation and experimentation, which generates a flood of data. This information is used to test and refine algorithms and to help systems learn. The algorithms are put into the cloud and distributed to machines via robust networks. The innovators do their next round of tests and experiments and the cycle continues.

exponential improvements

We'll continue to enjoy simultaneously lower prices and higher performance from our digital gear. Sustained steep price declines and performance improvement

re-engineering movement and business processes

With the computers handling the movement, people should be empowered to exercise their judgment. As process team workers they are both permitted and required to think, interact, use judgment, and make decisions. The ability to reason in a way that goes beyond executing role calculations on available data. Most of us believe that we're capable of delivering a great deal more than digital technologies can.

key partnerships

alliances between companies to optimize their business models, reduce risk, or acquire resources

There are two general ways for firms to raise capital

debt and equity

channels

describe how a company communicates with and reaches its customer segments to deliver a value proposition

key activities

describe the most important actions a company must take to operate successfully

key resources

describe the most important assets required to make the business model work → physical, financial, intellectual, or human → can be owned or leased by the company or acquired from key partners

customer relationships

describe the types of relationships that a company establishes with specific customer segments, ranging from personal to automated and should be tailored to each customer segment

cost structure

describes all costs incurred to operate a business model → some can be built entirely around low cost structures > can be non-profits (donations, partner organizations)

data

digital data, social media, and other sources combine to put us in an era of big data

Standard partnership

division of labor between minds and machines. Getting rid of human judgment altogether and relying solely on numbers plugged into formulas often yields better results; we need to rely less on expert judgments and predictions.

venture capitalists have a job that is...

high risk high reward

Status quo bias

it can be difficult for companies to imagine the future quickly enough to adapt; common in many industries; assume the future will be somewhat like the past

venture capitalists

opportunistic; they want to sell their share at a later date and make money → many top venture capital firms are located in Silicon Valley → "Sand Hill Road" → invest in a variety of startups at various stages of development EX: Andreessen Horowitz

revenue streams

represent the cash that a company generates from each customer segment

equity financing

where you trade partial ownership of some of your business to investors in exchange for their capital → you can get experience, wisdom, industry connections, etc. → giving away ownership → giving away decision making power

debt financing

you borrow money from a lender that you have to pay back → you have control over how the extra capital is spent → doesn't have a lasting impact on how your business is run

relationship between revenue and financing

→ Revenue Streams represent cash that a company generates from each customer segment → generally, revenue is income that comes from normal business activities only-- it doesn't include all the money Revenue from customers isn't the only way to bring operating cash to a firm

customer segments

→ a company may need to group customers into segments with common needs, behaviors, or other attributes Represent different segments if... → their needs require a distinct offer → different profitabilities → different types of relationships → reached through different distribution channels → willing to pay for different aspects

shark tank example

→ founders trying to raise capital by selling equity in their businesses → price of sold share = simple way to determine the value of the business EX: if I sell 25% of a business for $500,000 in equity, the business is valued at $2m

why we use the business model canvas

→ gives you a "shared language" to think about how to innovate in a rapidly changing socio-technical context → nine building blocks that show the logic of how the company will make money → derived from book Business Model Generation

value proposition

→ reason why customers turn to one company over another-- it solves a customer problem or satisfies a customer need → can be innovative, cutting-edge, new, or they can be similar to existing market offers, but with adding attributes (efficiency, cost, etc.)